stock price informativeness, corporate expenditure … · stock price informativeness, corporate...

STOCK PRICE INFORMATIVENESS,

CORPORATE EXPENDITURE AND

INFORMATION ASYMMETRY

Lee Mei Yee

Doctor of Philosophy

December 2013

STOCK PRICE INFORMATIVENESS,

CORPORATE EXPENDITURE AND

INFORMATION ASYMMETRY

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Lee Mei Yee

Bachelor of Accounting (First Class Honours)

University Malaya, Malaysia

Chartered Accountant (CA)

Malaysian Institute of Accountants (MIA)

Certified Public Accountant (CPA)

The Malaysian Institute of Certified Public Accountants (MICPA)

Postgraduate Diploma in Business and Commerce

Monash University Malaysia

Department of Accounting and Finance

School of Business

Monash University Malaysia

December 2013

ADDENDUM

p 3/lines 10 and 11: Delete the words “In this thesis,” and insert the following:

“Based on prior literature, this thesis examines whether stock price informativeness

influences managers in making their corporate expenditure decisions.” Replace “it…”

with “It…”

p 54: After para 2, add the following new paragraph:

“Based on the literature review, prior empirical research has provided evidence on the

determinants of stock price informativeness. However, studies examining the

consequences of stock price informativeness are relatively sparse. This thesis aims to

provide insights on how stock price informativeness influences managers‟ decisions on

corporate expenditure. This study is useful in providing direct evidence of how managers

adjust firms‟ corporate expenditure when stock price informativeness changes. The

findings from this research have fundamental policy implications because firms are more

likely to improve their stock price informativeness through appropriate financial

reporting.”

p 207/ para 2/ line 5: Delete the word “is” appearing after the word “flows”

pp 219 – 222/ Table 5.17 stretching over four pages:

The symbol “?” appearing in the column entitled “Expected Direction” for the variable ψ

should appear as “-ve”







Copyright Notices Notice 1 Under the Copyright Act 1968, this thesis must be used only under the normal conditions of scholarly fair dealing. In particular no results or conclusions should be extracted from it, nor should it be copied or closely paraphrased in whole or in part without the written consent of the author. Proper written acknowledgement should be made for any assistance obtained from this thesis. Notice 2 I certify that I have made all reasonable efforts to secure copyright permissions for third-party content included in this thesis and have not knowingly added copyright content to my work without the owner's permission.

ii

TABLE OF CONTENTS

TABLE OF CONTENTS ................................................................................................ ii

ABSTRACT ................................................................................................................ viii

ORIGINALITY STATEMENT ...................................................................................... x

ACKNOWLEDGEMENTS ........................................................................................... xi

CONFERENCE PAPERS PRODUCED FROM THESIS .......................................... xiv

LIST OF TABLES ........................................................................................................ xv

LIST OF FIGURES .................................................................................................. xviii

LIST OF ABBREVIATIONS ...................................................................................... xix

CHAPTER 1 INTRODUCTION ............................................................................... 1

1.1 Background of the Research ................................................................................. 1

1.2 Research Problem.................................................................................................. 6

1.3 Research Objectives .............................................................................................. 8

1.4 Research Questions ............................................................................................... 9

1.5 Overview of Research Methods and Findings ...................................................... 9

1.6 Significance of the Study .................................................................................... 14

1.7 Structure of the Thesis ........................................................................................ 16

1.8 Chapter Summary................................................................................................ 20

iii

CHAPTER 2 LITERATURE REVIEW ................................................................. 21

2.1 Introduction ......................................................................................................... 21

2.2 Stock Price Informativeness ................................................................................ 22

2.2.1 Idiosyncratic Volatility ................................................................................ 22

2.2.2 Measurement of Idiosyncratic Volatility ..................................................... 27

2.2.3 Significance of Stock Price Informativeness ............................................... 30

2.2.4 Empirical Research on Stock Price Informativeness ................................... 36

2.2.4.1 Empirical Research – Cross-Country Analysis ........................................ 36

2.2.4.2 Empirical Research – Firm-Level Studies ............................................... 44

2.2.5 Inconsistent Findings ................................................................................... 55

2.3 Corporate Expenditure ........................................................................................ 56

2.3.1 Research and Development Expenditure ..................................................... 57

2.3.1.1 R&D Expenditure and Return ................................................................ 58

2.3.1.2 R&D Expenditure and Risk .................................................................... 62

2.3.1.3 Determinants of R&D Expenditure ........................................................ 64

2.3.2 Capital Expenditure ..................................................................................... 74

2.3.2.1 Significance of CAPEX .......................................................................... 74

2.3.2.2 Determinants of CAPEX ........................................................................ 78

2.3.3 Selling, General and Administrative Costs .................................................. 81

iv

2.3.3.1 Characteristics of SGA Costs ................................................................... 82

2.3.3.2 Value Relevance of SGA Costs ............................................................... 91

2.3.3.3 Determinants of SGA Costs ..................................................................... 93

2.4 Chapter Summary................................................................................................ 95

CHAPTER 3 THEORETICAL FRAMEWORK AND HYPOTHESES

DEVELOPMENT ....................................................................................................... 96

3.1 Introduction ......................................................................................................... 96

3.2 Stock Price Informativeness and Corporate Expenditure ................................... 97

3.3 The Role of Information Asymmetry ................................................................ 106

3.3.1 Firm Size .................................................................................................... 112

3.3.2 Analyst Following...................................................................................... 115

3.3.3 Bid-ask Spreads ......................................................................................... 119

3.4 Research Model ................................................................................................. 124

3.5 Chapter Summary.............................................................................................. 127

CHAPTER 4 RESEARCH METHODOLOGY ................................................... 128

4.1 Introduction ....................................................................................................... 128

4.2 Research Paradigm ............................................................................................ 129

4.3 Population Data ................................................................................................. 130

v

4.4 Sources of Secondary Data ............................................................................... 131

4.5 Variables Measurement ..................................................................................... 131

4.5.1 Dependent Variable ................................................................................... 131

4.5.2 Independent Variable ................................................................................. 133

4.5.3 Proxies of Information Asymmetry ........................................................... 136

4.5.4 Control Variables ....................................................................................... 138

4.5.4.1 Firm Characteristics Control Variables .................................................. 139

4.5.4.2 Corporate Governance Control Variables .............................................. 149

4.6 Model Specification .......................................................................................... 152

4.6.1 Main Model................................................................................................ 153

4.6.2 Robustness Tests ........................................................................................ 155

4.6.2.1 Change Model ........................................................................................ 156

4.6.2.2 Two-Stage Least Squares Regression .................................................... 157

4.7 Sample Selection Procedure .............................................................................. 167

4.8 Statistical Analyses ........................................................................................... 169

4.8.1 Data Screening ........................................................................................... 169

4.8.2 Data Analyses ............................................................................................ 172

4.8.2.1 Univariate Tests ..................................................................................... 172

4.8.2.2 Multivariate Tests .................................................................................. 172

4.8.2.3 Additional Tests ..................................................................................... 178

vi

4.9 Chapter Summary.............................................................................................. 179

CHAPTER 5 RESEARCH FINDINGS AND DISCUSSION ............................. 180

5.1 Introduction ....................................................................................................... 180

5.2 Univariate Results ............................................................................................. 181

5.2.1 Descriptive Statistics of Corporate Expenditure ........................................ 181

5.2.2 Pearson Correlations .................................................................................. 193

5.2.3 Descriptive Statistics by Firm Size ............................................................ 203

5.2.4 Descriptive Statistics by Analyst Following .............................................. 208

5.2.5 Descriptive Statistics by Bid-ask Spreads ................................................. 212

5.3 Hypothesis 1 - Stock Price Informativeness and Corporate Expenditure ......... 216

5.3.1 Multivariate Tests ...................................................................................... 217

5.3.1.1 Direction of Idiosyncratic Volatility ...................................................... 239

5.3.1.2 Summary of Findings ........................................................................... 250

5.3.2 Robustness Tests ........................................................................................ 251

5.3.2.1 Change Model ........................................................................................ 251

5.3.2.2 Two-Stage Least Squares Regression .................................................... 276

5.3.3 Summary of Findings – Hypothesis 1........................................................ 284

5.4 Hypotheses 2a to 2c – The Role of Information Asymmetry ........................... 288

5.4.1 Firm Size .................................................................................................... 288

vii

5.4.2 Analyst Following...................................................................................... 303

5.4.3 Bid-ask Spreads ......................................................................................... 318

5.4.4 Summary of Findings – Hypotheses 2a to 2c ............................................ 331

5.5 Additional Tests ............................................................................................... 332

5.5.1 Different Measures of Idiosyncratic Volatility .......................................... 333

5.5.2 Controlling for the Effect of Global Financial Crisis ................................ 334

5.5.3 Controlling for Time-Series and Cross-Sectional Correlations ................. 335

5.6 Chapter Summary.............................................................................................. 335

CHAPTER 6 CONCLUSION ................................................................................ 337

6.1 Introduction ....................................................................................................... 337

6.2 Summary of Key Research Findings ................................................................ 338

6.2.1 Stock Price Informativeness and Corporate Expenditure .......................... 340

6.2.2 The Role of Information Asymmetry ........................................................ 343

6.3 Contributions of the Study ................................................................................ 348

6.4 Limitations of the Study .................................................................................... 351

6.5 Recommendations for Future Research ............................................................ 354

6.6 Concluding Remarks ......................................................................................... 355

REFERENCES ............................................................................................................ 357

viii

ABSTRACT

This study examines how firm-level corporate expenditure represented by R&D, capital

expenditure (CAPEX) and selling, general and administrative (SGA) costs responds to

stock price informativeness. Using data from the United States public listed companies

for the years 2003 to 2009 covering the post-Sarbanes Oxley Act period, a current year‟s

stock price informativeness, proxied by idiosyncratic volatility, is found to be negatively

associated with the subsequent year‟s R&D expenditure and SGA costs. However, it

was observed there is no relationship between a current year‟s idiosyncratic volatility

and CAPEX level of the subsequent year.

Additional insights are revealed by using a change model which examines the

relationship between changes in a current year‟s stock price informativeness and

changes in the subsequent year‟s R&D and SGA expenditure. The results exhibit that

when firm-level stock price informativeness is strengthening, a change in the current

year‟s idiosyncratic volatility is positively related to changes in R&D and SGA

expenditure in the following year. When stock price informativeness is deteriorating,

firm managers do not react immediately to modify R&D and SGA costs. This

asymmetric cost response is attributable, in part, to the cost “stickiness” behaviour of

firm managers when it comes to changing R&D investment and SGA costs. These

managers may be reluctant to increase corporate expenditure when stock price

informativeness worsens as they need to assess whether the declining idiosyncratic

ix

volatility is temporary or permanent in nature. It is found that firm managers will only

react by intensifying R&D expenditure and SGA costs when it is critically compelling,

that is, when the relative idiosyncratic volatility (1-R2) drops significantly by 20 per cent.

This finding is consistent with the learning theory that managers learn about firms‟

fundamental values from the market‟s feedback and they incorporate this new private

information to make efficient corporate decisions in R&D expenditure and SGA costs.

However, this study finds that firm managers do not rely on the input from the capital

markets to make their capital investment decisions.

Given the significance of stock price informativeness in enhancing allocation of firms‟

scarce resources, this study provides useful insights and understanding on how firms

modify their corporate expenditure in response to changes in stock price informativeness.

Further analyses show that the relationship between stock price informativeness and

corporate expenditure is dependent on information asymmetry, proxied by firm size,

analyst following and bid-ask spreads. The analyses highlight that managers respond

and learn more quickly from the new firm-specific information available in small firms

and in firms with low analyst following as well as in firms with high bid-ask spread.

These findings are consistent with the learning theory and information asymmetry

theory. Consequently, firm managers react more “aggressively” by altering R&D

expenditure and SGA costs in the subsequent year as stock price informativeness of a

current year changes.

x

ORIGINALITY STATEMENT

“I hereby declare that this thesis contains no material which has been accepted for the

award of any other degree or diploma in any university or other institution and I hereby

affirm that to the best of my knowledge, the thesis contains no material previously

published or written by another person, except where due reference is made in the text

of this thesis.”

Signed: Mei Yee LEE

Dated: December 27, 2013

xi

ACKNOWLEDGEMENTS

I would like to acknowledge and extend my heartfelt gratitude to the following

individuals for their wisdom, encouragement, inspiration, understanding and guidance in

completing this thesis. Without them, this research would not have been possible.

First and foremost, I would like to express my greatest appreciation and gratitude to my

principle supervisor, Professor Ferdinand A. Gul for his mentoring and direction in

completing this thesis. Without his scholarly guidance, expert knowledge in the field of

accounting and finance and invaluable insights, this research would not have been

embarked. His intellectual advice and encouragement inspires me perpetually and it is

my privilege and honour to be his first PhD student in Monash Malaysia.

I am grateful to my associate supervisor, Professor Jeyapalan Kasipillai for his relentless

effort and motivation as well as invaluable advice particularly during the writing stage

of this thesis. His professional guidance, continuous assessments and generous support

have contributed greatly to the successful completion of this thesis. I am also indebted to

him for supervising me during the pursuance of my Postgraduate Diploma in Business

and Commerce degree in this University.

xii

I am indeed thankful to Professor Susela Devi Selvaraj for her inspiration, guidance and

continuous encouragement in the undertaking of this academic endeavour. I would wish

to thank my research panel members, namely, Associate Professor Gareth Leeves,

Professor Mahendhiran Nair and Dr Teh Chee Ghee for their insightful comments and

suggestions during the progressive reviews of this thesis. Not forgotten, my sincere

appreciation goes to expert advice rendered by Professor Bin Srinidhi, Angel Sung, Dr

Anthony Ng, Professor Min Chung-Ki, Associate Professor Santha Vaithilingam, Dr

Foo Yee Boon and Dr Karen Lai. I duly acknowledge the invaluable feedback received

from the chairs, discussants and participants during my presentations at the Jornal of

Contemporary Accounting and Economics (JCAE) Doctoral Consortium 2013 held in

Hong Kong and the Accounting and Finance Association of Australia and New Zealand

(AFAANZ) 2013 Conference in Perth.

I would like to extend my sincere gratitude to Monash Malaysia for the granting of

financial support for my postgraduate studies. I am thankful to two academics, namely

Norita Nasir and Shyamala Dhoraisingam for their guidance and encouragement during

my teaching tenure. I am also grateful to research and administrative staff for their kind

assistance and co-operation during my postgraduate journey, especially to Stephanie

Phang, Loke Bee Khum, Veenee Ooi and Parameswari Sithamparam. My sincere thanks

go to my doctorate colleagues for their encouragement during these challenging three

years, specifically to Pak Mei Sen, Hasuli Perera, Sanjuktha Choudhury and Mary Gu.

xiii

I am grateful to my parents for their understanding and support given to me to embark

and complete this doctoral journey. Most importantly, I am eternally grateful for the

patience and continued support given to me by my beloved husband, Hooi Yew Chong

and my two loving sons, Kai Cheng and Kai Xin throughout the duration undertaken to

complete my postgraduate degrees.

I thank you all for being with me in realising my dream. To all of you, I dedicate this

thesis.

xiv

CONFERENCE PAPERS PRODUCED FROM THESIS

1. Lee, M. Y., Gul, F. A. & Kasipillai, J. (2013). Stock price informativeness and its

impact on corporate strategies. Paper presented at the Accounting and Finance

Association of Australia and New Zealand (AFAANZ) Conference on 7-9 July,

2013 held at Perth, Australia.

2. Lee, M. Y. (2013). The impact of stock price informativeness on corporate

strategies. Paper presented at the Journal of Contemporary Accounting and

Economics (JCAE) Doctoral Consortium on 3 January, 2013 held at Hong Kong.


strategies, corporate governance and firm performance. Paper presented at the 5th

Annual Doctoral Colloquium, Monash University Malaysia on 26-28 September,

2012 held at Pulau Tioman, Malaysia.


strategies, corporate governance and firm performance. Paper presented at the 4th

Annual Doctoral Colloquium, Monash University Malaysia on 1-3 December,

2011 held at Malacca, Malaysia.

I am honoured to be invited as a discussant at the Concurrent Paper Session of the

forthcoming JCAE Symposium held on January 3-4, 2014 in Kuala Lumpur, Malaysia.

The title of the paper is “Impact of International Financial Reporting Standards on Stock

Price Synchronicity in Asian Countries”.

xv

LIST OF TABLES

Table Contents Page

2.1 Value of Relative Idiosyncratic Volatility (1-R2) 26

4.1 Definition of Variables 162

4.2 Sample Selection Procedure 167

5.1 US Corporate Expenditure of Years 2004-2010 (in USD million) 182

5.2 Descriptive Statistics – R&D Expenditure 186

5.3 Descriptive Statistics – Capital Expenditure 188

5.4 Descriptive Statistics – Selling, General and Administrative Costs 190

5.5 Pearson Correlations – R&D Expenditure 194

5.6 Pearson Correlations – Capital Expenditure 198

5.7 Pearson Correlations – Selling, General and Administrative Costs 201

5.8 Descriptive Statistics – R&D Expenditure by Firm Size

204

5.9 Descriptive Statistics – Capital Expenditure by Firm Size 205

5.10 Descriptive Statistics – Selling, General and Administrative Costs

by Firm Size

206

5.11 Descriptive Statistics – R&D Expenditure by Analyst Following

209

5.12 Descriptive Statistics – Capital Expenditure by Analyst Following 210


by Analyst Following

211

5.14 Descriptive Statistics – R&D Expenditure by Bid-ask Spreads

213

5.15 Descriptive Statistics – Capital Expenditure by Bid-ask Spreads 214

xvi

LIST OF TABLES (Continued)

Table Contents Page


by Bid-ask Spreads

215

5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1) 219

5.18 Effect of Stock Price Informativeness on CAPEX (H1) 226

5.19 Effect of Stock Price Informativeness on SGA Costs (H1) 233

5.20 Effect of Stock Price Informativeness on R&D Expenditure (H1) –

by Direction of Idiosyncratic Volatility‟s Movement

240

5.21 Effect of Stock Price Informativeness on CAPEX (H1) – by

Direction of Idiosyncratic Volatility‟s Movement

244

5.22 Effect of Stock Price Informativeness on SGA Costs (H1) – by

Direction of Idiosyncratic Volatility‟s Movement

247

5.23 Changes in R&D Expenditure Following Changes in Stock Price

Informativeness

253

5.24 Changes in CAPEX Following Changes in Stock Price

Informativeness

263

5.25 Changes in SGA Costs Following Changes in Stock Price

Informativeness

268

5.26 Results of Two-Stage Least Squares Regressions 279

xvii

LIST OF TABLES (Continued)

Table Contents Page

5.27 Effect of Stock Price Informativeness on R&D Expenditure – by

Firm Size

289

5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size

(H2a)

295

5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size

(H2a)

298


Analyst Following (H2b)

304

5.31 Effect of Stock Price Informativeness on CAPEX – by Analyst

Following (H2b)

310

5.32 Effect of Stock Price Informativeness on SGA Costs – by Analyst

Following (H2b)

313


Bid-ask Spreads (H2c)

319

5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-ask

Spreads (H2c)

324

5.35 Effect of Stock Price Informativeness on SGA Costs – by Bid-ask

Spreads (H2c)

327

6.1 Summary of Research Objectives, Corresponding Hypotheses and

Research Findings

339

xviii

LIST OF FIGURES

Figure Contents Page

1.1 Structure of the Thesis 17

2.1 Stock Return Synchronicity in Various Countries in 1995 37

2.2 Asymmetric Behaviour of “Sticky” Costs 86

3.1 Research Model 124

5.1 Trend Analysis of US Corporate Expenditure from 2004-2010 184

5.2 Association between Current Year‟s Idiosyncratic Volatility and

R&D Expenditure of the Subsequent Year

242

5.3 Association between Current Year‟s Idiosyncratic Volatility and

SGA Costs of the Subsequent Year

249

5.4 Association between Changes in Current Year‟s Idiosyncratic

Volatility and Changes in R&D Expenditure of Subsequent Year

when Idiosyncratic Volatility Increases

259



when 1-R2 Drops 20%

260


Volatility and Changes in SGA Costs of Subsequent Year when

Idiosyncratic Volatility Increases

273


Volatility and Changes in SGA Costs of Subsequent Year when 1-

R2 Drops 20%

274

xix

LIST OF ABBREVIATIONS

CAPEX Capital Expenditure

CAPM Capital Asset Pricing Model

CEO Chief Executive Officer

CMG Capital Market Governance

CRSP Centre for Research in Security Prices

GAAP Generally Accepted Accounting Principles

GDP Gross Domestic Product

IAS International Accounting Standards

I/B/E/S Institutional Brokers‟ Estimate System

IFRS International Financial Reporting Standards

NYSE New York Stock Exchange

OLS Ordinary Least Squares

PIN Probability of Informed Trading

PLCs Public Listed Companies

R&D Research and Development

ROA Return on Assets

ROE Return on Equity

ROI Return on Investments

SGA Selling, General and Administrative

SIC Standard Industrial Classification

xx

LIST OF ABBREVIATIONS (Continued)

UK United Kingdom

US United States

VIF Variance Inflation Factor

2SLS Two-Stage Least Squares

1

CHAPTER 1

INTRODUCTION

1.1 Background of the Research

A recent strand of research has examined issues related to stock price informativeness.

These studies are motivated by the belief that high stock price informativeness is

associated with more efficient allocation of capital (Durnev, Morck & Yeung, 2004;

Chen, Goldstein & Jiang, 2007; Gul, Srinidhi & Ng, 2011b; Xu, Chan, Jiang & Yi,

2013). When firm-specific information is conveyed to the capital markets in an accurate

and timely manner, firms‟ stock prices are tracking closer to their fundamental values,

thereby reflecting a more efficient market. This phenomenon enables the appropriate

pricing of capital according to its different uses and provides meaningful feedback to

firm managers when stock prices move in response to their investment decisions

(Durnev, Morck, Yeung & Zarowin, 2003). Consequently, scarce resources can be

channelled to its highest value use or are withdrawn from sectors with poor financial

performance (Tobin, 1984). Furthermore, higher stock price informativeness are

associated with better management decisions (Durnev et al., 2004; Chen et al., 2007;

Frésard, 2012) and provides more information about a firm‟s future earnings (Durnev et

al., 2003; Jiang, Xu & Tong, 2009).

2

Technically, all private information is integrated into stock prices by the end of the

trading day (Kyle, 1985). Stock price informativeness is measured by idiosyncratic

volatility1 (Morck, Yeung & Yu, 2000; Durnev et al., 2004; Jin & Myers, 2006; Ferreira

& Laux, 2007). Idiosyncratic volatility measures the capitalization rate of firm-specific

information into stock prices through informed trading. Idiosyncratic volatility is higher

when a stock return is less correlated with market and industry returns, that is, when it

has lesser co-movement with the market (French & Roll, 1986; Roll, 1988). Thus,

idiosyncratic volatility is equal to the logistic transformation of a firm‟s 1-R2 value

(Ferreira & Laux, 2007) and is the inverse of stock price synchronicity proxied by R2

value (Morck et al., 2000; Jin & Myers, 2006).

Drawing from the learning hypotheses2 (or learning theory) of Dow and Gorton (1997)

and Subrahmanyam and Titman (1999), managers learn about the fundamental value of

firms from their own stock prices and incorporate this new private information in

allocating corporate resources (Luo, 2005; Chen et al., 2007; Frésard, 2012).

Information flows from firms to capital markets and also from the capital markets to

firms through stock prices (Dow & Gorton, 1997; Dye & Sridhar, 2002). Only new

1Idiosyncratic volatility is also referred to as firm-specific stock return variation or price non-

synchronicity (Durnev et al., 2004; Frésard, 2012). It is used interchangeably with the term “stock price

informativeness” in this thesis.

2 Some authors, for example Foucault and Frésard (2012) and Fresard (2012) name it as „managerial

learning channel‟. In this thesis, the term “learning theory” is used interchangeably with learning

hypothesis.

3

firm-specific information is able to influence managerial decisions whilst information

that managers are already aware of would not have any impact on their corporate

expenditure decisions (Chen et al., 2007). The new information that firm managers may

not know includes growth opportunities, future demand for a firm‟s products, market

competition, relationships with diverse stakeholders and financing opportunities (Dow

& Gorton, 1997; Subrahmanyam & Titman, 1999). As such, informed stock prices

convey information of market‟s assessment on firms‟ potentials and provide meaningful

signals to firm managers about the quality of their decisions. Managers can then use this

firm-specific information that they have yet to possess to improve the efficiency of their

corporate decisions, thus enhancing firm value (Chen et al., 2007; Frésard, 2012). In this

thesis, it is argued that stock price informativeness motivates firm managers to initiate

changes to their corporate expenditure in three specific areas, namely, research and

development (R&D) expenditure, capital expenditure (CAPEX) and selling, general and

administrative (SGA) costs. This thesis examines the association between a current

year‟s stock price informativeness and the subsequent year‟s corporate expenditure.

Firms‟ “information environment” has a role in deciding the extent of the agency

conflict between managers and investors. At various times, managers possess superior

firm-specific information, compared to investors (Armstrong, Guay & Weber, 2010),

and this results in problems of adverse selection (hidden information) as well as moral

hazard (hidden action) (Arrow, 1985). The former causes a failure to identify the true

value of firms (Akerlof, 1970; Healy & Palepu, 2001) while the latter results in earnings

4

management (Richardson, 2000) and non-disclosure of information (Verrecchia, 2001)

to meet the personal interests of managers. Information asymmetry also occurs among

investors and creates adverse selection problem when informed investors3 trade on firm-

specific information while uninformed investors depend more on publicly available

information (Brown & Hillegeist, 2007). Uninformed traders demand a discount in

buying firm shares, especially in illiquid markets, to price-protect themselves against

potential losses from trading with informed traders (Myers & Majluf, 1984; Merton,

1987). This results in a lower amount of share issuance proceeds and a higher cost of

capital (Bhattacharya & Spiegel, 1991; Leuz & Verrecchia, 2000). The unequal

dissemination of information among different parties affects the efficiency and

transparency of the capital markets (Armstrong et al., 2010). It is therefore important to

reduce information asymmetry, as superior information about firms helps to boost

market liquidity (Bushman & Smith, 2001) and improve the allocation of scarce

resources, thereby resulting in long-term economic growth (Merton, 1987).

Prior research shows that information moves faster not only in large firms (Atiase, 1985;

Bhushan, 1989a), but also in firms with high analyst coverage (Hong, Lim & Stein,

3 There are two types of investors: informed and uninformed (Grossman, 1976). According to Grossman

(1976), informed traders invest their time and resources to learn about a firm and thereby making their

investment decisions. Examples of informed market participants are institutional investors, insiders and

financial analysts (Piotroski & Roulstone, 2004). Uninformed traders, on the other hand, do not engage in

information collection activities but instead learn by observing the movement of the stock prices to make

their judgement about the true future price of a firm.

5

2000; Frankel & Li, 2004) and in firms with low bid-ask spreads (Welker, 1995;

Richardson, 2000). As such, these firms are expected to be associated with low

information asymmetry. Drawing ideas from the learning theory, firm managers learn

new private information that they have yet to possess from stock prices, in order to make

appropriate corporate decisions (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999).

While large firms generate huge amounts of public information through public

announcements and financial disclosures, this information has already been utilised by

their managers in past investment decisions. As such, the information gathered does not

have any effect on firms‟ corporate expenditure decisions. Managerial learning is also

expected to be lower for firms with higher analyst following since most of the

information produced by analysts is derived from firm managers (Agrawal, Chadha &

Chen, 2006) or has already been factored into managers‟ past investment decisions

(Chen et al., 2007) and hence, is unlikely to affect managerial decisions. Analysts do not

seem to produce new private information as most of the information they generate

reveals greater industry and market-level information (Piotroski & Roulstone, 2004);

they instead bring in more uninformed or noise trading to the stocks (Easley, O'Hara &

Paperman, 1998). This reduces the private information content in stock prices (Chen et

al., 2007) and discourages both managerial learning and prompt firms‟ reaction in

making corporate decisions.

On the other hand, empirical findings show that more private information is produced

for small firms (Chen et al., 2007; Bakke & Whited, 2010), firms with low analyst

6

following (Chen et al., 2007) and firms with high bid-ask spreads (Chan, Hameed &

Kang, 2013). Managers of firms with a greater extent of new firm-specific private

information are induced to learn more quickly and react more “aggressively” by altering

corporate expenditure when stock price informativeness changes. This thesis

investigates whether the relationship between a current year‟s stock price

informativeness and the subsequent year‟s corporate expenditure is dependent on

information asymmetry. Three proxies of information asymmetry, namely, firm size,

analyst following and bid-ask spreads, are employed in this study.

This introductory chapter provides background information of the thesis. The remaining

sections are organized as follows. Section 1.2 deliberates the research problem

statements. This is followed by the identification of research objectives in Section 1.3

and the elaboration of the research questions in Section 1.4. Section 1.5 presents an

overview of the research methods applied and briefly outlines the study‟s findings.

Section 1.6 delineates the significance of the study, while Section 1.7 presents the

structure of this thesis. A summary of this chapter is provided in Section 1.8.

1.2 Research Problem

Prior empirical research has established a connection between stock price

informativeness and corporate investment decisions (Durnev et al., 2004; Chen et al.,

2007). These studies, however, do not provide any direct guidance as to the relationship

7

between the levels of stock price informativeness and corporate expenditure. The links

of stock price informativeness to corporate expenditures such as R&D expenditure and

SGA cost also remains an unexplored area of study. This study bridges the gap by

investigating whether stock price informativeness motivates firm managers to change

corporate expenditure as proxied by R&D expenditure, CAPEX and SGA costs.

In view of the significance of stock price informativeness and its possible impact on

corporate expenditure decisions through the learning theory, it is pertinent to address the

relevant issues in examining the relationship between stock price informativeness and

corporate expenditure. For example, it would be useful to ascertain how this observed

relationship differs when idiosyncratic volatility strengthens or weakens. There is also

some concern as to how much managers would adjust corporate expenditure decisions

when stock price informativeness changes. This is because, there may not be

proportionate changes in the subsequent year‟s corporate expenditure arising from

changes in a current year‟s idiosyncratic volatility. As suggested by prior studies

(Anderson, Banker & Janakiraman, 2003; Balakrishnan & Gruca, 2008), asymmetric

cost behaviour is evident in corporate expenditure especially SGA costs.

The issue of information asymmetry is pivotal in finance theory because it is essential to

improving the information environment of firms to enhance the efficiency and

transparency of the capital markets. It is useful to unveil whether information

asymmetry plays a role in the association between stock price informativeness and

8

corporate expenditure. While small firms, firms with low analyst following and firms

with high bid-ask spreads are expected to be linked to higher information asymmetry,

empirical studies show that more private information is produced for these firms (Chen

et al., 2007; Bakke & Whited, 2010). A greater volume of private information enables

better managerial learning of information that managers have yet to possess from the

stock prices of firms. Consequently, managers of these firms are induced to respond

more enthusiastically by changing corporate expenditure in the subsequent year when

stock price informativeness of a current year changes. This study, therefore, investigates

whether the relationship between stock price informativeness of a current year and

corporate expenditure in the subsequent year is dependent on information asymmetry.

As mentioned in item 1.1, three proxies of information asymmetry, namely, firm size,

analyst following and bid-ask spreads are employed in this study.

1.3 Research Objectives

The objective of this research is two-fold. First, it investigates how firm-level corporate

expenditure responds to stock price informativeness. Second, this study seeks to assess

whether information asymmetry plays a role in the relationship between stock price

informativeness and corporate expenditure.

9

1.4 Research Questions

In order to achieve the objectives of this study, the following research questions are

formulated:

a) Does stock price informativeness induce changes in a firm‟s corporate

expenditure in the subsequent year?

b) Is the association between a current year‟s stock price informativeness and the

subsequent year‟s corporate expenditure dependent on information asymmetry?

1.5 Overview of Research Methods and Findings

Data from United States (US) public listed companies (PLCs) for the years 2003 to 2009

(inclusive of both years), covering the post Sarbanes Oxley Act period, is evaluated in

this study. Discretionary corporate expenditures such as R&D expenditure, CAPEX and

SGA costs are examined due to their significant contribution to total corporate

expenditure (inclusive of operating expenses and capital expenditure) of sample firms in

this study. For example, SGA costs make up 32 per cent of total corporate expenditure

while R&D expenditure and capital expenditure comprise 16 and eight per cent of total

corporate expenditure respectively. These three types of corporate expenditure represent

essential strategic tools that firms use to improve their performance. They have also

been widely employed in the strategic management literature (McAlister, Srinivasan &

10

Kim, 2007; Zhang & Rajagopalan, 2010; Srinivasan, Lilien & Sridhar, 2011; Pierce &

Aguinis, 2013).

This study adopts a lead-lag approach as there might be a time delay in the reaction of

stock prices to managerial learning and corporate expenditure decisions. The assumption

of Fama‟s (1970) Efficient Market Hypothesis4 on the immediate dissemination of all

available information and the instant reactions of investors does not always hold true

(Merton, 1987).

Hypothesis 1 of the study posits that stock price informativeness of the current year is

negatively associated with corporate expenditure in the subsequent year, ceteris paribus.

It is predicted that a low level of stock price informativeness is more likely to induce

firm managers to intensify their corporate expenditure to provide positive signals about

firms‟ prospects and future cash flows. This positive news is expected to increase

investors‟ confidence in view of the significance of corporate expenditure such as R&D

expenditure, CAPEX and SGA costs to firm performance as suggested by prior studies.

In contrast, when stock price informativeness is at a high level where investors are well

4 The Efficient Market Hypothesis posits that stock market prices are rational and reflect a high quality of

information about a firm‟s future expected profits. Stock prices may be modelled as a random walk as

they are driven by expectation on firms‟ future profits. Changes in these expectations are incorporated

fully in stock prices with immediate effect (Fama, 1970).

11

informed on firms‟ efficient resource allocation and potential earnings, firm managers

are most likely to maintain a relatively low level of corporate expenditure.

This study finds a significant negative association between a current year‟s stock price

informativeness and the subsequent year‟s R&D expenditure and SGA costs after

considering endogeneity. The impact of stock price informativeness on the level of the

subsequent year‟s R&D expenditure and SGA costs is greater (lower) when the

idiosyncratic volatilities of firms are weakening (strengthening) from the previous year.

It is observed that there is no relationship between a current year‟s stock price

informativeness and CAPEX in the subsequent year.

A “change model”5 is used to investigate the relationship between changes in a current

year‟s stock price informativeness and changes in the subsequent year‟s corporate

expenditure. The results reveal a positive relationship between a change in the current

year‟s idiosyncratic volatility and changes in subsequent year‟s corporate expenditure

represented by R&D expenditure and SGA costs when firm-level stock price

informativeness is improving from the previous year. These findings indicate that firm

managers are likely to learn positive signals from the stock markets as stock price

informativeness strengthens and thereby escalating R&D expenditure and SGA costs in

5 Change model is used to address the issue of reverse causality. In this study, the direction of impact is

specified from a current year‟s stock price informativeness to the subsequent year‟s corporate expenditure.

12

the subsequent year to capture a higher contribution of these corporate expenditures to

shareholders‟ wealth in the long-term.

When stock price informativeness is deteriorating when compared to the previous year,

there is no immediate reaction from firm managers to modify R&D expenditure and

SGA costs. This asymmetric cost response is partly caused by the cost “stickiness”

behaviour of managers. These managers may need to consider whether the phenomenon

of reducing stock price informativeness is temporary or long-term. Thus, they are

unwilling to change corporate expenditure as idiosyncratic volatility declines. This study

observes that firm managers respond by increasing R&D expenditure and SGA costs

only when it is crucial, that is, when the relative idiosyncratic volatility (1-R2) reduces

significantly by 20 per cent. The test of change model, however, suggest that changes in

a current year‟s idiosyncratic volatility is insignificantly related to changes in CAPEX in

the subsequent year regardless of the directions of movement in stock price

informativeness

The findings of asymmetric cost response in R&D and SGA expenditure are consistent

with the cost “stickiness” behaviour demonstrated in the seminal paper by Anderson et

al. (2003). In their study, managers make asymmetric cost adjustments when firms‟

sales revenue moves in different directions as a result of their expectation on future sales

demand. Other studies analyse cost “stickiness” behaviour under different business

environments, for example, Balakrishnan, Petersen and Soderstrom (2004) suggest cost

13

stickiness is dependent on capacity utilisation. Balakrishnan and Gruca (2008) identify

that “cost stickiness” is more prominent in firms‟ core activities compared to their

auxiliary services while Dalla Via and Perego (Forthcoming) examine cost “stickiness”

of small and medium-sized firms.

These findings on R&D and SGA expenditure are consistent with the learning theory

that highlights managers learn valuable private information from firms‟ stock prices and

integrate this new information to improve their corporate expenditure decisions. The

results also indicate that stock price informativeness in a current year is not related to

CAPEX in the subsequent year suggesting that private information obtained from the

capital markets is not a significant determinant of firms‟ capital decisions. Firm

managers are most likely to rely on other factors such as availability of internal cash

flow as well as assessment of risk and return of capital projects in planning firms‟

capital investment.

Hypotheses 2a to 2c posit that the negative associations between a current year‟s stock

price informativeness and subsequent year‟s corporate expenditure is likely to be

stronger in small firms, firms with low analyst following and firms with high bid-ask

spreads respectively. These firms are expected to be linked to higher information

asymmetry. Empirical studies, however, show that more private information is produced

for these firms (Chen et al., 2007; Bakke & Whited, 2010). A greater volume of private

14

information in these firms enables better managerial learning of information that

managers have yet to possess from firms‟ stock prices.

This study finds that the inverse relationship between a current year‟s stock price

informativeness and the subsequent year‟s corporate expenditure represented by R&D

expenditure and SGA costs is stronger for firms with high information asymmetry,

proxied by small firm size, low analyst following and high bid-ask spreads. The findings

also demonstrate that the effect of stock price informativeness on R&D expenditure and

SGA costs is greater (smaller) in these firms when their idiosyncratic volatilities are

weakening (strengthening). These findings are in line with the ideas of the learning

theory and information asymmetry theory (Chen et al., 2007; Bakke & Whited, 2010).

Firm managers of these firms learn from the new firm-specific information available and

are induced to respond more enthusiastically by making changes to firms‟ R&D and

SGA expenditure in the subsequent year when a current year‟s stock price

informativeness changes.

1.6 Significance of the Study

This study offers several essential insights on the multi-faceted linkages between

finance, accounting, strategic management and cost management. First, while existing

empirical studies examine the effects of the learning theory on corporate investment

(Durnev et al., 2003; Durnev et al., 2004; Chen et al., 2007; Foucault & Frésard, 2012)

15

and cash savings (Frésard, 2012), this study provides additional empirical evidence on

how stock prices influence other fundamental corporate actions. It does so by examining

three key corporate expenditures, namely, R&D expenditure, CAPEX and SGA costs.

Adopting the same theoretical principles of the learning theory, this study argues that

firm managers extract valuable private information from the stock prices of their own

firms and make necessary strategic changes in R&D and SGA costs to uphold firms‟

competitive advantage and survival. The results also indicate that stock price

informativeness in a current year is not associated with CAPEX in the subsequent year,

suggesting that capital investment decisions are not dependent on feedback derived from

the capital markets.

Second, this study presents direct evidence of how a current year‟s stock price

informativeness has an effect to the level of corporate expenditure and its changes in the

subsequent year. Hence, this research provides useful understanding of how firms adjust

their corporate expenditure in the subsequent year as stock price informativeness

changes.

Third, this study expands management accounting research by providing new insights

into the outcomes of managers‟ asymmetric cost response in determining corporate

expenditure. The results of this study display cost “stickiness” behaviour of firm

managers when it comes to changing R&D investment and SGA costs in response to

strengthening or weakening idiosyncratic volatility. In this regards, this study

16

contributes to the existing literature on “sticky cost” (Anderson et al., 2003; Weiss, 2010;

Chen, Lu & Sougiannis, 2012b; Kama & Weiss, 2013).

Fourth, this research adds to the literature on information asymmetry by assessing its

role in the relationship between stock price informativeness and corporate expenditure

using three specific proxies: firm size, analyst following and bid-ask spreads. The

outcome is that the relationship between a current year‟s stock price informativeness and

corporate expenditure in the following year is stronger in small firms and in firms with

low analyst following as well as in firms with high bid-ask spreads, hence further

highlighting the significance of this study.

1.7 Structure of the Thesis

This thesis consists of six chapters and it is outlined in Figure 1.1.

17

Figure 1.1 Structure of the Thesis

Chapter 1

Introduction

Chapter 2

Literature Review

Chapter 3

Theoretical Framework & Hypotheses Development

Chapter 4

Research Methodology

Chapter 5

Research Findings &

Discussions

Chapter 6

Conclusion

18

A summary of each chapter is presented as follows:

Chapter 1: Introduction

Chapter 1 begins with a brief background of this research that relates stock price

informativeness to corporate expenditure as well as to information asymmetry. This is

followed by the identification of: research problem statements, research objectives and

the study‟s research questions. Next, an overview of the research methods employed by

this study and the empirical findings obtained from it are presented. The significance of

the research is then delineated. The chapter concludes with an outline of the thesis

structure and a summary of the chapter.

Chapter 2: Literature Review

Chapter 2 evaluates the extant literature on stock price informativeness and corporate

expenditure. The in-depth evaluation of the literature on stock price informativeness

covers its characteristics, measurement and significance, followed by the relevant

empirical evidence provided by both cross-country and firm-level studies. The relevant

literature on R&D expenditure, CAPEX and SGA costs is also presented. It is centred on

their characteristics, benefits and determinants. A summary of the chapter is then

provided.

19

Chapter 3: Theoretical Framework and Hypotheses Development

Chapter 3 outlines the theoretical framework of the current study and formulates the

hypotheses connecting stock price informativeness, corporate expenditure and

information asymmetry. This chapter also presents an overview of the empirical

literature on the theories underpinning the current study. Next, four hypotheses are

developed to predict the association between stock price informativeness and corporate

expenditure as well as to determine whether the relationship is dependent on information

asymmetry. A research model is then presented, followed by a summary of the chapter.

Chapter 4: Research Methodology

Chapter 4 outlines the research methodology employed to examine the hypotheses

developed for this study. The research paradigm adopted for this study is elaborated and

then, the principal population data, sources of secondary data and variables

measurement are outlined. This is followed by a description of the model specification,

as well as the selection procedure used for choosing the sample. An overview of

statistical methodology used and a chapter summary are provided.

Chapter 5: Research Findings and Discussion

Chapter 5 presents the research findings, followed by a discussion of the results of the

current study. It begins with the description of the univariate results. Multivariate results

are then presented, followed by a discussion of the results with regards to the association

between stock price informativeness and corporate expenditure, as well as how this

20

association is dependent on information asymmetry. A chapter summary is then

provided.

Chapter 6: Conclusion

Chapter 6 presents a summary of the research findings in relation to the study‟s

objectives and hypotheses, and is followed by implications of the research. The study‟s

contributions to existing literature are highlighted and this is followed by an outline of

the limitations of the study, recommendations for future research directions and

concluding remarks of the thesis.

1.8 Chapter Summary

This chapter provides the background information related to stock price informativeness,

corporate expenditure and information asymmetry. It further identifies the research

problem statements, research objectives and research questions. An overview of the

research methods used in the study and the empirical findings derived from it are then

presented, followed by an outline of the study‟s significance. The organization of the

thesis is also outlined.

The next chapter presents a review of the existing literature on stock price

informativeness and corporate expenditure, from which the current state of knowledge is

identified.

21

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

The previous chapter (Chapter 1) provides the background information of the thesis

which constitutes the foundation of this study. It specifies the research problem

statements, outlines the research objectives and states the research questions. It also

provides a snapshot of the research methods used and reports on the empirical findings,

followed by an outline of the significance of the study and finally presents the structure

of this thesis.

This chapter (Chapter 2) evaluates the relevant literature of stock price informativeness

and corporate expenditure. The review begins with an overview of literature on

informativeness of stock prices in Section 2.2 in order to comprehend its characteristics,

measurement and significance, followed by both cross-country and firm-level evidence

of the empirical research in stock price informativeness. Section 2.3 deliberates the

characteristics, benefits and determinants of corporate expenditure, particularly in the

area of research and development (R&D) expenditure, capital expenditure (CAPEX) and

22

selling, general and administrative (SGA) costs. Section 2.4 provides the summary of

this chapter.

2.2 Stock Price Informativeness

Stock price informativeness is perceived as uncertainty-reduction in the value of stocks

due to knowledge of the price (Kyle, 1985). Durnev et al. (2003) describe it as “the

extent of information that stock prices contain about future earnings”. Variation in stock

returns is mainly caused by public news and is also attributable to the availability of

firm-specific private information by investors (Ferreira & Laux, 2007). Public

announcements and financial disclosures are integrated into stock prices directly while

firm-specific information gathered by informed investors is embedded into stock prices

through the process of trading (Piotroski & Roulstone, 2004; Gul et al., 2011b).

Grossman and Stiglitz (1980) predict that higher intensity of informed trading is due to a

lower cost of private information. This phenomenon results in higher firm specific

variation and consequently more informative stock pricing (Durnev et al., 2004).

2.2.1 Idiosyncratic Volatility

Stock price informativeness is measured using idiosyncratic volatility. Morck et al.

(2000) observe a long-term rise in idiosyncratic variation of US stock returns from 1926

to 1995 while Campbell, Lettau, Malkiel and Xu (2001) document a substantial upward

pattern in firm-level idiosyncratic volatility relative to market volatility for the years

23

1962 to 1997. Xu and Malkiel (2003) also show that idiosyncratic volatilities of

individual stocks increased during the 1980s and 1990s.

The measure of idiosyncratic volatility was first proposed by Roll (1988). He argues that

the extent to which stocks prices move together is determined by the relative amount of

firm-level and market-level information integrated into stock prices. Roll (1988) uses

Capital Asset Pricing Model (CAPM) to regress firm returns on market returns and

considers the cross-sectional R2

value, being the coefficient of determination, as a

measure of the explanatory power. He observes that a significant portion of stock return

variation is not due to market-wide and industry factors as the market model of his US

sample using 1982-1987 data reports an average adjusted R2

value of only about 35%

using monthly data and a low 20% relying on daily returns.

Roll (1988) further expects an increase in R2 value in asset pricing regression models

when excluding all identifiable public news released by comparing regression models

with and without observations of firms‟ events reported in the financial press.

Nevertheless, he finds no significant improvement in the R2 value, indicating that the

stock return variation is not explained by public announcements. Roll (1988) therefore

suggests that the firm-specific stock price movement noted in the US stock prices is

either attributable to firm-specific private information, or “occasional frenzy” which is

not associated with the firm‟s fundamentals. French and Roll (1986) find that US stock

returns for the years 1963 to 1982 are more volatile during the trading period compared

24

to non-trading period. This is because higher variances occur during the trading period

are attributable to firm-specific information.

Whether stock price movement is explained by firm-specific private information or

“occasional frenzy”, i.e., noise constitutes an empirical issue. However, the first

conjecture of Roll (1988) that firm-specific return variation reflects arbitrageurs trading

on private information is strongly supported by a large body of empirical work and will

be elaborated in detail in item 2.2.4.

A firm‟s stock returns convey new market-level and firm-specific information (Morck et

al., 2000). Idiosyncratic volatility is the component of a firm‟s stock return variation not

explained by market return. It is computed based on the correlation between stock‟s

return and the return of the corresponding industry and that of the market. The rationale

is if a firm‟s stock return is strongly correlated with the market returns, i.e., co-move

with the market, the stock prices are less likely to reflect firm-specific information

(French & Roll, 1986; Roll, 1988). Idiosyncratic volatility would be higher when the

stock return is less correlated with market returns, indicating higher stock price

informativeness (Morck et al., 2000). Thus, idiosyncratic volatility represents the

impounding of firm-specific information into stock prices by informed trading instead of

using public information (Roll, 1988). Idiosyncratic volatility is the logistic

transformation of the value of 1-R2 of a firm (Ferreira & Laux, 2007). It is also the

25

inverse of stock price synchronicity proxied by R2 value (Morck et al., 2000; Jin &

Myers, 2006).

The value of R2 is measured for each firm-year in the sample. By construction, low

values of R2 or alternatively, high values of 1-R

2 (relative idiosyncratic volatility)

indicate that firms‟ stock returns are not closely tied to market and industry returns,

thereby reflecting relatively high firm-specific information (Morck et al., 2000; Stowe &

Xing, 2011).

Table 2.1 summarises the mean and maximum values of relative idiosyncratic volatility

(1-R2) presented by empirical research in the area of stock price informativeness for US

and Chinese companies in the years 2000 to 2013.

26

Table 2.1 Value of Relative Idiosyncratic Volatility (1-R2)

Empirical Research Sample

Size

Years

Covered

1-R2

Mean

1-R2

Max

US Companies

Morck et al. (2000) 7,241 1995 0.979 N.A.

Wurgler (2000) 868 1963-1995 0.874 N.A.

Durnev et al. (2004) 4,029 1990-1992 0.781 0.960

Chen et al. (2007) 68,277 1981-2001 0.830 1.000

Ferreira and Laux (2007) 161,691 1990-2001 0.854 1.000

Fernandes and Ferreira (2009) N.A. 1980-2003 0.807 N.A.

Hutton, Marcus and Tehranian

(2009)

40,882 1991-2005 0.750 N.A

Dasgupta, Gan and Gao (2010) 89,010 1976-2004 0.870 N.A

Ferreira, Ferreira and Raposo

(2011)

11,755 1990-2001 0.738 0.917

Crawford, Roulstone and So

(2012)

613,111 1996-2006 0.890 N.A

Frésard (2012) 88,501 1970-2006 0.790 N.A.

An and Zhang (2013) 79,932 1987-2010 0.842 N.A.

Chan et al. (2013) 26,853 1989-2008 0.777 N.A.

Chinese Companies

Gul, Kim and Qiu (2010) 6,120 1996-2003 0.546 N.A

Gul, Cheng and Leung (2011a) 1,227 2001-2005 0.552 0.988

Xu et al. (2013) 10,326 2003-2010 0.471- 0.537* N.A N.A. - Not available

* A range of mean values is reported.

The regression models applied to calculate the value of relative idiosyncratic volatility

(1-R2) for each of the empirical research listed in Table 2.1 may not be similar, thus

comparisons should be done with caution. The mean value of relative idiosyncratic

volatility (1-R2) of US companies ranges from 0.738 to 0.979 for varying periods

27

covered in these studies. While the maximum value of relative idiosyncratic volatility

(1-R2) is unavailable for most research, it is reported as 1.000 in both studies by Chen et

al. (2007) and Ferreira and Laux (2007), indicating that 100% of the firms‟ stock return

variations are explained by firm-specific information. Compared to US companies,

Chinese companies achieved lower levels of relative idiosyncratic volatility (1-R2).

These results portray higher co-movement between individual firms‟ stock returns with

the market and industry returns among Chinese firms, thereby signifying a lower level

of stock price informativeness.6

2.2.2 Measurement of Idiosyncratic Volatility

Stock price changes are explained by general systematic variation, industry influences

and firm-specific events. Thus, the volatility of a stock‟s return is made up of systematic

risk (as explained by market and industry return) and idiosyncratic volatility (referred to

as unsystematic risk). Following Ferreira and Laux (2007) and Gul et al. (2011b),

idiosyncratic volatility in this study is measured based on a regression estimation of

stock returns for each firm for all fiscal years on the returns of the market index as

follows:

6 Morck et al. (2000) find that China ranked second, after Poland, in stock price synchronicity in their

cross-country study (see Figure 2.1) and they attribute it to poor investor protection. Gul, Kim and Qiu

(2010) explain that low stock price informativeness in China is caused by a lack of enforcement in its

disclosure regulations and managerial entrenchment arising from concentrated ownership in Chinese firms.

28

(2.1)

where:

is daily excess stock returns for firm i and is the daily value-weighted excess

stock return of the market portfolio when the CAPM is used as the model of market

equilibrium. Both coefficients and are estimated for each fiscal year using

regression analysis. In this model, the coefficient is assumed to capture all systematic

risk while idiosyncratic volatility is the variance of being the unsystematic risk that

can be diversified.

The above model is generally estimated using ordinary least squares (OLS) regression

by assuming that ( ) ( ) .

The variance of the stock return, ( ) is the aggregate of two variances measured

as follows:

( ) ( ) (2.2)

Consequently,

where:

and .

29

The first component of Equation (2.2) is the systematic risk while the second is the

idiosyncratic variance( ) The idiosyncratic variance is presented as follows:

(2.3)

where:

The relative idiosyncratic volatility, being the ratio of idiosyncratic variance to total

volatility (

⁄ for each firm-year t, is the proportion of volatility that is not

explained by systematic components. It is equal to the value of 1- from regression

(2.1), where is the coefficient of determination of firm i in year t.

The value of relative idiosyncratic volatility, 1-R2, is not suitable to be used as a variable

in regressions because it is bound within the intervals [0, 1]. A standard econometric

remedy is adopted by applying logistic transformation on the ratio of (1-R2)/R

2

following Morck et al. (2000). The transformed measure generates from a variable

originally bound by zero to an unbound continuous variable with a more normal

distribution.

Formally, idiosyncratic volatility Ψi,t is defined as:

Ψi,t = Ln

= Ln

(2.4)

30

where:

the variable Ψ measures the firm-specific stock return variation relative to market-wide

variation and is the coefficient of determination of firm i in year t.

Idiosyncratic volatility is scaled by total variation in returns as firms in some industries

are more affected by economy-wide shocks, resulting in higher level of firm-specific

activities (Ferreira & Laux, 2007). Idiosyncratic volatility can also be computed using

the Fama and French three-factor model (Fama & French, 1993, 1995, 1996) and

Brockman and Yan (2009) model. The estimations for these regression models are

presented in item 4.5.2.

2.2.3 Significance of Stock Price Informativeness

Higher idiosyncratic volatility is associated with more informative stock prices (Morck

et al., 2000). Goyal and Santa-Clara (2003) found that idiosyncratic volatility constitutes

approximately 80% of total risk on average. Understanding stock price informativeness

is important because of its direct implications to efficient capital allocation (Wurgler,

2000; Durnev et al., 2003), comprehending managerial decisions (Durnev et al., 2004;

Chen et al., 2007; Frésard, 2012) and information gathering about firms‟ future earnings

(Durnev et al., 2003; Jiang et al., 2009). Further, idiosyncratic volatility is tied up with

the following, namely, firms valuation (Stowe & Xing, 2011), corporate governance

31

(Holmström & Tirole, 1993; Ferreira et al., 2011) and cost of equity (Fernandes &

Ferreira, 2009).

An efficient stock market serves to process information to facilitate channelling of

scarce resources to their greatest economic use (Durnev et al., 2003). Idiosyncratic

volatility reflects the integration of firms‟ private information into stock prices and

therefore signifies active trading by informed arbitrageurs. According to Durnev et al.

(2003), this phenomenon denotes that firms‟ stock prices are closer to their fundamental

(full information) value and exhibits an efficient stock market. As such, capital is

suitably priced in its varying uses and firm managers receive meaningful responses

when stock prices change in accordance to their corporate decisions. The authors

suggest that these effects lead to an efficient allocation of capital, as scarce resources

can be invested in sectors that have high returns or are withdrawn from sectors with

deteriorating prospects (Tobin, 1984).

Wurgler (2000) provides evidence that stock markets exhibiting higher firm-specific

stock price movements provide more constructive public signals of investment

opportunities. He examines financial markets across 65 countries and finds that stock

price synchronicity, being the inverse of stock price informativeness, is negatively

associated with the efficiency of capital allocation. This behaviour suggests capital is

better allocated when there is lesser co-movement of stock prices with the market.

According to Wurgler (2000), larger capital markets such as Germany, Japan, the United

32

Kingdom (UK) and the US produce better informed stock prices due to more effective

arbitrage facilitated by higher stock liquidity and lower transaction costs. This

phenomenon allows better differentiation of investment quality by investors and

managers, thereby leading to better capital allocation.

Durnev et al. (2004) document a positive relationship between firm-specific return

variation and the efficiency of corporate investment. They explain that higher stock

price informativeness encourages better corporate governance, resulting in capital

investment decisions that are more efficient and better aligned with shareholder value

maximisation. As such, capital investments are more efficient when the stock prices

convey more firm-specific information. In a similar vein, Chen et al. (2007) investigate

the relationship between the amount of private information in stock price (proxied by

value of 1-R2) and the sensitivity of investment to stock price and they find robust

positive correlation. This result portrays that investment responds more to stock prices

when the stock price informativeness is higher. According to Chen et al. (2007), firm

managers apply information they learn from the stock markets to make investment

decisions. Applying the same rationale, Frésard (2012) demonstrates that corporate cash

savings are more sensitive to stock prices when the prices convey more information that

is new to firm managers.

Higher level of stock price informativeness is also linked to more information about

firms‟ future earnings embedded in current stock prices. This finding is supported by

33

Durnev et al. (2003) who find a significant positive association between idiosyncratic

volatility and accounting measures of stock price informativeness (which represents how

much information stock prices reveal about future earnings by estimating from a

regression of current stock returns against future earnings). Their findings conclude the

linkage of greater idiosyncratic volatility and better functioning of stock markets. Jiang

et al. (2009) further suggest that idiosyncratic volatility reflects information of firms‟

future earnings.

Stock price informativeness affects investors‟ evaluation of firm value. Stowe and Xing

(2011) used a sample of 90,111 firm-year observations for the years 1990 to 2004 and

document that firms with higher R2 value tend to be overpriced. This is because

investors depend more on market-wide information in the valuation of the high-R2 firms

and tend to ignore some firm-specific information leading to infrequent evaluation.7

High-R2 firms under-perform in the long-term when compared to low-R

2 firms,

suggesting that the former are more likely to be riskier. Hence, these high-R2 firms are

likely to be more susceptible to agency problems as their managers tend to resort to

manipulation when they discover subsequently that they cannot achieve the required

firm performance level so as to maintain high stock prices (Jensen, 2005).

7 According to Benartzi and Thaler‟s (1995) theory of myopic loss aversion, when an investment is

evaluated less frequently, the same investment would appear to be more valuable to investors, who are

typically loss averse. Thus, high-R2 firms are likely to be overvalued. Alternatively, high-R

2 firms tend to

behave like the market (as co-move with the market) and thus seem to be more attractive to the investors

leading to higher tendency of overpricing (Stowe & Xing, 2011).

34

Firm-specific information conveyed by stock prices can also affect corporate

governance as it provides warning to the financial market to interfere especially when

firms are not managed well. Durnev et al. (2004) suggest that stock prices determine the

corporate governance mechanisms, for example, shareholder lawsuits, executive options,

pressure from institutional investors, and the market for corporate control. Holmström

and Tirole (1993) investigate whether stock prices monitor managerial performance.

They demonstrate that stock prices integrate information that cannot be obtained from

firms‟ current and future financial data but is effective to discipline managers. A firm

with deteriorating financial performance may become a takeover target and result in

possible dismissal of their managers if a takeover occurs. This risk will assist in

curtailing misconduct among firm managers. Further, a more informative stock market

facilitates the designing of executive compensation packages in accordance to firms‟

stock prices performance.

Apart from improving external monitoring mechanisms by disciplining managers

(Holmström & Tirole, 1993), information revealed by stock prices play a fundamental

role in enhancing the internal monitoring role of the board of directors. Ferreira et al.

(2011) find that stock price informativeness determines board structure. They argue that

information reflected by stock prices affects the manner how managers are monitored in

two ways. First, more informative stock prices would enable better external monitoring

mechanisms. Consistent with Holmström and Tirole‟s (1993) rationale, Ferreira et al.

35

(2011) opine that firms become cheaper takeover caused by plummeting stock prices

due to value-detrimental projects. This threat prevents firm managers from making

unfavourable corporate decisions. Second, more informative stock prices provide new

firm-specific information to both the markets and boards of directors whereby informed

directors can utilise the information conveyed by stock prices to improve their

monitoring responsibility. Using a sample of 9,447 firm-year observations for the years

1990 to 2001, Ferreira et al. (2011) found robust negative association between stock

price informativeness and board independence, suggesting that stock market monitoring

and board monitoring are substitutes. The authors opine that firms with better informed

stock prices are associated with less demanding boards, for example, a lower level of

board independence and fewer board meetings. They conclude that board structure is

dependent on firms‟ stock price informativeness.

Empirical research also shows that stock price informativeness helps to reduce cost of

equity. In an investigation of the relationship between initial enforcement of insider

trading laws and stock price informativeness in 48 countries, Fernandes and Ferreira

(2009) discover that idiosyncratic volatility reduces the risk for uninformed investors

and contributes to the reduction in cost of equity arising from implementing insider

trading laws.

36

2.2.4 Empirical Research on Stock Price Informativeness

The following sub-items briefly elaborate on the cross-country and firm-level evidence

gathered from empirical findings on stock price informativeness.

2.2.4.1 Empirical Research – Cross-Country Analysis

A developing body of finance literature provides evidence on the information-based

interpretation of idiosyncratic volatility using cross-country analysis. It demonstrates the

importance of strong macro infrastructure in terms of efficiency of judicial systems,

investor protection and financial reporting to ensure the effectiveness of stock price

informativeness.

Morck et al. (2000) examine worldwide stock return synchronicity (inverse of

idiosyncratic volatility) at the country-level in 1995 using R2 value. The R

2 value

measures the percentage of total bi-weekly firm-level return variation of each country

that is explained by the local and US value-weighted market indexes. Figure 2.1

graphically highlights the differences of stock return synchronicity across countries.

37

Figure 2.1 Stock Return Synchronicity in Various Countries in 1995

Source: Morck et al. (2000), pg 227

The leading country for stock return synchronicity is Poland (about 58%) while

Malaysia (about 43%) is in the third position after China (about 46%). United States has

the lowest stock return synchronicity at less than five per cent suggesting that stock

38

prices in the US reflect fundamental values. Morck et al. (2000) observe that stock

prices move more in the same direction in poor economies than in rich economies,

indicating low idiosyncratic volatility in emerging markets but the reverse is true in

developed markets. They argue that the finding is not due to market size, country size,

nor economy diversification but attribute it to stronger perception of “investors‟ private

property rights” in developed countries. Better investor protection against corporate

insiders discourages income shifting by major shareholders and promotes informed

arbitrage. This leads to capitalization of more firm-specific information in share prices

and results in less co-movement in stock returns across firms in developed countries,

consistent with the explanation of Roll (1988).

Irvine and Pontiff (2009) provide a new interpretation of evidence from Morck et al.

(2000). They relate the higher idiosyncratic risks observed in the US for the years 1964

to 2003 to an increasingly competitive environment in which firms have lesser market

power. Information opacity discourages product market competition of a country,

thereby affecting its primary business environment (instead of through stock trading)

which in turn affects R2

value of the stock return. Cross-country analysis by Irvine and

Pontiff (2009) show countries with more competitive economies experience greater

growth in idiosyncratic risk.

Jin and Myers (2006) further explain that information opacity combined with minimal

shareholder protection enable managers to appropriate firms‟ cash flow and lowers stock

39

price informativeness. Idiosyncratic volatility is reported to be higher in countries with

developed and transparent financial markets where informed traders are motivated to

gather private information. However, countries with weak governance and opaque

accounting would induce low idiosyncratic volatility. Moreover, Jin and Myers (2006)

find that opaque stocks with higher R2 value (lower idiosyncratic volatility) have higher

likelihood to crash, that is, to deliver large negative returns when compared to stocks in

relatively transparent countries. Higher crash rates are associated with lower

idiosyncratic volatilities of 40 stock markets around the world in the years 1990 to 2001.

This is because managers of opaque firms conceal bad news from investors and reduce

their extraction of cash flows to safeguard their own positions. However, the

accumulated bad news finally reaches a maximum level when all bad news are released

simultaneously which results in a stock price crash.

Li, Morck, Yang and Yeung (2004) compare the co-movement of individual stock

returns across 17 emerging countries for the years 1990 to 2001. They observe a

significant positive association between capital market openness and idiosyncratic

volatility in most emerging markets economies, especially those with financially stable

institutions. The authors further find that higher firm-specific return variation is strongly

correlated with greater capital market openness, but not products market openness.

Extending studies on emerging countries, Hsin and Tseng (2012) focus on whether stock

price informativeness is determined by investors‟ trading behaviour and the level of

globalization of the market. They observe that the likelihood to be involved in

40

speculative trade and a lower connection with the world market results in lower stock

price informativeness in emerging countries. Difficulties arise in valuing firm-level

fundamentals in a speculative market as it generates more “noise” trading. The stock

price co-movement is also found to be more pronounced in bearish markets implying

that investors may be more loss averse during down markets and this restricts informed

arbitrage, thereby reducing stock price informativeness.

Brockman, Liebenberg and Schutte (2010) discover counter-cyclical patterns in stock

return co-movement in 36 countries for the years 1980 to 2007, that is, co-movement

increases during economic recessions and decreases during economic expansions. The

authors suggest that stock return movement is caused by information production as both

the volume and attributes of information produced are more (less) superior during

economic expansions (recessions), resulting in reduction (increase) in stock return co-

movement. They further find that the relationship between business cycle and co-

movement is weaker in high-income countries as well as those countries that have more

developed financial markets and transparent environment. This finding suggests that

both financial development and transparency are instrumental to ensure a better flow of

information under any economic situation.

In another related study, Daouk, Lee and Ng (2006) developed a Capital Market

Governance (CMG) index from individual stock exchanges of 32 countries for the years

1969 to 1998. The CMG index encapsulates three dimensions of security laws: the

41

extent of earnings opacity, the implementation of insider laws, and the impact of

eliminating constraint of short-selling. Their empirical results show that the

improvement in CMG index is linked to increased idiosyncratic volatility.

Furthermore, Fernandes and Ferreira (2009) examine the relationship of initial

enforcement of insider trading laws and stock price informativeness in 48 countries for

the years 1980 to 2003. The implementation of insider trading laws prohibits trading by

insiders and reduces instances of crowding-out, i.e., preventing others to gather

information. These regulations encourage informed trading by outside investors, hence

more informed market participants are prepared to invest their resources to collect

information about a firm, thereby increasing informativeness of stock prices. Fernandes

and Ferreira (2009) observe that firm-specific return variation is greater after the

enforcement of insider trading laws but only in developed markets. The enforcement of

insider trading laws is insignificantly related to the impounding of information into

stock prices in emerging countries with poor legal institutions. Insider trading

contributes to the role of price discovery, that is, integration of information into stock

prices in emerging countries. Other informed market participants such as analysts,

cannot add to the missing information caused by the discontinuation of insider trading

upon enforcement of insider trading laws, resulting in little advancement in stock price

informativeness in emerging countries.

42

Fernandes and Ferreira (2008) find that cross-listing in US increases price

informativeness of firms located in developed markets as a result of additional

disclosure and scrutiny, but decreases price informativeness of firms in emerging

markets. This is mainly because greater analyst coverage arising from cross-listing in

emerging markets facilitates production of market wide information, instead of firm-

specific information, in line with the findings in Chan and Hameed (2006). The

increased disclosure associated with US exchange rules can crowd out private

information gathering in emerging countries, showing that disclosure standards must be

complemented with other policy initiatives to motivate better production of firm-specific

information in emerging countries.

Both voluntary and mandatory adoption of International Financial Reporting Standards

(IFRS) increase stock price informativeness. Kim and Shi (2010) analyse 15,382 firm-

year observations of both IFRS adopters and non-adopters from 34 countries for the

years 1998 to 2004. Their results highlight that stock price informativeness improves

among voluntary IFRS adopters compared to IFRS non-adopters. Voluntary IFRS

adoption improves firms‟ financial disclosures, thereby motivating investors to gather,

process and trade on firm-specific information to increase informativeness of stock

prices. On the other hand, Beuselinck, Joos, Khurana and Van der Meulen (2009) found

a V-shaped pattern in synchronicity (the inverse of non-synchronicity) during IFRS

adoption among 2,071 mandatory IFRS adopters in 14 European Union countries for the

years 2003 to 2007. The V-shaped curve displays reduction in synchronicity around the

43

time of mandatory IFRS adoption and increases in stock return synchronicity in the

post-IFRS adoption period. The authors explain that the improvement in stock price

informativeness around mandatory IFRS adoption period is contributed by a reduction

in firm opacity arising from required IFRS disclosures. A greater amount of transparent

information, however, has reduced the anticipated “surprise” events that could occur in

the future, leading to decrease in informativeness of stock prices after the IFRS adoption

period.

Broadly speaking, foreign investors are expected to play a significant role in influencing

stock price informativeness. In this regard, He, Li, Shen and Zhang (2013) find that

large foreign ownership8

is positively related to price non-synchronicity. The

researchers used a cross-sectional data of 3,189 firms in 40 markets for the year 2002.

Large foreign shareholders are found to improve the informativeness of stock prices via

informed trading as they are more likely be motivated and are competent in gathering

and processing value-relevant information. The information-based trading by large

foreign shareholders helps the incorporation of private information into stock prices, for

instance, selling their shares when receiving negative information. Large foreign

shareholders can also boost stock price informativeness through enhanced firms‟

corporate governance and disclosure quality. Large foreign shareholders are able to

8 Large foreign ownership is defined as those ultimate owners who own more than 5% of outstanding

shares of a firm and are domiciled outside the country of the invested firm.

44

monitor managers attentively and reduce agency costs, leading to more informative

stock prices. Consistent with other international studies, He et al. (2013) discover that

large foreign ownership has a stronger impact on stock price informativeness in

developed markets with higher investor protection and a transparent information

environment.

In summary, these empirical studies exhibit that stock price informativeness is higher

when property rights are stronger with improved corporate governance control.

2.2.4.2 Empirical Research – Firm-Level Studies

The use of idiosyncratic volatility as a proxy of stock price informativeness at firm level

is empirically supported by several studies in the area of corporate governance (Ferreira

& Laux, 2007; Khanna & Thomas, 2009; Gul et al., 2011a; Gul et al., 2011b), firms‟

ownership structure (Brockman & Yan, 2009; Gul et al., 2010; Hou, Kuo & Lee, 2012;

An & Zhang, 2013; Ding, Hou, Kuo & Lee, 2013) and earnings management (Hutton et

al., 2009). Other studies are equally well supported in the areas of financial reporting

(Haggard, Martin & Pereira, 2008), market competition (Gaspar & Massa, 2006; Peress,

2010), cross-listing (Foucault & Gehrig, 2008; Foucault & Frésard, 2012), earnings

growth (Xu & Malkiel, 2003) and market participants (Piotroski & Roulstone, 2004;

Crawford et al., 2012; Xu et al., 2013).

45

a) Corporate governance

Ferreira and Laux (2007) examine the relationship between corporate governance and

stock price informativeness and they find firms with fewer anti-takeover provisions

exhibit more advanced level of stock price informativeness. According to them, fewer

anti-takeover provisions signify openness to market for corporate control as well as

openness to information sharing. This leads to greater shareholder protection and in turn

improves stock price informativeness through increased transparency. Ferreira and Laux

(2007) suggest that fewer anti-takeover provisions in a firm induce its investors to

gather more firm-specific private information to evaluate the probability of a potential

takeover. Exposure to market forces also discipline managers from expropriating outside

investors and attracts more uninformed investors to trade based on private information.

Informed trading improves the impounding of private information into stock prices and

increases stock price informativeness.

Gul et al. (2011b) show a positive connection between gender diversity in corporate

boards and stock price informativeness for US companies during 2001 to 2007. Gender-

diverse boards improve stock price informativeness by improving voluntary public

disclosures in large firms and by motivating gathering of firm-specific information in

small firms. On the other hand, Khanna and Thomas (2009) examine the relationship

between stock price synchronicity (inverse of stock price informativeness) and joint

46

control of firm activities using a unique data set. This data set provides information on

the extent of equity cross-ownership, common shareholders and director interlocks9 in

Chile, an emerging country. Lower stock price informativeness is found to be

significantly related to joint control activities especially for firms possessing

interlocking directorships. Firms‟ stock prices are more likely to have co-moving returns

with the existence of interlocking directorship. This is because director relationships are

viewed by the capital market as reducing corporate transparency. As such, Khanna and

Thomas (2009) suggest weak corporate governance leads to deteriorating stock price

informativeness.

Gul et al. (2011a) relate perks and informativeness of stock prices in the Chinese market.

Chinese firms with better perquisites are linked to lower stock price informativeness as

high perk consumption, representing agency problems, is perceived negatively by the

market. Further, this negative relationship between perks and stock price

informativeness is attenuated in firms with better quality of financial reporting,

reflecting the positive impact of corporate governance mechanism. As such, Gul et al.

(2011a) suggests that stock price informativeness is dependent on both perks and

financial report quality, in line with the study of Jin and Myers (2006).

9 In the data used by Khanna and Thomas (2009), there are three types of interlocks between firms: (1)

firms‟ ownership of equity stake in each other, (2) partial ownership in firms by the same individual(s),

and (3) common directors of both firms.

47

Auditor quality is shown by Gul et al. (2010) to be positively related to stock price

informativeness. Auditors are important in improving the quality of the information

contained in financial statements and reducing information asymmetries between

insiders and investors (Dopuch & Simunic, 1982). They facilitate the flow of more

reliable firm-specific information to the capital markets by increasing credibility to

firms‟ financial reports, thereby improving the capitalization of firm-specific

information to the stock prices. Gul et al. (2010) examine this phenomenon among

Chinese listed firms which have relatively weak shareholder protection and they do not

generally appoint Big-4 auditors. The researchers found that the appointment of Big-4

auditors is related to higher level of idiosyncratic volatility. Strict adherence to firm-

level corporate governance is shown to improve the information environment in

emerging countries where country-level shareholder protection is relatively weak.

b) Ownership structure

Ownership structure plays an essential role in firms‟ information environment.

According to Brockman and Yan (2009), block ownership (concentrated ownership) in

US firms for the years 1996 to 2001 tend to have access to more precise firm-specific

information at a lower cost as opposed to diffuse shareholders, leading to higher

idiosyncratic volatility.

48

By contrast, Gul et al. (2010) found a concave association between stock price

synchronicity and ownership by the largest shareholder of listed firms in China with its

peak at an approximately 50 per cent shareholding. Their Chinese sample consists of

6,120 firm-year observations for the years 1996 to 2003. This is because stock price

synchronicity initially escalates at a declining rate due to entrenchment effect whereby

major shareholders are induced or provided with opportunities to expropriate firm

resources at the expense of minority shareholders (Johnson, La Porta, Lopez-de-Silanes

& Shleifer, 2000). When the percentage of ownership by the largest shareholders

exceeds certain level, the stock price synchronicity starts to diminish, indicating

improving stock price informativeness. This is mainly attributable to the incentive

alignment effect because firms now feature as if it is a private company in the

possession of major shareholders. In addition, Gul et al. (2010) find that stock price

informativeness is lower when the largest shareholder is government-related. Ownership

of firms by government results in problems of poor investor protection and a lower level

of transparency in financial reporting, leading to lower integration of firm-specific

information into stock prices. Their investigation also reveals that foreign ownership is

positively associated with stock price informativeness as it enables a better flow of

reliable private information into the capital markets.

Hou et al. (2012) further examine the consequences of state ownership on stock price

informativeness in China using a distinctive example of the Split Share Structure

49

Reform. Prior to the reform, the restricted shares held by state shareholders could be

traded freely in the Chinese stock market compared to shares owned by private

shareholders. Thus, the wealth of state shareholders was not dependent on the share

price movement in the capital market. In the year 2005, the trading constraint imposed

on restricted shares was removed and therefore the wealth of these stakeholders is now

influenced by share price movements. This aligns the interests of both state and private

shareholders in monitoring firm managers and therefore, lowers information asymmetry

problems. The empirical results of Hou et al. (2012) show that the Split Share Structure

Reform increases stock price informativeness, especially among those who owned more

restricted shares.

In another Chinese study, Ding et al. (2013) show that mutual fund ownership improves

stock price informativeness as institutional investors are more knowledgeable and

motivated to monitor firms when compared to individual shareholders. Mutual funds are

more developed and play an external monitoring role to improve transparency, thereby

increasing the stock price informativeness. However, the positive effect of mutual funds

on the revelation of firm specific information in stock prices in China is attenuated in

state-controlled firms. The state ownership of listed firms decreases the reliance on

Chinese capital markets for external funding, thereby weakening monitoring capabilities

of mutual funds on managers.

50

An and Zhang (2013) provide collaborative evidence using their US study. Stock price

informativeness is positively associated with shareholdings by dedicated institutional

investors. These investors hold huge shareholdings on long investment horizons, hence

are motivated to scrutinize their portfolios. Management entrenchment is constrained by

better institutional monitoring, thereby leading to impounding of more firm-specific

information into stock prices. Nevertheless, the relationship between institutional

ownership and transient institutional investors shows the opposite direction as these

investors are more interested to trade rather than for long-term investment. Therefore,

firm managers are able to expropriate firms‟ cash flows due to lesser monitoring by

transient institutional investors who hold smaller interest in those firms, thereby

resulting in lower stock price informativeness.

c) Earnings management

Hutton et al. (2009) establish a negative link between opacity (measured by earnings

management) and idiosyncratic volatility using 40,882 US firm-year observations for

the years 1991 to 2005. Their observations suggest that earnings manipulation impedes

the flow of firm-specific information to the capital markets and it predicts crash risk of

opaque firms reliably, thereby confirming the findings of Jin and Myers (2006).

However, this phenomenon seems to disappear after the enforcement of Sarbanes Oxley

Act in the year 2002, suggesting that earnings management has reduced considerably

due to better monitoring.

51

d) Financial reporting

Improving disclosures and the quality of financial reporting alleviate information

asymmetries (Diamond & Verrecchia, 1991). Haggard et al. (2008) provide evidence

that enhanced voluntary disclosure increases stock price informativeness through a

lower cost of acquiring information and improvement in corporate transparency.

Applying analysts‟ assessment of disclosure policies adopted by firms for the years 1982

to 1995, Haggard et al. (2008) demonstrate that their finding is consistent with the study

by Jin and Myers (2006) as greater firm transparency is shown to improve stock price

informativeness.

By contrast, Rajgopal and Venkatachalam (2011) observe that weakening financial

reporting quality, which is represented by accrual based measures of earnings quality, is

linked to an improvement in idiosyncratic volatility for the years 1962 to 2001 in the US.

They claim that their finding is consistent with the practitioners‟ viewpoint on opacity

and irrelevancy of financial reports. Chen, Huang and Jha (2012a) also document that

information quality revealed in managerial discretion is associated with idiosyncratic

volatility whereby firms with weaker information quality have greater idiosyncratic

volatility.

e) Market competition

Competitive environment faced by firms can also influence the idiosyncratic volatility

of their stock returns. Gaspar and Massa (2006) report that low idiosyncratic volatility is

52

observed in firms possessing high market power or firms that are well established in

concentrated industries. According to them, market power acts as a hedging instrument

that eases idiosyncratic fluctuations whereby a large component of any idiosyncratic

cost shocks is transferred to the firms‟ customers to protect their profits. In addition,

high market power implies lesser information ambiguity for the investors that results in

lower stock return volatility. Their findings is consistent with a cross-country study by

Irvine and Pontiff (2009) that show countries experiencing intense competition exhibit

greater growth in idiosyncratic risk. However, Peress (2010) has a different view. He

demonstrates that firms with more market power generate higher trading volumes as

their profits are less risky. The enhanced trading activity for these firms, especially

among the informed investors, improves the impounding of fundamental information

into stock prices. Therefore, Peress (2010) suggests that firms with higher market power

has more informative stock prices.

f) Cross-listing

A study by Foucault and Gehrig (2008) reveals that cross-listing intensifies informed

trading, thereby strengthening the informativeness of firms‟ stock prices on their future

cash flows and growth opportunities. Managers of cross-listed firms benefit from more

informative stock prices by making more efficient investment decisions. Accordingly,

cross-listed firms achieve higher firm value as compared to non-cross-listed firms. This

finding is empirically supported by Fernandes and Ferreira (2008) who report that cross-

listing is positively linked to stock price informativeness. Further, the findings in

53

Foucault and Gehrig (2008) help to explain why cross-listed firms in the US possess

higher sensitivity of investment-to-stock prices than firms that were never cross-listed as

documented by Foucault and Frésard (2012).10

g) Earnings growth

Xu and Malkiel (2003) investigate the influence of earnings growth on idiosyncratic

volatility and they observe a positive relationship between long-term growth rate and

idiosyncratic volatility. This confirms their hypothesis that high-growth firms operating

in an industry with rapid technological changes are required to invest in distinctive

projects and hence, are associated with higher level of idiosyncratic risk.

h) Market participants

Piotroski and Roulstone (2004) examine how three different types of informed market

participants, namely, financial analysts, institutional investors and insiders determine the

information environment. They find that analyst activities are positively related to stock

return synchronicity (inverse of idiosyncratic volatility) while insider trading is

negatively related to stock return synchronicity. This implies analysts increase the

amount of industry information in prices while insiders convey firm-specific

information into stock prices.

10 Refer to item 3.2 for discussion of findings in Foucault and Frésard (2012).

54

Chan and Hameed (2006) also find that greater analyst coverage in emerging countries

is associated with lower stock price informativeness. Xu et al. (2013) observe similar

findings in China but they extend the study by examining whether highly regarded

analysts, or known as star analysts, are able to reduce the extent of stock price

synchronicity. They found that the star analysts are able to produce firm-specific

information and increase stock price informativeness as a result of their outstanding

human capital and firm-specific experience, whilst non-star analysts generate more

market and industry-level information in stock prices, thereby reducing stock price

informativeness.

Crawford et al. (2012) also investigate how analysts contribute to the mixture of firm-

specific, industry and market-wide information available about a firm and they focus on

the analysts‟ initiation of coverage on a firm. When analysts initiate coverage on firms

with no prior following, the stock price informativeness is lower, indicating that analysts

provide low-cost industry and market-wide information so that they can increase their

coverage to greater number of firms. However, when analysts initiate coverage on firms

with existing analyst following, they tend to generate firm-specific information to

discriminate themselves from the existing analysts. Therefore, Crawford et al. (2012)

conclude that the type of information that analysts produce at initiation of coverage is

dependent on the information provided by other analysts.

55

2.2.5 Inconsistent Findings

While the literature has provided a variety of reasoning for using idiosyncratic volatility

as a proxy of stock price informativeness, other empirical studies have contradicting

views on whether this measure is capable of capturing the amount of private information,

for example, Kelly (2005), Dasgupta et al. (2010) and Alves, Peasnell and Taylor (2010).

Kelly (2005) finds that low R2 (indicating higher 1-R

2) in a firm does not reflect

incorporation of greater extent of firm-specific information into stock prices while

Dasgupta et al. (2010) argue that a more transparent firm can have a higher R2 value

(inverse of idiosyncratic volatility), contrary to the conventional wisdom. Using data

from 40 countries over 20 years, Alves et al. (2010) conclude that R2 is insufficient to

represent the quality of the information environment for a country. They perceive it is

beyond belief that R2 of a country can change considerably when a country‟s corporate

governance and investor protection level are assumed constant.

On the other hand, Lee and Liu (2011) contend that the relationship between stock price

informativeness and idiosyncratic volatility is U-shaped. In their model, idiosyncratic

volatility is decomposed into two elements, namely, noise element (arising from demand

by liquidity traders) as well as information element comprising of an information-

updating portion and an uncertainty-resolving portion. Lee and Liu‟s (2011) model

shows that the noise element decreases with price informativeness while the information

element firstly decreases and then increases with price informativeness. As stock price

56

informativeness increases, the information-updating portion increases but the

uncertainty-resolving portion declines. Thus, the information element of idiosyncratic

volatility being the aggregate of information-updating portion and uncertainty-resolving

portion has a U-shape with price informativeness because the average variance over

time is the lowest when the uncertainty is resolved steadily. Lee and Liu (2011) attempt

to reconcile two opposing views in the research on stock price informativeness: Morck

et al. (2000) and Jin and Myers (2006) conclude firms with greater information in stock

price reveal higher idiosyncratic volatility while Kelly (2005) and Dasgupta et al. (2010)

find the reverse is true.

2.3 Corporate Expenditure

Three types of corporate expenditure are selected in this study to examine how each of

them responds to stock price informativeness: R&D expenditure, CAPEX and SGA

costs. R&D expenditure and CAPEX are basic corporate resource allocations while

SGA costs address a firm‟s expense structure. They contribute significantly to total

expenditure (inclusive of operating expenses and capital expenditure) of the sample

firms in this study, for example, SGA costs comprise 32 per cent of firms‟ total

expenditure while R&D expenditure and CAPEX contribute 16 and eight per cent of

total expenditure respectively. These corporate expenditures are discretionary in nature

as they are determinable and controllable by managers and they have a significant

impact on firm performance. Furthermore, they are complementary and represent

57

different features of a firm‟s strategic profile (Finkelstein & Hambrick, 1990; Carpenter,

2000; Zhang & Rajagopalan, 2010). Drawing from the strategic management literature,

R&D expenditure, CAPEX and SGA costs are also part of the six key strategic

dimensions11

used in measuring strategic change12

(Finkelstein & Hambrick, 1990;

Carpenter, 2000; Zhang & Rajagopalan, 2010). Strategic changes are undertaken by

firms in their alignment with external environment (Rajagopalan & Spreitzer, 1997) to

provide competitive advantage and ensure firm survival (Carpenter, 2000).

The following items briefly provide relevant literature on R&D expenditure, CAPEX

and SGA costs, with a focus on their benefits and determinants.

2.3.1 Research and Development Expenditure

Research and development activity generates knowledge assets that allow firms to

produce new and more superior products, more efficient production methods and

innovated form of business organizations (Erickson & Jacobson, 1992). R&D activities

not only result in the radical production of new products, processes or organizational

forms but also involves incremental improvements to existing goods and methods of

11 The other three key strategic dimensions used to measure strategic change are advertising expenditure,

inventory levels and financial leverage (Finkelstein & Hambrick, 1990; Carpenter, 2000; Zhang &

Rajagopalan, 2010).

12 Strategic change is the variation over a long period of time in the firm‟s pattern of resource allocation in

various key strategic dimensions (Finkelstein & Hambrick, 1990; Zhang & Rajagopalan, 2010).

58

production (Shadab, 2008). Cohen and Levinthal (1989) suggest that in addition to

producing new information, R&D initiatives improve firms‟ absorptive capacity to

recognise, adapt and develop knowledge from the environment.

A review of literature on R&D expenditure is presented from the perspective of

economic return and risk. The study also explores the determinants of R&D expenditure.

2.3.1.1 R&D Expenditure and Return

There is a large body of research in finance, management and marketing that relates

R&D activities to firms‟ financial performance. Investment in R&D contributes

substantially to firms‟ productivity and output, hence, shareholders‟ wealth, and its

benefit extends over a long period of time (Lev, 1999). R&D expenditure is viewed as

an intangible capital with positive consequences on sustained future cash flows

(Hirschey, 1985; Chauvin & Hirschey, 1993). Connolly and Hirschey (2005) document

a significant positive impact of R&D intensity on Tobin‟s Q, consistent with Griliches

(1981) and Pakes (1985). Capon, Farley and Hoenig (1990) perform a meta-analysis of

320 published studies between 1921 and 1987 to examine the determinants of financial

performance and observe that R&D expenditure is significantly associated to increased

profitability.

59

Sougiannis (1994) demonstrates that reported accounting earnings reflect benefits from

past R&D expenditure. Eberhart, Maxwell and Siddique (2004) find a positive abnormal

long-term operating performance following an unexpected increase in R&D expenditure,

thereby indicating that R&D initiatives are beneficial investment. Value creation via

R&D projects, in combination with value appropriation (through advertising activities),

enhances firm value (Mizik & Jacobson, 2003). Innovation is more likely to generate

persistently high level of profits (Roberts, 2001) and brings greater welfare to

consumers, shareholders, firms and economies (Shadab, 2008). Ho, Tjahjapranata and

Yap (2006) find R&D investment has a positive impact on firms‟ growth opportunities

while Erickson and Jacobson (1992) opine that increasing R&D expenditure provides a

positive signal to the market that the firms possess the discretionary funds needed to

embark on R&D projects.

Past research findings also show that the market considers corporate R&D activities as

investments that generate future benefits, thus assigning significant valuation to firms

undertaking such projects (Kothari, Laguerre & Leone, 2002). For example, Lev and

Sougiannis (1996) and Chan, Lakonishok and Sougiannis (2001) find a significant

positive association between R&D investment and excess risk-adjusted returns in the

subsequent years, hence confirming the value relevance of R&D expenditure to

investors. Penman and Zhang (2002) also report a positive association between R&D

growth (changes in R&D investment) and subsequent excess return. The tentative

explanation offered by these studies is that shares of R&D-intensive firms are mispriced

60

as investors are unable to see through earnings distortion caused by the nature of

accounting for R&D costs, that is, by immediately expensing R&D expenditure in the

current year‟s financial statements. Firms‟ earnings are likely to be understated when

R&D investment increases and vice versa. Therefore, R&D investment does not appear

to be fully reflected in current year stock prices but instead are seen in future stock

returns. Eberhart et al. (2004) also observe significantly positive abnormal stock returns

for a period of five years after unexpected increases in R&D expenditure. They interpret

it as an evidence of investors‟ under-reaction to the intangible information that R&D

investment reflects on future cash flows. However, Chan et al. (2001) opine that

managers who are confident about firms‟ future growth will continue to invest heavily

on R&D activities in spite of unfavourable past firm performance as well as the

necessity to minimise cost but the market tends to overlook the positive signals of R&D

investment.

Lev and Sougiannis (1996) offer an alternative explanation of substantial future risk-

adjusted stock returns: investors‟ requirements of higher expected returns from these

R&D-intensive firms as a reward for risk-taking. Chambers, Jennings and Thompson

(2002) distinguish between systematic mispricing and risk explanations for R&D-related

excess returns. They demonstrate that excess returns reported in previous studies are

attributable to risk associated with R&D activities. Nevertheless, Ciftci, Lev and

Radhakrishnan (2011) compare high and low R&D-intensity firms. They discover that

high R&D-intensity firms tend to innovate by developing new products while low

61

R&D-intensity firms are more likely to imitate, indicating lower level of risks. High

R&D-intensity firms are also found to produce larger risk-adjusted returns in the short-

term. Subsequently, the excess returns of these firms become identical to those of the

low R&D-intensity firms. Thus, Ciftci et al. (2011) claim that the pattern of returns

reversal is consistent with mispricing explanation but inconsistent with risk justification.

Srinivasan et al. (2011) investigate the effects of R&D spending on profits and stock

returns during recessionary periods. They observe that firms with greater market share

gain more from R&D expenditure during recessions, leading to higher profits and stock

return. Firms with higher market share are in a better position to achieve economies of

scale in their R&D projects and leverage their R&D outputs effectively during

recessions. Srinivasan et al. (2011) highlight that increased investors‟ anticipation of

future risk-adjusted cash flows leads to higher stock returns. The positive impact of

R&D investment on firms‟ profits and stock returns are portrayed in highly leveraged

firms which increase their R&D expenditure during recessions. This phenomenon

provides a positive signal to the market that these firms are of good quality as they are

able to lower their cost of capital during recessions, hence increasing security for their

lenders.

Bublitz and Ettredge (1989) examine the endurance of R&D expenditure by using stock

returns and disaggregated earnings data. They reveal that the expected benefits from

R&D expenditure are long-lasting. Lev and Sougiannis (1996) relate company‟s

62

operating profits to the time series of their yearly R&D expenditure up to 10 years back

and suggest that the general lifetime of R&D projects range from five to nine years.

According to Lev and Sougiannis (1996), the shortest life of R&D projects is found in

scientific instruments industry while the longest life is in chemical and pharmaceuticals

companies. This is consistent with Nadiri and Prucha‟s (1996) estimate of the useful life

of R&D projects that vary between seven and nine years. Nevertheless, Erickson and

Jacobson (1992) find that R&D expenditure do not have any substantial impact on

accounting and stock market returns in their US sample covering the years 1972 to 1986.

They attribute this to minimal barriers to imitation of R&D innovation to generate long-

term benefit from these projects.

2.3.1.2 R&D Expenditure and Risk

Investment in R&D activities generally bears greater risk and requires long-term

obligations as it can take several years before a particular innovation is successfully

commercialized. Firms may have to sacrifice short-term profits to gain in the long run

(Shadab, 2008). Chambers et al. (2002) and Berk, Green and Naik (2004) suggest that

risks are markedly larger for R&D-intensive firms. This is mainly due to the technical

uncertainty arising from R&D projects and risk associated with firms‟ cash flows after

development is completed (Berk et al., 2004). Ho, Xu and Yap (2004) also find a

positive association between R&D intensity and systematic risk in the stock market

especially for firms which are more profitable. This also applies to large firms and firms

63

that make intensive R&D investments. Inherent business and operating risks lead to

higher systematic risk. Nevertheless, McAlister et al. (2007) examine 644 public-listed

firms between 1979 and 2001 and discover that R&D investment reduces firms‟

systematic risks. They suggest that R&D expenditure generates strategic differentiation,

efficiency and flexibility, thus protecting firms from market downturn, thereby lowering

their systematic risk.

Numerous studies demonstrate that risk associated with R&D investment may outweigh

their potential benefits. For example, Kothari et al. (2002) examine contribution of R&D

investment to future earnings variability using a large sample of 50,000 firm-year

observations for the years 1972 to 1992 and learn that R&D investment produces more

doubtful future benefits compared to capital expenditure. Shi (2003) discovers that firms‟

R&D intensity is inversely associated with bond rating and premiums at issuance, thus

affecting bondholders negatively. Conversely, Eberhart, Maxwell and Siddique (2008)

evaluate using a different measure of R&D intensity, that is, dividing R&D expenditure

by total assets or total sales, compared to “R&D expenditure scaled by market value of

equity” applied by Shi (2003). They notice that R&D investment is beneficial to

bondholders as increase in R&D investment lowers the firm‟s default risk.

64

2.3.1.3 Determinants of R&D Expenditure

The following item briefly summarizes past research findings on the determinants of

firm-level R&D expenditure.

a) Firm size

Firm size is shown to be related postively to R&D spending (Rothwell, 1984). Large

firms are more willing than small firms to engage in R&D activities especially those

from the concentrated industries as they are better insulated from competition and are

able to internalise the benefits of R&D investment (Kamien & Schwartz, 1982). Larger

firms possess the required resources to enable them to deal with higher risk (McEachern

& Romeo, 1978), thus they are able to enjoy the greatest economies of scale in R&D

activities (Baysinger & Hoskisson, 1989). However, low R&D expenditure is also found

in large firms or firms with substantial market shares as their managers may be less

motivated to invest in innovations (Graves, 1988; Hansen & Hill, 1991).

Firm size is also related to R&D productivity. In a seminar study, Schumpeter (1942)

concludes that larger firms tend to be better off in implementing and exploiting R&D

efforts. Chauvin and Hirschey (1993) investigate the effectiveness of R&D expenditure

across firm size and industry group using data for the years 1988 to 1990. They

demonstrate that the market value effect of a dollar in R&D expenditure tends to be

stronger for larger firms, compared to smaller firms, in both manufacturing and non-

manufacturing sectors. Subsequent research provides evidence that R&D efforts of

65

larger firms are more productive. Cohen and Kleppler (1996) suggest that the advantage

of averaging the R&D costs over their higher level of output allow big firms to benefit

more from such an activity. Henderson and Cockburn (1996) explain that larger firms

are more capable in realizing economies of scale (arising from specialization and

sharing of fixed costs) and economies of scope by sustaining diverse portfolios of

research projects. Larger firms are also able to capture knowledge spillover13

internally

and externally within firms. Connolly and Hirschey (2005) extend the studies to firms

for the years 1997 to 2001 and found consistent results: the effect of R&D investment

on Tobin‟s Q is stronger for larger firm size and for manufacturing firms.

Ciftci and Cready (2011) analyse whether firm size plays a fundamental role in R&D-

related earnings performance and earnings volatility. Consistent with larger-firm

advantage in R&D productivity, the positive relationship between R&D intensity and

realisation of future earnings is stronger when firm size increases. In terms of earnings

volatility, larger firms are able to diversify the risk of R&D investment, thus

experiencing lower future earnings variability.

13 Jaffe (1986) explore the concept of knowledge spillover and show that R&D activities of neighbouring

firms have considerable impact to firms‟ own R&D success.

66

b) Internal finance

Kamien and Schwartz (1978) suggest that internal finance is the most leading

determinant of industrial R&D projects, consistent with Schumpeter‟s (1942) view on

the potential importance of internal finance for innovation. Based on a panel study of

179 small firms14

in high technology industries, Himmelberg and Petersen (1994) find

that internal finance determines R&D investment expenditure and they attribute the need

of internal finance to the existence of information asymmetries between firms and

providers of external financing. R&D investment is featured as high information

asymmetry because it is a poorly disclosed productive input, thus affecting outsiders in

making appropriate evalution of a firm‟s R&D performance (Aboody & Lev, 2000).

According to Grabowski (1968), funds available in a firm improve its ability to invest in

R&D projects. He opines that managers are generally unwilling to rely on external

funding to finance their investment as it involves higher risks and transaction costs. In

addition, the riskiness of R&D activities also discourages the use of borrowing to

finance R&D investment (McEachern & Romeo, 1978). Thus, internal finance is

directly related to R&D expenditure.

14 Large firms have better access to external resources and generate cash flows in excess of investment

needs. Thus, small firms are examined in the study of Himmelberg and Petersen (1994).

67

c) Financial leverage

Financing-related activities of firms are closely related to financial leverage. Some high-

leveraged firms have to preserve their current cash flow for debt services, thus are

discouraged from investing in R&D initiatives (Barker & Mueller, 2002). In addition,

high debt implies creditors‟ control over strategy formulation where financial

institutions are more risk-averse than shareholders. As such, Baysinger and Hoskisson

(1989) suggest that to the extent that a firm‟s debt position reflects the influence of

financial institutions on decision making, debt is negatively related to R&D expenditure.

Ho et al. (2006) find firm size advantage of R&D investment on firms‟ growth

opportunities disappears as the financial leverage level rises. In particular, they discover

that highly-leveraged small firms enjoy the greatest growth opportunities, suggesting the

need to consider factors of firm size and financial leverage together in evaluating the

value relevance of R&D expenditure.

d) Industry

Previous research has shown that the level of firms‟ R&D investment is dependent on

the industry membership of the firms (Scherer, 1984). Industries vary in the extent of

their basic scientific knowledge in the area of their business activities (Baysinger &

Hoskisson, 1989). According to Connolly and Hirschey (2005), R&D intensity is more

likely to be higher in manufacturing industries. They show that the average R&D

intensity of the manufacturing sector is almost 10 times than that of non-manufacturing

firms. Their sample consists of 15,709 firm-year observations across 80 countries for the

68

years 1997 to 2001. R&D spending during the years 1988 to 1990 is found by Chauvin

and Hirschey (1993) to be the highest, particularly in industries such as industrial

machinery, computer equipment, measuring equipment, photography, electronic

equipment, as well as chemical and allied products. However, lesser R&D projects are

found in service industries, as well as financial and retail sectors. The sectoral

differences among industries reflect the varying technological opportunities available,

that is, to what extent market accepts product innovations (Baysinger & Hoskisson,

1989; Connolly & Hirschey, 2005). Lev (1999) in his trend analysis of US R&D

expenditure from the 1970s to the late 1990s show that R&D investment has increased

smoothly in emerging technologies such as biotechnology, computers and

telecommunications due to constantly growing opportunities.

e) Geographic location

Audretsch and Feldman (1996) change the usual product dimension to a geographic

location. They find geographical concentration of innovative activities and output are

linked to R&D-intensive industries. This is mainly attributable to the existence and

effects of knowledge spillover of R&D investments (Jaffe, 1986; Griliches, 1998). The

spillover tends to be geographically localized, thus new technological knowledge can

flow easier locally than over distant places. Technological spillover to the research

community plays an essential role in creating more knowledge, thereby increasing

returns and ultimately spurring economic growth (Grossman & Helpman, 1991, pp. 17-

18).

69

f) Industrial clusters

Baptista and Swann (1998) reveal that the likelihood for innovation is higher for firms

situated in strong industrial clusters as opposed to firms situated outside these clusters.

Porter (1990) indicates that clustering facilitate information flow and interchanging

between rival firms, thereby fostering innovation, entry of new firms to the market as

well as growth. This is consistent with the concept of knowledge spillover of R&D

investment (Jaffe, 1986; Griliches, 1998) whereby greater knowledge spillover is

generated leading to higher innovative output.

g) Diversification strategy

Diversification strategy can affect R&D investment decisions in large mutiproduct firms.

Baysinger and Hoskisson (1989) show that the degree of diversification has a significant

negative relationship with R&D intensity in diversified firms. R&D intensity in

dominant-business firms (i.e. firms engaging in limited diversification) was found to be

significantly greater than highly-diversified firms. Baysinger and Hoskisson (1989)

correlate these findings to firms‟ internal control systems and managers‟ willingness to

invest in R&D projects. Highly diversified firms apply a system of strict financial

control (Gupta, 1987), hence their managers are more likely to avoid risky R&D

investment to meet short-term financial performance goals. By contrast, firms

implementing dominant-business diversification (limited diversification) emphasize on

both financial and strategic controls. They use subjective data in performance evaluation

70

(Gupta, 1987), hence fostering risk-taking behaviour that leads to higher R&D

expenditure.

h) Corporate governance

R&D expenditure is discretionary in nature, i.e., controllable and determinable by

managers. Fama and Jensen (1983) suggest that firms with high R&D investment suffer

from severe agency problems. According to them, agency costs arise when managers

possess power over firms‟ resources and pursue opportunistic activities to expropriate

wealth from firms and shareholders for their own gains. High investments in R&D

projects are generally perceived as a high risk venture but a high return strategy that

could likely provide future benefits to shareholders. Managers, however, are not willing

to invest in long-term R&D projects as they do not generate short-term returns

(Baysinger, Kosnik & Turk, 1991) and are risky in nature (Haynes & Hillman, 2010).

Stock market participants, especially institutional investors, emphasize greatly on

current year results and the short time horizon, therefore, prohibit firms from making

necessary R&D investments for long-term growth (Erickson & Jacobson, 1992). When a

company‟s control and evaluation system places emphasis on financial performance

criteria such as return on investment, managers will avoid risky projects and sacrifice

long-term innovation projects to achieve immediate financial performance goals

(Baysinger & Hoskisson, 1989). Consequently, conflict of interest exists between

managers and shareholders in making R&D investment decisions (Jensen & Meckling,

71

1976). There is higher propensity for managers to uphold their own interests when there

is a lack of proper governance controls (Fama & Jensen, 1983).

Agency theory stresses the role of independent board of directors in monitoring

managerial actions. Past research shows that corporate governance influences

management‟s discretion in either investing or avoiding risky R&D projects through

monitoring via board composition and ownership structure. For example, Baysinger et al.

(1991) report that high insider representation on board of directors jointly with a

concentration of institutional investors is positively associated with corporate R&D

expenditure. The positive association between management representation in board of

directors and R&D expenditure is consistent with the results reported by Hill and Snell

(1988). According to Hill and Snell (1988), more insiders are included in the board of an

R&D firm for the incorporation of firm‟s functional activities around its R&D strategy.

Baysinger and Hoskisson (1990) relate the control emphasis of the board of directors,

that is, either applying strategic control or financial control on R&D expenditure. Firms

that place emphasis on strategic control focus on long-term performance, promote long-

term risk taking and reward activities resulting in innovation (Shadab, 2008). Managers

in R&D-intensive firms have to bear higher risk of technological ambiguity and

obsolescence whereas shareholders can diversify their risk by holding a diversified

portfolio of stocks. Thus, there is a higher likelihood for managers who are assessed by

strategic control to value more towards uncertain cash flows arising from risky R&D

activities. Baysinger et al. (1991) suggest that managers are more like to support risky

72

R&D initiatives if they are well-represented in the board of directors to make decisions.

Baysinger and Hoskisson (1990) argue that boards dominated by outsiders are related to

a lower R&D investment if they emphasize more on financial controls.

In addition, Baysinger et al. (1991) provide empirical evidence on the positive

relationship between equity concentration among institutional investors and R&D

expenditure, contrary to the myth that institutional investors are short-term oriented.

Institutional investors, adopting high risk-high return strategy, value R&D investment

positively and are able to diversify their R&D risk better than individual investors by

holding diversified investment portfolios. Hansen and Hill (1991) found consistent

results and suggest that individual investors are more risk-averse and they opt for lower-

risk strategies.

i) Top management team (TMT)

Chief Executive Officer (CEO) attributes are found to predict firms‟ R&D expenditure.

For example, Barker and Mueller (2002) show that higher R&D expenditure is observed

in firms where CEOs are younger and invest more in firms‟ shares. Their CEOs are also

more likely to be educated in advanced science-related areas and are more experienced

in marketing or engineering fields.

Barker and Mueller (2002) find that R&D expenditure increases with longer CEO tenure,

signifying that CEO personalities shape strategic decisions to match their own choices.

73

This is contrary to the studies which find that CEOs tend to make fewer strategic

changes as their years of service increases (Grimm & Smith, 1991; Hambrick &

Fukutomi, 1991). Retiring CEOs may emphasize stability and efficiency and thus, are

unwilling to undertake innovation strategies through increased R&D expenditure. CEOs

also face incentives to restrict R&D expenditure nearing their retirement to improve

short-term performance (Dechow & Sloan, 1991) and to conserve their CEOs‟ pension

entitlements (Sundaram & Yermack, 2007). Conversely, Cazier (2011) find no evidence

of reduced R&D costs during periods close to CEOs‟ retirement by tracing R&D

expenditure made by the same CEOs over a period of time. He explains that extant

evidence regarding R&D curtailment among short-horizon CEOs is caused by empirical

research designs that results in invalid inferences.

Both upper-echelon and resource-based theories focus on the relevance of top

management team‟s (TMT) firm-specific knowledge, collective confidence, and top

management diversity to R&D investment decisions (Finkelstein & Hambrick, 1996;

Penrose, Penrose & Pitelis, 2009). Kor (2006) examines the direct and interaction

effects of TMT and board composition on R&D intensity. His results show that

managers‟ tenure is negatively and non-linearly associated to R&D intensity as long-

tenured managers are more risk-averse and prefer not to commit high level of R&D

expenditure. The presence of founders in the TMT is associated with higher R&D

investment. Managers‟ shared team-specific experience is also positively correlated with

R&D investment as the trust and common understanding among TMT members allows

74

firms to make risky investments. Kor (2006) also exemplifies that conflicts between

TMT members and boards can undermine the effectiveness of board monitoring, thereby

resulting in decreased R&D expenditure.

2.3.2 Capital Expenditure

The second type of corporate expenditure that is examined in the study is capital

expenditure. It is an essential component of a nation‟s aggregate demand and gross

domestic product (GDP), contributing to world economic growth (Dornbusch & Fischer,

1992, p. 22). The following extant literature on CAPEX is reviewed based on its

significance to firms and market, as well as factors that determine the level of capital

investments.

2.3.2.1 Significance of CAPEX

There is a large volume of literature describing the role of CAPEX. Among others,

McConnell and Muscarella (1985) show that announcements of increased (decreased)

capital investments are generally related to significantly positive (negative) excess stock

returns. This is in line with market value maximisation hypothesis which suggest that

managers seek to maximise firms‟ market value in making CAPEX decisions. Managers

would invest as long as the marginal rate of returns on investment exceeds the market‟s

required rate of return. As such, unexpected increase in CAPEX is accompanied by an

increase in market value of industrial firms, and vice versa.

75

Chung, Wright and Charoenwong (1998) have differing views. They evaluate the

market responses to firms‟ CAPEX decisions from the perspective of firms‟ growth

prospects. They find that the response to the announcements of the quantum of CAPEX

by firms is linked to level of Tobin‟s Q representing investment opportunities. Therefore,

they argue that the market‟s evaluation of the quality of firms‟ investment opportunities

affects the share price reaction to capital investment decisions. Analysing from the angle

of investment opportunities, Chen (2006) suggest that the announcement of capital

investment exerts a positive effect on market reaction in focused firms than in

diversified firms.15

This is because better investment opportunities exist in focused firms

and their managers tend to invest in projects with positive net present values.

CAPEX conveys additional valuable and relevant information about firms‟ future

earnings. When the market is able to predict managers‟ investment decisions from the

existing information, all appropriate information has already been incorporated in the

share prices. Thus, Kerstein and Kim (1995) hypothesize that unexpected increases in

CAPEX would only be value-relevant when managers react to private information about

firms‟ future demand or cost structure and make proper investment decisions. The

additional capital investments provide positive signals to the market about prospects of

projects undertaken whereas the decrease in CAPEX indicates that the reverse is true.

The empirical results highlights a significant positive relationship between changes in

15 In Chen (2006), focused firms are firms with a single segment of business while diversified firms are

firms with multiple business segments.

76

CAPEX and excess returns, after controlling for concurrent earnings information and

pre-disclosure information differences, and thereby providing support to Kerstein and

Kim‟s (1995) view.

An unexpected increase in CAPEX may however, result in an unfavourable economic

value. According to McConnell and Muscarella (1985), size maximisation hypothesis,

contrary to market value maximisation hypothesis, posits that managers tend to over-

invest where marginal returns of investment are below the market‟s required return to

increase firm size. In this instance, any unexpected increase in CAPEX would

negatively affect the market value of firms whereas any unexpected reduction in

CAPEX would increase firms‟ market value. This empire-building implication of

increased capital expenditures arises from managers who invest for their own benefits

rather than to maximise shareholders wealth (Jensen, 1986).

Titman, Wei and Xie (2004) find a significant negative relationship between unexpected

CAPEX and future stock returns. Firms increasing their capital investment recorded

negative benchmark-adjusted returns for five years subsequent to the expenditure. These

observations are interpreted as investors‟ under-reaction to the tendencies of over-

investment or empire building. Furthermore, Titman et al. (2004) discover that the

negative association between CAPEX and stock return is stronger in firms that have

higher cash flows and lower financial leverage. These firms are mostly managed by

individuals who possess higher discretionary power in making investment decisions and

77

thus, have a higher likelihood to over-invest. As such, capital expenditure does not

necessarily improve firm performance especially when the firm has free cash flow

(Jensen, 1986) and when the investment is wasteful (Lang, Stulz & Walkling, 1991;

Chen, 2006).

Capital investment is also found to affect earnings. Inci, Lee and Suh (2009) provide

international evidence from their investigation undertaken by using firms in 40 countries

for the years 1988 to 2004. They highlight the importance of legal system and financial

development in exploring the association between CAPEX and earnings. According to

Inci et al. (2009), the CAPEX-earnings‟ relationship is generally positive for firms in

common law and financially developed countries but is generally negative in firms

domiciled in civil law and financially developing countries. This is similar with the

over-investment problem faced at the firm level. A lack of proper governance systems

and under-developed capital markets, especially in civil law and financially under-

developed countries, could retard generation of positive returns from their capital

investments. Consequently, Inci et al. (2009) recommended that policy makers should

develop the legal environment such as corporate governance, shareholder protection and

monitoring mechanisms to motivate managers to invest in value-increasing capital

projects.

78

2.3.2.2 Determinants of CAPEX

In view of the significance of CAPEX, a considerable amount of literature has been

published on its determinants. The determinants are briefly outlined below.

a) Internal cash flow

Internally generated cash flow is fundamental in determining capital expenditure (Vogt,

1994; Griner & Gordon, 1995; Gordon & Iyengar, 1996). Two hypotheses, namely, the

pecking order hypothesis and free cash flow hypothesis, have been developed to explain

the rationale behind the use of internal cash flow to finance CAPEX. According to the

pecking order hypothesis developed by Myers (1984) and Myers and Majluf (1984),

managers prefer internal cash flow over external financing for the purpose of funding

capital projects to mitigate dilution of current shareholder value. This is mainly caused

by information asymmetry problem (in the form of adverse selection)16

that arises when

firm managers are better informed about the firm value as opposed to new potential

investors. Firm managers may under-invest by turning down profitable projects to avoid

issuing new shares. Availability of internal cash flow minimises external financing cost

and determines the level of capital expenditure. Fazzari, Hubbard and Petersen (1988)

confirm the significance of cash flow to liquidity-constrained firms as sensitivity of

capital investment-to-cash flow is stronger for these companies. Griner and Gordon

16 More discussion on adverse selection is deliberated in item 3.3.

79

(1995) and Carpenter and Guariglia (2008) show consistent results and interpret it as

evidence of imperfect information linked to external financing.

The alternative hypothesis used to explain why internal cash flow determines CAPEX

level is the free cash flow hypothesis. Jensen (1986) contends that instead of paying out

free cash flow in the form of dividends and debt-financed share repurchase, managers

choose to over-invest firms‟ free cash flow wastefully on value-decreasing projects so as

to increase their personal wealth. Therefore, agency problem plays an important role in

the relationship between internal cash flow and capital expenditure level, as evidenced

by Strong and Meyer (1990) and Devereux and Schiantarelli (1990). However, Griner

and Gordon (1995) find that the cash flow-CAPEX relationship is not due to conflict of

interest between managers and shareholders.

Vogt (1994) examines whether pecking order model or free cash flow hypothesis prevail

in the relationship between internal cash flow and capital investment. He suggests that

pecking order behaviour is mostly found in small firms while the free cash flow

hypothesis is portrayed in large firms. Both types of firms choose to make low dividend

payout to preserve their cash flows.

Vogt (1997) provides two additional evidence in explaining the role of internal cash

flow on CAPEX by focusing on excess returns around firms‟ CAPEX announcements.

Firstly, he finds a significant positive relationship between internal cash flow and

80

announced CAPEX, consistent with findings by McConnell and Muscarella (1985).

Secondly, it is observed that both small firms and firms with concentrated insider

ownership record significant positive excess returns around CAPEX announcements.

The excess returns increase as the abilities to finance CAPEX by internal cash flow rise.

This is consistent with findings by Vogt (1994) that small firms are following the

behaviour portrayed by the pecking order hypothesis (Myers, 1984; Myers & Majluf,

1984).

Inci et al. (2009) also show a positive association between earnings (representing

internal generated cash flows) and CAPEX in nearly all 40 countries surveyed,

regardless of the form of legal system and extent of financial development in these

countries. A similar relationship between earnings and CAPEX is also prevalent in

financially developed markets as these markets have easy access to external financing.

This indicates that internal financing is crucial for capital investments and thus is

consistent with the pecking order hypothesis. However, free cash flow behaviour is

identified in firms with low insider ownership indicating higher levels of agency costs.

As such, following the pecking order theory and the free cash flow hypothesis, firm size

and insider ownership are expected to indirectly determine capital expenditure.

81

b) Return on investment

According to Gordon and Iyengar (1996), managers consider the incremental effect of a

project on firms‟ average return on investments (ROI) when they make capital

investment decisions. There is a high possibility for managers to reject a profitable

capital project if the investment is expected to reduce a firm‟s overall ROI. Gordon and

Iyengar (1996) observe a positive association between ROI and capital expenditure,

suggesting ROI maximisation is an essential determinant of CAPEX. They also find that

the relationship between ROI and CAPEX remains robust even when ROI is maximised

but not for the best interests of firms‟ shareholders.

c) Sales growth

Griner and Gordon (1995) suggest that sales growth represent investment opportunities

while Gordon and Iyengar (1996) argue that sales growth contributes to company funds

and enables capacity expansion. As such, sales growth is expected to be positively

associated with capital expenditure.

2.3.3 Selling, General and Administrative Costs

The third proxy of corporate expenditure examined in this study is selling, general and

administrative (SGA) costs. These costs form a major proportion of the business

operation expenses (Chen et al., 2012b), therefore representing a fundamental aspect of

firms‟ strategic tools (Finkelstein & Hambrick, 1990). As reported by Banker, Huang

82

and Natarajan (2011b), the average ratio of SGA costs to total assets is 31 per cent while

R&D expenditure is merely three per cent of total assets for their US sample for years

1970 to 2004. SGA costs comprise input resource expenditure in advertising, selling

(including marketing as well as distributing products and services), information

technology, human resources and R&D expenditure, thereby creating intangible assets

and generating long-term value. These costs are required by the International

Accounting Standards (IAS) to be expensed immediately in the current year (Banker et

al., 2011b).

2.3.3.1 Characteristics of SGA Costs

The SGA cost to sales ratio is closely monitored by investors and analysts as any

changes in this ratio provide meaningful signals with regards to firms‟ future

performance (Anderson, Banker, Huang & Janakiraman, 2007). In a conventional

fundamental analysis,17

an increase in the ratio of SGA cost/sales from the previous

period represents operational inefficiency as managers are deemed to be incapable of

controlling costs, thus providing unfavourable signals about firms‟ future profitability. A

17 Fundamental analysis is a diagnostic procedure used to analyse financial statement items to assess firm

performance and forecast future earnings. Lev and Thiagarajan (1993) examined 12 fundamental signals

that provide incremental information. This involves interpretations of financial ratios and their changes in

the area of inventory, accounts receivables, CAPEX, R&D expenditure, SGA costs, gross margin,

provision of doubtful debts, taxation, order backlog, labour force, last-in-first-out (LIFO) earnings and

audit qualifications.

83

decreasing SGA cost/sales ratio, on the other hand, indicates managerial ability to

contain costs and consequently are linked to future increases in firms‟ value (Lev &

Thiagarajan, 1993). In determining the value relevance of SGA costs, Lev and

Thiagarajan (1993) reveal a significant negative relationship between an increasing SGA

cost/sales ratio and contemporaneous excess stock return. Abarbanell and Bushee (1997)

investigate whether the changes in fundamental ratios are associated with the changes in

firms‟ future earnings. They do not find any significant association between changes in

SGA costs/sales ratio and changes in the subsequent year‟s earnings but instead observe

significant negative associations between these two variables when firms‟ earnings were

worsened from the previous year and also during period of low GDP at the

macroeconomics level. This finding provides some support for the idea that an increase

in SGA costs/sales ratio conveys negative signals about future earnings change, at least

when sales and earnings are deteriorating.

The implicit assumption used in the fundamental analysis is that costs (SGA costs)

change proportionately with activity levels (sales) (Noreen, 1991). Traditional models

view costs as fixed or variable and the magnitude of cost change is not subject to the

direction of the changes in activity levels (i.e. when sales increase or decrease). Studies

have been carried out to investigate the validity of the proportionality assumption

(Noreen & Soderstrom, 1994; Balakrishnan & Soderstrom, 2000). Noreen and

Soderstrom (1997) examine hospital overhead costs and discover that it is more accurate

to predict costs by assuming all costs are fixed instead of being under the conjecture that

84

they are variable and vary proportionately to activities. They also find evidence that

costs are more inclined to adjust when there is an increase in activities, as opposed to a

decrease in the cost drivers. This is consistent with Cooper and Kaplan (1988) who

observe that managers more readily raise costs when activities intensify than lower costs

when the activity levels decline.

The asymmetric behaviour of cost is termed as “sticky” in the seminal paper by

Anderson et al. (2003). “Sticky” costs elevate more rapidly as the volume of activities

increase than when they decline as volume of activities plunge with an equivalent

amount. Anderson et al. (2003) examine the “sticky” cost behaviour by associating

movements in SGA costs to contemporaneous changes in net sales during revenue-

increasing and revenue-decreasing periods. SGA expenditure is considered as a

significant variable in their model because many of its components are sales-driven.

Anderson et al. (2003) demonstrate that the directions of the movement in activity levels

do affect the extent of changes in cost: SGA costs rise 0.55 per cent for every one per

cent increase in sales revenue, but drop only 0.35 per cent for every one per cent

decrease in sales revenue. SGA costs are “sticky” because managers are making

asymmetric adjustments in firms‟ committed resources as a result of their expectation of

future demand. Managers intensify necessary resources in response to increased demand

and incur additional SGA expenditure but when demand falls, managers need to assess

its nature. If the deteriorating demand is more likely to be permanent, managers need to

eliminate committed resources quickly and to substitute such resources only when the

85

sales are reinstated subsequently. This involves adjustment costs such as firing costs for

labour (i.e. severance pay to retrenched employees), re-hiring costs (for example,

searching and training costs for new employees) as well as installing and disposal costs

for equipment. If managers anticipate the fall in demand as temporary in nature and

expect it to recover soon, they prefer to hold on to the unutilised committed resources

and incur operation costs, thus “stickiness” of SGA costs occurs. Consequently, the

costs are inclined to “stick” and do not reduce proportionately even when activity level

drops (Weiss, 2010).

The findings of asymmetric behaviour of SGA costs by Anderson et al. (2003)

motivated researchers to undertake studies to validate their results. Balakrishnan,

Petersen and Soderstrom (2004) examine therapists‟ salaries in the US and found that

the level of capacity utilisation in therapy clinics affects managers‟ reaction to changes

in activity levels, thereby determining the extent of cost “stickiness”. Figure 2.2

illustrates three cost functions, namely, “sticky” cost, normal cost and “anti-sticky” cost

under varying capacity utilisation as described by Balakrishnan, Petersen and

Soderstrom (2004).

86

Figure 2.2 Asymmetric Behaviour of “Sticky” Cost

Source: Weiss (2010), pg 1444.

In Figure 2.2, “sticky” cost function is depicted by the bold line when firms are

operating at high capacity at level A while “anti-sticky” cost function is represented by

the dashed line when firms are experiencing idle capacity at level A. The line in between

shows the normal cost function when firms are utilising their normal capacity at level A.

When firms are under high capacity utilisation (strained capacity), managers do not

reduce their committed resources immediately if there is a drop in the activity level so as

to ease pressure on the strained resources. This is also applicable when the declining

demand is viewed as temporary. However, any increase in demand is likely to prompt an

A

Cost

Activity level

"Sticky"

Normal

"Anti-Sticky"

87

increase in resources that are required when firms are already experiencing high

capacity utilisation. Thus, “sticky” cost behaviour will be portrayed because the

response to a drop in activity level is smaller than the reaction to an equivalent increase

in activity level. This phenomenon is seen by the bold line representing the “sticky”

cost function in Figure 2.2.

Conversely, managers tend to use idle resources or “slack” to fulfil the increased

demand if firms are experiencing an excess capacity condition. When there is a drop in

activity level, managers may view it as confirmation of a permanent reduction in

demand, hence react more responsively. This result in “anti-sticky” costs as the cost

reaction to the activity decrease is more than the cost response to activity increase and it

is shown by the dashed line in Figure 2.2. However, when the firms are operating at

normal capacity utilisation, Balakrishnan et al. (2004) found no significant differences

in cost response when there is either an increase or a decrease in activities. Their

findings propose that capacity utilisation determine cost “stickiness”, thereby suggesting

extra care in interpreting the conclusion of Anderson et al. (2003) on “sticky” costs.

Banker, Byzalov, Ciftci and Mashruwala (2012) propose that managerial expectations

arising from prior sales changes play a critical role in deciding the structure of cost

asymmetry, i.e., “stickiness” and “anti-stickiness”. After a prior sales increase

(decrease), managers are optimistic (pessimistic) on future demand and more (less)

likely to increase their investments in resources when current sales increase and have a

88

lower (higher) tendency to remove committed resources when current sales decrease. As

a result, cost “stickiness” is noted after prior sales improvement whereas “anti-stickiness”

is demonstrated after a drop in prior sales.

Other studies investigate the different aspects of asymmetric behaviour of SGA costs,

for example, Balakrishnan and Gruca (2008) identify that cost “stickiness” is greater in

core activities of organizations compared to their auxiliary or supportive services. This

is due to the vital nature of core activities associated to the firms‟ missions and thus,

greater adjustment costs are involved when needs arise to change firms‟ capacities.

Dalla Via and Perego (Forthcoming) examine cost “stickiness” of Italian small and

medium-sized firms but find no evidence of asymmetric cost behaviour in their SGA

expenditure. Weidenmier and Subramaniam (2003) investigate the magnitude of

revenue change and discover that there is no “sticky” cost behaviour in SGA

expenditure when there is little changes in revenue. However, when the revenue change

is more than 10 per cent, SGA costs will be “sticky”. They have also found varying

degrees of SGA costs “stickiness” among different industries due to firms‟ inventory

and fixed assets holdings as well as employee intensity.

Cost “stickiness” also behaves differently across countries. Calleja, Steliaros and

Thomas (2006) reveal that operating costs18

of firms located in France and Germany are

18 Operating costs are made up of cost of goods sold and SGA costs.

89

more likely to be “sticky” than those of the US and UK firms. This is explained by the

different systems of corporate governance as well as varying degrees of pressure from

the market for corporate control. On the other hand, Banker and Chen (2006b) analyse

asymmetric cost response from a macroeconomic perspective using 19 countries who

are members of the Organization for Economic Co-operation and Development (OECD)

for the years 1996 to 2005. They found that the level of cost “stickiness” among these

countries is determined by labour market characteristics, for instance, collective

bargaining of labour agreements, level of unemployment benefits and the stringency of

employee protection law. Banker, Byzalov and Chen (2013) confirm that stricter

employment protection legislation gives rise to a higher degree of firm-level cost

“stickiness”. They attribute this scenario to the “economic theory of sticky cost”, i.e., the

presence of adjustment costs in managerial decisions on resource commitment.

Cost “stickiness” may also arise due to agency problem (Anderson et al., 2003).

Managers with empire building instincts have a greater propensity to build their firms

beyond theirs optimal levels or maintain “slack” for their own personal interests (Jensen,

1986). According to Chen et al. (2012b), these managers are more likely to boost SGA

expenditure in the form of office payroll, sales commissions, travel, entertainment,

office rental and office expenses when sales grow but prefer to delay the removal of

such costs when demand drops, resulting in cost asymmetry behaviour. Furthermore,

managers‟ reluctance to downsize adds to the degree of SGA costs “stickiness”. Indeed,

Chen et al. (2012b) document a positive connection between agency problem and the

90

asymmetry degree of SGA costs, but the positive impact is mitigated by strong corporate

governance. Conversely, Kama and Weiss (2013) discover that self-interested managers

who are motivated to meet earnings target or wanting to achieve financial analysts‟

earnings forecast will accelerate the removal of committed resources when sales drop.

This is so even if the sales demand is expected to be recovered soon. As such, Kama and

Weiss (2013) find that deliberate managerial action in adjusting corporate resources has

reduced, rather than increased, the extent of cost “stickiness”.

The asymmetric cost behaviour affects the accuracy of analysts‟ earnings forecast and

analyst coverage. Weiss (2010) observes that firms with more explicit cost “stickiness”

exhibit higher volatility in future earnings, thus lessening the accuracy of analysts‟

earnings forecast. This is in line with Banker and Chen (2006a) who suggest the

significance of incorporating “sticky” cost behaviour in predicting firms‟ future

profitability. Weiss (2010) also confirms that firms with “stickier” cost attract lesser

analyst following, nonetheless, investors seem to take into consideration the asymmetric

cost behaviour when they evaluate these firms.

Banker, Basu, Byzalov and Chen (2012) discover that both cost “stickiness” (asymmetry

cost response) and conservatism (asymmetric earnings response to news) (Basu, 1997)

jointly explain asymmetric relation between earnings and stock returns, i.e., the

association between earnings and stock returns is stronger for negative returns rather

than for positive returns.

91

Recent studies, for example, Anderson and Lanen (2009) and Balakrishnan, Labro and

Soderstrom (2011) disagree with the “sticky” cost behaviour from previous studies as

they found there is no asymmetric cost response. Nevertheless, Banker, Byzalov and

Plehn-Dujowich (2011a) argue that the shortcomings in the assumptions and

econometric analyses presented in the studies by both Anderson and Lanen (2009) and

Balakrishnan et al. (2011) have rendered their viewpoints unjustifiable.

2.3.3.2 Value Relevance of SGA Costs

Due to the nature of “stickiness” of SGA costs, Anderson et al. (2007) question the

validity of interpreting a rising SGA costs/sales ratio in the fundamental analysis as an

inefficient way of doing business particularly when revenue declines. They extended

Abarbanell and Bushee‟s (1997) earnings prediction model but divided firms into

increasing and decreasing sales. Their empirical evidence shows that SGA costs provide

different signals during revenue-increasing and revenue-decreasing periods. An

improvement in SGA costs/sales ratio during declining sales reflects managerial

optimistic expectation of forthcoming recovery of sales demand, resulting in retention of

committed resources. Consequently, a positive relationship is observed between an

increase in SGA costs/sales ratio and future earnings when sales falls whereas a negative

association is found when sales increase.

92

Few empirical studies on information contents suggest that SGA costs exhibit a positive

influence on future profitability. Banker, Huang and Natarajan (2006) justify that SGA

costs (excluding R&D costs and advertising expenditure) create intangible assets in the

form of customer royalty and operating efficiency, thereby depicting a six-year positive

economic benefit on current and future operating income. They also demonstrate that

investors appear to recognise the implicit assets value in SGA expenditure even though

it is required by the IAS to be expensed off immediately. Banker et al. (2011b) proceed

to examine the impact of SGA costs in the executive labour market. They predict that

the future value-creation ability of SGA expenditure encourages managers to increase

such expenditure after receiving long-term compensation. Banker et al. (2011b) find that

an increase in SGA costs after managers received new equity incentive is only depicted

in firms where SGA expenditure creates high future value. Conversely, SGA costs do

not increase in companies where low future values are generated after firm managers

receive their long-term compensation. This is because managers acknowledge the ability

of equity incentive in generating long-term value for firms when their SGA costs create

increased earnings.

To better understand value relevance of SGA costs, Baumgarten, Bonenkamp and

Homburg (2010) argue that it is critical to differentiate whether an increase in SGA

costs/sales ratio arises from management intention to improve profitability or is due to

deficiencies in cost control. An increase in the SGA costs/sales ratio is considered as

intended by management if firms‟ SGA costs/sales ratio is below its industry average,

93

indicating effective cost management and therefore should increase future firm

profitability. By contrast, an increase in the SGA costs/sales ratio is not to be expected

by the management if firms‟ past SGA costs/sales ratio is above the industry average

and thus would not have any impact on firms‟ profit. Baumgarten et al. (2010) find a

positive impact of an increasing SGA costs/sales ratio on firms‟ future profitability only

for SGA costs-efficient companies and conclude that an intentional increase in SGA

costs/sales ratio exerts positive consequences on operational efficiency leading to

escalating profitability.

2.3.3.3 Determinants of SGA Costs

R&D expenditure forms part of SGA costs and thus, factors that determine R&D

expenditure are also determinants of SGA costs. These determinants were outlined in

item 2.3.1.3 and they are firm size, internal finance, financial leverage, industry,

geographic location, industrial clusters, diversification strategy, corporate governance

and the top management team. The following items (a) to (e) outline five additional

determinants of SGA costs which are derived from Banker et al. (2011b) whose study

examine future value creation of SGA costs.

94

a) Property, plant and equipment

Banker et al. (2011b) suggest that firms spending more in tangible assets like property,

plant and equipment will reduce their SGA costs. This reflects the resource constraints

faced by most firms and highlights the importance of efficient capital allocation.

b) Industry concentration

Firms operating in competitive industries are prone to spend more in SGA costs.

Therefore, industry concentration is negatively associated with SGA costs.

c) Growth opportunities

Higher growth opportunities are positively associated with SGA costs as high-growth

firms are able to generate more funds for capacity expansion.

d) Standard deviation of return on assets

Banker et al. (2011b) suggest that firms operating under uncertainty are more likely to

invest more in SGA costs to derive more future value. Firms operating under uncertain

business environment possess high standard deviation of return on assets.

e) Employee intensity

Employee intensity is derived by dividing the number of employees by sales. Firms

hiring a higher number of employees tend to invest more in costs related to human

resources, thus incurring higher SGA expenditure (Chen et al., 2012b).

95

2.4 Chapter Summary

This chapter critically reviews the existing literature on stock price informativeness and

corporate expenditure. It commences with a review of literature on the characteristics,

measurement and significance of stock price informativeness, followed by the empirical

evidence provided by cross-country and firm-level studies in this area. Then, the chapter

evaluates the relevant literature on corporate expenditure comprising R&D expenditure,

CAPEX and SGA costs, particularly on their characteristics, benefits and determinants.

The next chapter (Chapter 3) outlines the theoretical framework of the current research

and develops the hypotheses that are inter-related to stock price informativeness,

corporate expenditure and information asymmetry.

96

CHAPTER 3

THEORETICAL FRAMEWORK AND HYPOTHESES DEVELOPMENT

3.1 Introduction

The preceding chapter (Chapter 2) presents the literature review of two main

components of this study, namely, stock price informativeness and corporate

expenditure. The literature on stock price informativeness includes an in-depth review of

its characteristics, measurement and significance as well as the relevant empirical

evidence provided by cross-country and firm-level research. The previous chapter also

presents the relevant literatures on R&D expenditure, CAPEX and SGA costs,

particularly on the characteristics, benefits and determinants in relation to corporate

expenditure.

This chapter (Chapter 3) presents the theoretical framework and hypotheses

development of the current study. An overview of the empirical literature on the theories

underpinning the current study is provided under the following two captions, namely,

“stock price informativeness and corporate expenditure” and “information asymmetry”

in Sections 3.2 and 3.3 respectively. Accordingly, four hypotheses are formulated to

predict the association between stock price informativeness and corporate expenditure as

well as to examine whether this relationship is dependent on information asymmetry. A

97

research model is then presented in Section 3.4 while Section 3.5 provides the summary

of this chapter.

3.2 Stock Price Informativeness and Corporate Expenditure

This study investigates the association between stock price informativeness and

corporate expenditure. In doing so, this item outlines the learning theory and

subsequently formulates the hypothesis that is to be tested.

Existing corporate finance literature asserts that managers can learn about their firms

from their own stock prices (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999). It

is referred to as “learning hypothesis” or “learning theory” and the idea originates from

Hayek (1945) who opines that when information is conflicting and imperfectly

possessed by different individuals, the price system efficiently combines the dispersed

information and transmits it across diverse market participants. It enables efficient re-

coordination of scarce resources that can benefit the whole economy. In the stock

market, information is produced, aggregated and communicated into market prices

through trading activities of various investors and speculators (Grossman & Stiglitz,

1980; Glosten & Milgrom, 1985; Kyle, 1985). Dow and Gorton (1997) and

Subrahmanyam and Titman (1999) provide the theoretical framework of learning theory.

These authors suggest stock prices may possess some new information that managers do

98

not have and in turn will induce them to deduce this information to improve their

corporate decisions.

Dow and Gorton (1997) developed a stock market model whereby managers have the

discretionary power to make making investments decisions.19

According to them,

managers have some private information about the value of the firms but do not know

all the relevant information to make prudent investment decisions. They explain that the

flow of information in the capital market is bi-directional as the market ascertains the

quality of managers‟ decisions while at the same time, managers learn more about the

market‟s valuation of future investment opportunities of their firms. Information about

firms‟ growth opportunities may flow from stock market into the companies through

stock prices. More specifically, high stock prices may convey information of market‟s

assessment on firms‟ potentials. Firm managers can then improve their decisions by

observing the stock prices, resulting in greater number of investment projects.

Consequently, Dow and Gorton (1997) suggest that, in an equilibrium situation, stock

markets lead corporate investment by communicating essential information to managers.

Moreover, Subrahmanyam and Titman (1999) highlight that information conveyed by

stock prices guide financing decisions. They scrutinize the connection between

informational substance of stock prices and firms‟ options to solicit for either private or

19 The managers, however, must be given the appropriate incentives that are linked to stock prices in their

remuneration contracts to reduce agency costs (Dow & Gorton, 1997).

99

public financing. The researchers highlight that public financing is more beneficial than

private financing in providing managers with information derived from the stock market.

This is because the aggregate information generated across many stock market

participants, albeit its diversity, could provide more meaningful signals, thereby

improving the allocation of scarce resources. Other related models on learning theory

are developed by Dye and Sridhar (2002), Dow and Rahi (2003) and Goldstein and

Guembel (2008).

Dye and Sridhar (2002) show that stock prices combine new private information from

various investors that managers have yet to possess. This is probably caused by a lack of

appropriate direct communication channel between firms and various market

participants outside the trading process or even due to investors‟ reluctance to share

information (Chen et al., 2007; Bakke & Whited, 2010; Frésard, 2012). This new

information, for instance, is about appraisal of future investment or growth prospects;

project‟s appropriate cost of capital; future demand of firms‟ products; firms‟

relationships with various stakeholders such as competitors, employees and suppliers; as

well as future financing opportunities (Chen et al., 2007; Frésard, 2012). The

information, however, is unlikely to be related to the technology used by firms as the

managers are expected to know more about technological issues of their firms (Chen et

al., 2007).

100

Stock prices play the fundamental role of information production (Dow & Gorton, 1997)

and resource allocation (Dye & Sridhar, 2002; Goldstein & Guembel, 2008) as the

feedback generated from stock prices can direct corporate investment. Consequently, the

learning theory implies that the financial market influences real economic activity

(Morck, Shleifer, Vishny, Shapiro & Poterba, 1990).

There is growing empirical evidence supporting the learning theory. For example, Chen

et al. (2007) examine the effect of stock price informativeness on the sensitivity of

investment-to-price. They hypothesize that managers learn new valuable information

from the private information embedded in the stock prices to make corporate decisions.

Generally, firm managers make decisions based on public and private information

revealed by stock prices as well as private information they themselves know but has yet

to be reflected in the stock prices. Chen et al. (2007) argue that corporate investment is

more sensitive to stock prices if the prices reveal information that is new to the

managers for their decision making. Therefore, any piece of information revealed by

stock prices that is known by firm managers will not have any impact on managerial

investment decisions. This information is already factored in managers‟ past investment

decisions and will, in turn, reduce the sensitivity of investment-to-stock prices.

The variable of interest in Chen et al. (2007) is represented by an interaction between a

measure of private information (using either value of 1-R2 or Probability of Informed

101

Trading, PIN measure)20

and Tobin‟s Q. They found a significant positive relationship

between the interaction variable and investment, indicating that firms‟ corporate

investment is more sensitive to stock prices when the informativeness of stock prices is

greater. The positive association displayed also implies that the stock prices reveal new

private information that managers have learnt and used in their corporate decisions,

thereby providing evidence of the learning theory.

Bakke and Whited (2010) examine whether corporate investment decisions are

determined either by private information reflected by stock prices or caused by

mispricing. They use an econometric model to isolate stock price movements that are

pertinent to investment and other irrelevant effects. Bakke and Whited (2010) observed

that investment decisions are not influenced by stock market mispricing. Their results

are consistent with Chen et al. (2007) and it is likely that managers exploit valuable

information learnt from stock prices to make their corporate decisions.

Frésard (2012) expects that the private information feedback effect of stock prices on

firms‟ future strategic issues can improve the information asymmetry environment,21

thereby influencing corporate actions such as corporate cash savings. Adapting the

20 Probability of Informed Trading (PIN) measure was developed by Easley, Kiefer and O'Hara (1996). It

is a measure of private information in stock prices by estimating directly the probability of informed

trading in a stock.

21 This is because investors possess some information that managers may have neglected or be unaware of,

resulting in imperfect information environment (Frésard, 2012).

102

approach of Chen et al. (2007), Frésard (2012) investigates the link between stock price

informativeness and the sensitivity of cash savings-to-stock prices. He reports that cash

savings are more sensitive to stock prices when the prices incorporate greater

information that is new to the managers. This finding provides evidence that corporate

cash savings are affected by managerial learning from the stock market.

Foucault and Frésard (2012) document that the investment-to-price sensitivity of cross-

listed firms in the US is significantly greater than those of non-cross-listed-firms. Cross-

listed firms are those that have listed their equity shares in several stock exchanges

(Foucault & Gehrig, 2008). Foucault and Frésard (2012) analyse 633 foreign firms that

are cross-listed on US exchanges for the years 1989 to 2006 by following Chen et al.‟s

(2007) methodology. Relating learning theory to cross-listing, Foucault and Frésard

(2012) suggest the latter provides additional trading platforms where new private

information can be exploited. Furthermore, informed traders could be more superior

than firm managers in evaluating firms‟ corporate strategy as they are probably better in

gaining access to information on foreign demand of firms‟ products or growth prospects

of foreign operations. Therefore, cross-listing improves the informativeness of stock

prices about the value of firms‟ future cash flows (Foucault & Gehrig, 2008), thus

conveying more private information that is new to managers. Firm managers learn and

extract valuable feedback for their corporate decisions, thus making investment of cross-

listed firms more sensitive to stock prices and more advanced in resource allocation.

103

Other studies also report that corporate decisions are influenced by the information

content of stock prices. Luo (2005) presents that managers learn from observing the

market reaction to conclude the merger and acquisition deals of their firms. He suggests

that market participants, such as financial analysts and institutional investors could be

more superior than the companies involved in the merger and acquisition deal in

evaluating relevant issues. These market participants will then provide appropriate

signals to the firms as to their acceptance or refusal of the merger and acquisition

proposals by taking their positions in the stocks of the companies involved. Luo (2005)

analyses a total of 2,114 merger and acquisition announcements in the 1990s and

discovers that the abnormal return experienced around the announcement date strongly

predicts whether the deals are finally carried out or are cancelled. He attributes the

findings to learning theory and opines that managerial learning frequently takes place

when it is easier to cancel the announced merger and acquisition deal or when the

market is expected to know more than the managers.

Giammarino, Heinkel, Hollifield and Li (2004) investigate a total of about 3,500

seasoned equity offerings in the US, of which 96 per cent are finally completed while

the remaining are withdrawn subsequently. They observe that stock prices movement

after announcement of the equity issue leads to managers‟ decision to either proceed or

withdraw their placement. Consistent with the learning theory, the researchers suggest

that the registration of seasoned equity offerings encourages information collection by

the market on the relevant information that managers do not have. The market will

104

reflect the impact of the new issue on firm value through stock prices and managers

seem to learn from the prices and respond accordingly.

In summary, the learning theory suggests that firm managers learn new valuable private

information by observing their own stock prices. They will then incorporate this

feedback about the fundamentals or prospects of their firms to improve their corporate

investment decisions, leading to increased firm value (Luo, 2005; Chen et al., 2007;

Frésard, 2012). Firms allocate capital more efficiently when their stock prices convey

larger private information (Durnev et al., 2004). Thus, the stock market is a good

predictor of business cycles as it guides investment decisions and managers will

continue to react to stock prices even when the prices fluctuate excessively (Fischer &

Merton, 1984). Nonetheless, empirical research does not seem to provide any direct

guidance on the relationship between the level of idiosyncratic volatility and corporate

expenditure.

It is predicted that when firms‟ stock price informativeness is at a low level, i.e., when

firms‟ stock prices co-move more extensively with the market and industry, firm

managers learn this feedback from the stock prices. It is argued that firm managers are

likely to actively respond by increasing their corporate expenditure level. All these

corporate expenditures are shown in prior studies to contribute to firm performance. For

example, R&D expenditure creates knowledge assets and contributes to firms‟

productivity and long-term shareholders‟ wealth, CAPEX contributes to firms‟

105

profitability while SGA expenditure is a type of input resource expenditure that creates

intangible assets and generates long-term firm value. Managers aim to provide positive

signals to market participants as well as to convey additional valuable and relevant

information about firms‟ future earnings, thereby boosting investors‟ confidence level

with regards to firms‟ future performance.

When firms level stock price informativeness is at a high level, firms‟ stock prices track

closely to their fundamental values, exhibiting high efficiency of resource allocation in

these firms (Durnev et al., 2003). Market participants are better informed of firms‟

future cash flows and growth opportunities from the current stock prices (Durnev et al.,

2003; Jiang et al., 2009). In addition, high stock price informativeness is also associated

with better management decisions (Chen et al., 2007; Frésard, 2012). Firms‟ future

performance is perceived positively by the capital market and thus, it may not be

necessary for managers to maintain a high level of corporate expenditure. It is argued

that managers extract valuable information from the stock market and are likely to react

by maintaining a lower level of corporate expenditure. Consequently, a negative

relationship between stock price informativeness and corporate expenditure is predicted.

How quickly managers can adjust to the corporate expenditure level is a matter of

interest. There might be a delay of stocks price reactions as managers take time to gather

private information from the capital markets and then initiate relevant changes to

corporate expenditure. Formal procedures such as board approval might also be required

106

to effect any variation in R&D expenditure, CAPEX and SGA costs. Merton (1987)

explains that the assumption of immediate dissemination of all available information

and instant investors‟ reactions of Efficient Market Hypothesis (Fama, 1970) do not

always hold. There is also increasing evidence that the capital market is not

informationally efficient and stock prices might take years before they fully reflect

available information (Kothari, 2001). Therefore, a lead-lag approach is likely to be

more appropriate in this study by linking firms‟ current year stock price informativeness

to their subsequent year‟s corporate expenditure level.

Consequently, this study examines the following hypothesis in an alternative form:

H1: The stock price informativeness of a current year is negatively associated with

corporate expenditure in the subsequent year, ceteris paribus.

3.3 The Role of Information Asymmetry

This study further examines how information asymmetry plays a role in the relationship

between stock price informativeness and corporate expenditure. Information asymmetry

theory is applied to analyse the impact of imperfect information environment on the

relationship between stock price informativeness and corporate expenditure. The

development of three hypotheses is then outlined in detail for each of the proxies of

107

information asymmetry adopted in this study, namely, firm size, analyst following and

bid-ask spreads.

The issue of information asymmetry is central in the finance theory and has attracted

much attention from the capital markets and regulators (Armstrong, Balakrishnan &

Cohen, 2012). Information asymmetry occurs when one party enjoys “informational

advantage” over others (Brennan & Tamarowski, 2000). This market friction hinders

efficient allocation of resources in the capital markets and is primarily caused by

unequal dissemination of information between different parties. Information asymmetry

introduces two types of problems, namely, adverse selection and moral hazard. These

problems are also known as “hidden information” and “hidden action” respectively

(Arrow, 1985).

The first problem of information asymmetry, adverse selection or “hidden information”

occurs when the contracting parties are assumed to possess heterogeneous information.

One party is more informed than the other party, in other words, possess some private

information about the transaction (Armstrong et al., 2010). The extent of informational

awareness between parties brings about inability to distinguish between good and bad

quality of products or services. This is known as “lemons” problem as illustrated by

Akerlof (1970) using the automobile market as an example. He explains that

information asymmetry exists, for example, when car sellers know more about the

quality of their used cars than the potential buyers. The prospective buyers are only

108

willing to pay a certain price regardless of the quality of the used cars as it is difficult for

them to identify cars that are of better quality versus lemons (bad quality). The sellers,

on the other hand, are unable to acquire the true value of these cars and are therefore

reluctant to trade their used cars or may even withdraw totally from the market. Akerlof

(1970) concludes that the adverse selection may have downgraded the average quality of

goods and services.

The second problem of information asymmetry, namely, moral hazard or “hidden action”

arises when individual actions cannot be effectively observed during execution of

contracts although both parties are assumed to have homogeneous information at the

inception of the contracts (Hölmstrom, 1979; Armstrong et al., 2010). “Hidden action”

applies an ex-post concept22

as private information pertaining to the unobservable

behaviour of one party may develop during the execution of a contract and this

unobservable action could be detrimental to the other party‟s interest (Douma &

Schreuder, 2008, p. 73). The problem of moral hazard is prevalent in the insurance

business and among employment contracts (Pauly, 1974; Hölmstrom, 1979). Arrow

(1963) relates moral hazard to the incentive effect of insurance on insured‟s behaviour.

The insured tend to behave incautiously, or may even act with bad intention. As it is

impossible for the insurance companies to observe the real cause of the claim, i.e.,

22 While moral hazard or “hidden action” is an ex-post scenario, adverse selection or “hidden information”

relates to an ex-ante situation as private information exists before transactions took place.

109

whether it is due to negligence or fraud, the insurance coverage will be discontinued

when the insurance claims multiply.

After analysing the adverse selection and moral hazard problems highlighted by Arrow

(1985), one can conclude that information asymmetry plays a fundamental role

especially in an agency relationship. According to Jensen and Meckling (1976), agency

relationship is a contract where the principals appoint agents to carry out some duties on

their behalf and this involves delegation of authority in making decisions. Thus, in terms

of the firm-specific information hierarchy, managers (agents) are on average viewed as

the most informed while shareholders (principals) are the least informed (Armstrong et

al., 2010). Healy and Palepu (2001) suggested a situation where there is a mixture of

good and bad (lemon) business proposals. Both investors and managers are rational and

they evaluate these business ideas based on their own information. In the event investors

are unable to discriminate between two types of business proposals, these proposals will

be valued on an average basis. Therefore, the capital markets will inaccurately value

those business plans if adverse selection problem prevails.

Moral hazard problems (hidden action) can also take place as a result of information

asymmetry existing between principals and agents. The principals (shareholders) are not

able to observe the agents‟ (managers) behaviour, or obtain perfect information about

the agents‟ actions (Armstrong et al., 2010). Firm managers possess private information

about firms‟ prospective earnings stream that current and potential shareholders do not

110

have. Richardson (2000) suggests that information asymmetry allows managers to

engage in earnings management (hidden action) for empire building and deriving

perquisites while Verrecchia (2001) observes that self-interested managers withhold

information when firms are performing badly.

Furthermore, information asymmetry occurs among investors, that is, between informed

and uninformed investors. This creates adverse selection problem as the informed

investors trade on private information they possess about the true value of the firms

while the uninformed investors rely more on public information available (Brown &

Hillegeist, 2007). In addition, individual investors may not have the time, capacity or

access to collect and process information on firms and managers (Levine, 1997;

Richardson, 2000). Thus, investors will “price-protect” themselves against potential

losses from trading with informed investors by demanding a discount to purchase the

firm shares (Myers & Majluf, 1984; Merton, 1987). This price-protection is reflected in

reduced market liquidity (Glosten & Milgrom, 1985; Welker, 1995). Firms will then

have to issue shares at a discount to motivate potential shareholders to buy firms‟ shares

in illiquid markets, resulting in lower share issuance proceeds and higher cost of capital

(Bhattacharya & Spiegel, 1991; Leuz & Verrecchia, 2000).

Consequently, it is important to alleviate information asymmetry to strengthen the

efficiency and transparency of the capital markets. Better information about firms would

enhance capital market liquidity to facilitate long-term high-return corporate

111

investments (Bushman & Smith, 2001) and reallocate resources to their most efficient

uses leading to long-run economic growth (Merton, 1987). This can be achieved by

timely disclosures of value-relevant information by firms and information collection by

investors (Frankel & Li, 2004). Monitoring can also be carried out to solve moral hazard

problem (Hölmstrom, 1979). An example is to design an efficient compensation contract

where the principal (board of directors or shareholders) identifies observable

performance measures and provides incentives to the agent (CEO) to be accountable and

takes the desired actions (Armstrong et al., 2010) such as voluntary disclosures (Healy

& Palepu, 2001).

Financial reporting has been recognized as a tool to mitigate information asymmetry

problem (Healy & Palepu, 2001). Firms‟ commitment to timely voluntary disclosure of

high-quality accounting information serves to lessen the potential losses from trading

with better informed investors (Bushman & Smith, 2001), to mitigate mispricing

dilemma (Healy & Palepu, 2001) and to reduce cost of capital (Verrecchia, 2001).

Mandatory disclosure of private information as required by regulation can also provide

shareholders and outside directors with relevant and reliable information and for

monitoring purpose. Information intermediaries like financial analysts and rating

agencies also contribute to the gathering and production of private information about

firms and aid in detecting managerial misbehaviour (monitoring role) (Healy & Palepu,

2001).

112

The economics, accounting and finance literature introduces various proxies for the

information asymmetry. The proxies frequently used and adopted in this study are firm

size, analyst following and bid-ask spreads.

3.3.1 Firm Size

Prior research suggests that firm size is a proxy for the extent of information available

about a firm (Grant, 1980; Armstrong et al., 2010). Large firms in general experience a

greater flow of information than small firms (Beaver, 1968). Atiase (1985) confirms that

the amount of private pre-disclosure information produced and disseminated is higher

for large firms. According to Bhushan (1989a), large firms possess more sources of

information and in general make more public announcements as opposed to small firms.

He reports that the marginal cost of information collection declines as firm size

increases. Collins, Kothari and Rayburn (1987) find that a higher number of traders and

analysts are processing information that is mainly available for large firms. These

market participants invest additional resources to gather more information about these

firms and the stock prices of large firms will then become even more informative

(Grossman, 1976; Grossman & Stiglitz, 1980). Hence, big firms are shown to be more

efficient in information production and dissemination (Collins et al., 1987).

Stock prices incorporate information more rapidly in large firms than in small firms

(Brown & Hillegeist, 2007). Freeman (1987) discovers that stock prices of large firms

113

predict accounting earnings more rapidly compared to small firms. This is because

information gathering activities are more intense for large firms as motivated by their

higher market capitalization. Hong et al. (2000) highlight that news flows faster in large

firms. This is mostly likely due to investors‟ preference to focus more on stocks in large

firms compared to small firms as a result of fixed costs of information acquisition faced

regardless of firm size. Diamond and Verrecchia (1991) denote that large firms

generally disclose more public information to reduce information asymmetry as there

will be higher opportunities for them to benefit from increased liquidity and diminished

cost of capital when compared to small firms.

It appears that stock prices of large firms are relatively more informative and small firms

would experience more severe information asymmetry difficulties. However, Bakke and

Whited (2010) show that small firms exhibit larger private information as shown in their

higher idiosyncratic volatility, as opposed to big firms. Chen et al. (2007) also express

that their private information measure, the value of 1-R2, is negatively correlated with

firm size, indicating that more private information is produced for small firms.

Firm managers make corporate decisions using their own private information as well as

public and private information reflected by stock prices. Drawing from the learning

theory, managers extract private information that they have yet to possess from firms‟

stock prices to make appropriate investment decisions (Chen et al., 2007; Bakke &

Whited, 2010; Frésard, 2012). Therefore, only private information that is new to

114

managers is able to influence managerial decisions whilst information that managers

already are aware of would not have any impact on corporate expenditure. Large firms

produce voluminous public information through public announcements, financial

disclosures and information gathering activity by market participants, including analysts

(Atiase, 1985; Bhushan, 1989a). However, this information is already made use by

managers in making their past investment decisions, hence would not be effective for

their current corporate expenditure decisions.

On the other hand, it is anticipated that managers of small firms are able to derive a

greater extent of new firm-specific private information from their stock prices. This

private information could be in a form of market‟s assessments of firms‟ growth

prospect, sales demand or competitiveness that firm managers have yet to possess (Dow

& Gorton, 1997; Subrahmanyam & Titman, 1999). This feedback serves as an input to

managers‟ corporate expenditure decisions.

Consequently, it is predicted that the relationship between a current year‟s stock price

informativeness and corporate expenditure of the subsequent year is dependent on

information asymmetry. The stock prices of small firms reveal new private information

that managers can learn and use in making their decisions on discretionary expenditure.

Therefore, managers of small firms will be more responsive to changes in stock price

informativeness and modify their corporate expenditure accordingly. This reasoning

lead to the following hypothesis:

115

H2a: The negative relationship between a current year‟s stock price informativeness and

the subsequent year‟s corporate expenditure is likely to be stronger for small firms.

3.3.2 Analyst Following

Analyst following (or analyst coverage) proxies for the effort and resources dedicated to

information gathering and it is represented by the number of analysts that cover a firm

(Hong et al., 2000; Frankel & Li, 2004; Frankel, Kothari & Weber, 2006; Armstrong et

al., 2010). Financial analysts act as information intermediaries in the capital markets.

They gather and interpret information from public and private sources, assess the current

financial performance of firms they are following, and prepare reports relating to the

prospects of these firms. Their reports comprise earnings forecast, recommendations to

either buy, sell or hold for shares and bonds, as well as a price target (Healy & Palepu,

2001; Asquith, Mikhail & Au, 2005).

Prior studies indicate that analyst reports are informative, that is, producing a stock-price

reaction (Givoly & Lakonishok, 1979; Lys & Sohn, 1990; Francis & Soffer, 1997) and

they complements financial reports informativeness (Frankel et al., 2006). Frankel and

Li (2004) reveal that firms with less informative financial statements are more likely to

have higher analyst coverage, suggesting that analyst following is a substitute for value

relevance of financial statements. Analysts‟ earnings forecasts are found to be more

superior than forecasts that are generated by time-series models (Fried & Givoly, 1982;

116

Bhushan, 1989b). Consequently, financial analysts convey more timely and new

information (Healy & Palepu, 2001; Asquith et al., 2005), thereby increasing the

informational efficiency of the capital markets (Moyer, Chatfield & Sisneros, 1989;

Frankel et al., 2006). Apart from providing information, financial analysts also act as a

monitoring mechanism to mitigate agency costs (Jensen & Meckling, 1976) and this is

empirically evidenced by Moyer et al. (1989) and Chung and Jo (1996).

Analysts gather and analyse both private and public information and consequently their

roles have a direct impact on the extent of information asymmetry faced by firms

(Armstrong et al., 2012). Increased analyst following is shown to be associated with

lower information asymmetry between managers and shareholders (Frankel & Li, 2004).

This is because analyst are increasing the speed of diffusion of firm-specific information

across market participants when they extend their coverage of firms (Hong et al., 2000).

However, Piotroski and Roulstone (2004) exemplify that firms covered by more analysts

incorporate greater industry and market-level information (instead of firm-specific

information) into stock prices as the analysts possess the necessary expertise and

industry affiliation to better analyse and distribute common industry information.

Share prices in firms with wider analyst following reflect future earnings more rapidly

than those firms neglected by analysts, thereby facilitating an earlier resource

deployment from less productive uses (Ayers & Freeman, 2003). This signifies that

stock prices of firms followed by a higher number of analysts will adjust more promptly

117

to new macroeconomic news, consistent with the study by Brennan, Jegadeesh and

Swaminathan (1993). Hong et al. (2000) found that stock prices adjust much more

sluggishly to bad firm-specific information for thinly followed firms. Similarly, widely

followed firms will portray smaller stock price surprises by firms‟ earnings

announcement, as compared to firms followed by fewer analysts (Dempsey, 1989).

Brennan and Subrahmanyam (1995) and Brennan and Tamarowski (2000) discover that

analyst following is negatively associated with adverse selection costs as measured

using the Kyle (1985) model. This suggests that high analyst coverage lowers

information asymmetry and improves stock liquidity.

In general, the intensity of analyst activities (analyst following) is negatively associated

with information asymmetry because information travels faster across the investing

public for stocks with higher analyst coverage. Nevertheless, prior empirical studies

show that the private information measure, for example, 1-R2 value used by Chen et al.

(2007) is negatively associated with analyst coverage. Bakke and Whited (2010) and

Frésard (2012) use analyst following to represent public information when they examine

whether private information in stock prices determine managerial decisions on corporate

investment or corporate cash savings respectively.

In Chen et al. (2007), high analyst coverage has a negative effect on investment

sensitivity to stock price. They explain that a large proportion of information produced

118

by analysts is obtained from managers. Being fully alert of this information, managers

do not change their investment when the information is reflected in the stock prices,

resulting in lower sensitivity of investment-to-stock prices. According to Chen et al.

(2007), the information released by analysts may be able to improve stock price

informativeness as more managerial information is incorporated into stock prices.

Analysts‟ reports, however, have lower tendency to affect managerial decisions as this

information was already factored in managers‟ past investments decisions. Frésard

(2012) also agrees that the content of the information analysts release is unlikely to be

new to managers as most of the information produced by analysts is mainly derived

from firm managers (Agrawal et al., 2006). As such, less managerial learning is

expected when analysts generate information about a firm‟s prospects.

Easley et al. (1998) demonstrate that analysts do not seem to generate new private

information but introduce more uninformed or noise trading to the stocks. This has

worsened the private information content in stock prices, thereby reducing the

sensitivity of investment-to-price as reported in Chen et al. (2007). Easley et al. (1998)

conclude that analysts rely more on public (rather than private) information for their

recommendations to investors to either buy, sell or hold firms‟ shares.

It is probable that firm managers with low analyst following are able to learn new firm-

specific private information from the stock prices and are therefore more enthusiastic in

adjusting their corporate expenditure in the subsequent year in response to a current

119

year‟s change in stock price informativeness. Therefore, it is expected that the

relationship between a current year‟s stock price informativeness and corporate

expenditure of the subsequent year is dependent on analyst following.

The following hypothesis is examined in an alternative form:

H2b: The negative relationship between a current year‟s stock price informativeness and

the subsequent year‟s corporate expenditure is likely to be stronger for firms with low

analyst following, ceteris paribus.

3.3.3 Bid-ask Spreads

In an imperfect information environment, the less informed investors “price-protect”

themselves against exploitation by the informed traders. Observable market liquidity

measures are used to ascertain the perceived level of information asymmetry that market

participants deal with (Lev, 1988). One of them is bid-ask spreads which directly

quantify the price protection that uninformed investors require as compensation for the

information risk when trading with the informed market participants (Welker, 1995).

The information asymmetry motive for bid-ask spreads originates from Bagehot (1971)

and was formally analysed by Copeland and Galai (1983). Bagehot (1971) points out

120

that a market maker23

will lose generally when trades with the better informed investors

as the latter possess more firm-specific information about the firms‟ true value

(indication of adverse selection problem). Further, the informed investors will only trade

with the market maker when the quoted prices are favourable to them (Lev, 1988). As

such, the market maker needs to recover the losses suffered and this is done through

gains obtained in trades with liquidity traders. Liquidity traders do not have any

informational advantage about firm values but need to trade to fulfil immediate liquidity

requirement. The gains to the market maker are achieved by setting an appropriate bid-

ask spread. The optimal behaviour of the market maker is modelled by Glosten and

Milgrom (1985) and their study shows that bid-ask spreads arise from adverse selection

problem. Larger bid-ask spreads represent more severe information asymmetry (Lev,

1988). Wider bid-ask spreads also signify higher transaction costs incurred in share

trading (Demsetz, 1968; Amihud & Mendelson, 1980) and is associated with lower

trading volumes, hence reducing market liquidity (Hamilton, 1978; Karpoff, 1986).

Bid-ask spreads are extensively used in the past studies to capture the information

environment of firms especially related to disclosure literature. For example, Welker

(1995) finds a negative association between financial analysts‟ disclosure ranking and

23 Market maker, or known as market specialist, is the exchange specialist (for listed securities) or the

over-the-counter dealer (for unlisted securities) who acts as a middleman by holding inventories and

facilitating trade. The market maker match buyers and sellers whose orders fail to arrive concurrently,

thus providing liquidity to the capital market (Bagehot, 1971; Glosten & Milgrom, 1985).

121

firms‟ bid-ask spreads while Healy, Hutton and Palepu (1999) show that increased

disclosure is associated with reduced bid-ask spreads. While these studies focus on US

firms that are highly committed in financial disclosure, Leuz and Verrecchia (2000)

examine German firms that have changed from local accounting standards to either

International Accounting Standard (IAS) or US Generally Accepted Accounting

Principles (GAAP) for their financial reporting. Their empirical results show that firms

who are committed to a higher disclosure standard exhibit lower bid-ask spreads,

indicating that the switch have successfully lessened information asymmetry. Petersen

and Plenborg (2006) also find that voluntary disclosure is negatively associated with

information asymmetry (represented by bid-ask spreads) in firms listed on the

Copenhagen Stock Exchange. The findings of the above studies indicate that disclosure

policy plays a pivotal role in reducing information asymmetry.

Bid-ask spreads are also applied in the field of corporate governance. Richardson (2000)

notes a positive relationship between bid-ask spreads and earnings management. The

imperfect information environment does not facilitate adequate monitoring of

management‟s action due to a lack of funds and information deficiency among outside

stakeholders. This allows managers to manage accruals and earnings for their personal

benefits. By contrast, Chung, Elder and Kim (2010) exemplify that corporate

governance increases financial and operational transparency, thereby reducing

information asymmetry between managers and shareholders as well as among investors.

122

Consequently, firms with higher level of corporate governance are found to have smaller

bid-ask spreads representing lower information asymmetry.

Large bid-ask spreads seem to represent higher information asymmetry when linked to

disclosure of public information (Welker, 1995; Richardson, 2000). Nonetheless, it is

crucial to comprehend the speed of diffusion of firm-specific information by examining

the connection between bid-ask spreads and idiosyncratic volatility. Spiegel and Wang

(2005) explain how idiosyncratic volatility connects with bid-ask spreads by looking at

their respective relationships with stock returns. Bid-ask spreads are found to be

positively correlated with stock returns (Amihud & Mendelson, 1986, 1989; Amihud,

2002) while a positive relationship is noted between idiosyncratic volatility and stock

returns (Lehmann, 1990; Goyal & Santa-Clara, 2003). Spiegel and Wang (2005) further

provide direct empirical evidence that idiosyncratic volatility is positively correlated

with bid-ask spreads.

Chan et al. (2013) also document a negative association between stock price

synchronicity (inverse of idiosyncratic volatility) and bid-ask spreads, an illiquidity

measure. They clarify when stock prices are more correlated with the market or industry,

market makers depend more on the information observed from the market movement24

to lessen the adverse selection risks of liquidity traders and improve market liquidity.

24 It is easier for market makers (market specialists) to observe market-wide information than firm-

specific information (Chan et al., 2013)

123

This implies that bid-ask spreads increase as idiosyncratic volatility improves. Therefore,

it is argued that managers of firms with large bid-ask spreads are accessible to more

firm-specific information and thus opportunities are higher for managerial learning to

improve in them making corporate decisions.

In addition, bid-ask spreads are shown to be higher for small firms due to lower

probability to find a party to trade, hence inventory and processing costs are higher for

the market makers for these firms (Stoll, 2000). Therefore, the rationale for applying the

learning hypothesis for small firms can also be extended to firms with high bid-ask

spreads.

As such, this study predicts that when firms experience high bid-ask spreads, their

managers will be able to learn more from private information revealed by the capital

markets, making them react more vigorously by altering corporate expenditure in the

subsequent year when stock price informativeness of a current year changes.

The above reasoning leads to the following hypothesis:

H2c: The negative relationship between a current year‟s stock price informativeness and

the subsequent year‟s corporate expenditure is more likely to be stronger for firms with

high bid-ask spreads, ceteris paribus.

124

3.4 Research Model

The research model of the current study is depicted in Figure 3.1.

Figure 3.1 Research Model

t= current year

t+1 = subsequent year

H1= Hypothesis 1

H2a= Hypothesis 2a

H2b= Hypothesis 2b

H2c= Hypothesis 2c

H2c

Bid-ask spreads

H2b

Analyst following

H2a

Firm

size

Corporate

Expenditure

(t+1)

H1

Stock Price

Informativeness

(t)

125

The research model displayed in Figure 3.1 highlights the association between a current

year‟s stock price informativeness and corporate expenditure of the subsequent year by

adopting a lead-lag approach. The learning theory suggests that firm managers apply the

new firm-specific information learned from the stock markets to improve efficiency of

their corporate decisions (Luo, 2005; Chen et al., 2007; Frésard, 2012). It is argued in

this study that stock price informativeness induces managers to initiate changes to their

corporate expenditure in three specific areas, namely, R&D expenditure, CAPEX and

SGA costs. When stock price informativeness is at a low level, firm managers will

maintain a high level of corporate expenditure by conveying positive signals about firm‟

future performance to the stock markets in view of the expected benefits deriving from

corporate expenditure. Conversely, a high level of stock price informativeness indicates

that stock prices are reflecting positive information about firms‟ future earnings and

thereby reflecting better allocation of firms‟ scarce resources. Relatively, their managers

need not incur high level of corporate expenditure. Managers learn valuable information

from the stock market and will respond by maintaining an appropriate level of corporate

expenditure. Consequently, Hypothesis 1 postulates that a current year‟s stock price

informativeness is negatively associated with corporate expenditure of the subsequent

year.

Further, the research model exhibited in Figure 3.1 illustrates how the relationship

between a current year‟s stock price informativeness and the subsequent year‟s

126

corporate expenditure is dependent on information asymmetry. Three proxies of

information asymmetry, namely, firm size, analyst following and bid-ask spreads are

used in this study. Large firms and firms with high analyst following as well as firms

with low bid-ask spreads are associated with low information asymmetry as information

flows faster in these firms. However, lesser managerial learning is expected in these

firms. This is because the information generated by large firms though public

announcements and financial disclosure is already utilised by their managers in past

investment decisions while information produced by analysts is mainly sourced from

managers or is already considered in managers‟ corporate decisions. Applying ideas

from the learning theory, the information produced by large firms, firms with high

analyst following and firms with low bid-ask spreads discourages managerial learning

and hence does not have any impact on firms‟ corporate expenditure decision.

Conversely, empirical findings show that more private information is produced for small

firms (Chen et al., 2007; Bakke & Whited, 2010), firms with low analyst following

(Chen et al., 2007) and firms with high bid-ask spreads (Chan et al., 2013). It is

predicted that the higher extent of managerial learning in these firms motivates their

managers to learn quicker and react more “aggressively” in making changes to firms‟

corporate expenditure such as R&D expenditure, CAPEX and SGA costs. Consequently,

Hypothesis 2a posits that the association between a current year‟s stock price

informativeness and the subsequent year‟s corporate expenditure is likely to be stronger

in small firms while Hypothesis 2b predicts that the association between a current year‟s

stock price informativeness and the subsequent year‟s corporate expenditure is likely to

127

be stronger in firms with low analyst following. Hypothesis 2c hypothesizes that the

association between a current year‟s stock price informativeness and the subsequent

year‟s corporate expenditure is likely to be stronger in firms with high bid-ask spreads.

3.5 Chapter Summary

This chapter provides an overview of the empirical literature and the theoretical

framework applied for the current study. The learning theory is used to explain the

relationship between a current year‟s stock price informativeness and the subsequent

year‟s corporate expenditure, while the information asymmetry theory is applied to

understand whether the association between a current year‟s stock price informativeness

and corporate expenditure of the subsequent year is dependent on firm size, analyst

following and bid-ask spreads. As a result, four hypotheses (H1, H2a, H2b and H2c) are

formulated. The research model is then presented to illustrate the hypotheses developed.

The next chapter (Chapter 4) evaluates and outlines the methodology deployed in this

study to examine the hypotheses developed.

128

CHAPTER 4

RESEARCH METHODOLOGY

4.1 Introduction

The preceding chapter (Chapter 3) provides the theoretical framework of the study. The

learning theory is applied to examine the association between a current year‟s stock

price informativeness and the subsequent year‟s corporate expenditure. The information

asymmetry theory is applied to determine whether this association is dependent on

information asymmetry. Four hypotheses are developed and illustrated using a research

model.

This chapter (Chapter 4) describes in detail the research methodology adopted for this

study and outlines procedures that are employed to examine the research problems.

Section 4.2 elaborates on the research paradigm adopted in this study. This is followed

by a description of the principal population data (Section 4.3), outlining sources of

secondary data (Section 4.4), explaining variables measurement (Section 4.5), model

specification (Section 4.6) and selection procedure for choosing the sample (Section 4.7).

Section 4.8 provides an overview of the statistical methodology employed in the current

study and a summary of this chapter is provided in Section 4.9.

129

4.2 Research Paradigm

Research paradigm (also referred to as a philosophical perspective) is an approach to

enhance knowledge by adopting certain theoretical assumptions, research goals and

research methods (Kuhn, 1962). Research is conducted based on researchers‟ own

philosophical stances which are largely dependent upon their personal backgrounds,

histories, cultural contexts and perceptions. This explains why there could be different

answers even when there are similar research questions.

Research paradigms are categorized into different groups, for example, positivism,

constructivism, interpretivism and critical theory, among others (Crotty, 1998, p. 16).

According to Guba (1990) and Ponterotto (2005), these paradigms differ from the

following perspectives:

a) ontology belief which deals with the nature of “reality” that is “how things really

are” and “how things work”,

b) epistemology, which is concerned with how knowledge is created and the

relationship between research participants and the researcher,

c) methodology, which is the process and procedures adopted by researchers in

finding out knowledge, and

d) goal of research.

The philosophical perspective applied to address the research questions formulated in

this study is positivism. The choice of this perspective is derived after considering its

130

ontological, epistemological and methodological standpoints. The ultimate aim of this

research is to assess how firm-level corporate expenditure responds to stock price

informativeness. This study also investigates whether information asymmetry plays a

role in the relationship between stock price informativeness and corporate expenditure.

Positivism is a term coined by Auguste Comte (1798-1857) through his work “Societe

Positiviste” in 1848 (Crotty, 1998, p. 19). Emphasizing on “hypothetico-deductive”

method (Crotty, 1998, p. 32) , this is the main philosophical position in management

studies that assumes that knowledge of the world is obtained through applying scientific

approaches (Eriksson & Kovalainen, 2008, p. 18).

4.3 Population Data

The target population is US public listed companies (PLCs) for the years 2003 to 2009.

Data prior to 2003 are excluded to minimise any possible impact arising from the

Sarbanes-Oxley Act executed in year 2002. This study adopts a lead-lag approach, thus

the data used for the dependent variable, i.e., corporate expenditure are for the years

2004 to 2010 while the data for independent variable being the stock price

informativeness as well as the control variables are for the years 2003 to 2009. To

qualify as a sample for the study, a company must possess a complete set of data in

respect of the dependent, independent and control variables for all the relevant years

covered.

131

4.4 Sources of Secondary Data

Secondary data is collected from numerous sources, namely, Compustat database for

financial data, The Centre for Research in Security Price (CRSP) database for stock

market data, Risk Metrics database for Gompers‟s Governance index, Execucomp

database for directors‟ data and Institutional Brokers‟ Estimate System (I/B/E/S) for

analysts‟ information.

4.5 Variables Measurement

This item outlines how the dependent variable (corporate expenditure), independent

variable (stock price informativeness), proxies for information asymmetry and control

variables used in this study are measured.

4.5.1 Dependent Variable

In this study, the dependent variable, corporate expenditure is measured by using the

following three variables, R&D expenditure, CAPEX and SGA costs:

a) Research and development expenditure

Research and development expenditure is measured using the natural logarithm of

annual R&D costs-to-assets ratio. This ratio scaled by book value of total assets is

commonly used particularly in the strategic management literature (Mizik & Jacobson,

132

2003; Eberhart et al., 2004; Kor, 2006; McAlister et al., 2007; Eberhart et al., 2008; Luo

& de Jong, 2012). According to Eberhart et al. (2008), the ratio of R&D costs/total

assets is more superior than the ratio of R&D costs/market value of equity as the latter

may provide a misleading impression of R&D expenditure caused by the fluctuating

nature of the market value of equity. Kor (2006) is of the view that some firms may not

have sales revenue in their early years of product development, hence he prefers to

standardize R&D investment by total assets. Therefore, R&D costs/total asset ratio is

used in this study because the book value of total assets does not vary much on an

annual basis, hence, it is found to be statistically more stable.

b) Capital expenditure

In this study, CAPEX is represented by the natural logarithm of annual capital

expenditure scaled by total assets. Scaling the annual capital expenditure by total assets

is broadly used in extant literature (Strong & Meyer, 1990; Carpenter & Guariglia, 2008;

Inci et al., 2009).

c) Selling, general and administrative costs

Selling, general and administrative costs are measured by the natural logarithm of the

SGA costs of the current year deflated by total assets. The ratio of SGA costs/total assets

is used by Banker et al. (2006, 2011b) in their studies of value relevance of SGA

expenditure. Most research in the area of SGA costs standardize SGA expenditure by

total sales to examine its asymmetric cost behaviour (Anderson et al., 2003; Anderson et

133

al., 2007; Dalla Via & Perego, Forthcoming) and information contents (Baumgarten et

al., 2010; Janakiraman, 2010). Nevertheless, the ratio of SGA costs/total assets is

adopted in the current study as the book value of total assets is more enduring when

compared to fluctuating sales.

4.5.2 Independent Variable

The independent variable, that is, stock price informativeness is represented by

idiosyncratic volatility. Following Ferreira and Laux (2007) and Gul et al. (2011b),

idiosyncratic volatility in this study is measured using a regression projection of daily

excess stock returns for each firm i for every fiscal year on the returns of the market

index using the CAPM model:

(4.1)

where:

is daily excess stock returns for firm i and is the daily value-weighted excess

stock return of the market portfolio. Both coefficients and are estimated for each

financial year by employing regression analysis. This model assumes that all systematic

risk is captured by the coefficient while the variance of represents idiosyncratic

volatility (unsystematic risk).

134

The value of 1- is obtained from the regression (4.1), where

is the coefficient of

determination of firm i in year t. It represents the relative idiosyncratic volatility, i.e., the

ratio of idiosyncratic variance to total volatility for each firm-year t and it is the

proportion of volatility that is not explained by systematic components (Ferreira & Laux,

2007). As 1- is bound within the intervals [0,1], a logistic transformation is done on

the ratio of (1-R2)/R

2 following Morck et al. (2000) and Ferreira and Laux (2007) to

generate the variable idiosyncratic volatility, Ψ with a more normal distribution.

Therefore, idiosyncratic volatility Ψi,t is formally defined as:

Ψi,t = Ln

(4.2)

where:

Ψi,t idiosyncratic volatility measures the firm-specific stock return variation relative to

market-wide variation and is the coefficient of determination of firm i in year t.

Idiosyncratic volatility is higher when firms‟ stock return is less correlated with market

returns, indicating a more informative stock price. The following variations to the above

model are examined in the additional tests to verify the robustness of idiosyncratic

volatility measure:

135

a) Use different market index.

The equally-weighted market return is used to replace value-weighted market return to

generate a different measure of idiosyncratic volatility (Gul et al., 2011a).

b) Use Fama & French three-factor model (Fama & French, 1993, 1995, 1996)

Following Ferreira et al. (2011) and Gul et al. (2011b), the annual idiosyncratic

volatility is estimated by using the Fama-French three-factor model that is by obtaining

the value of 1-R2 from the regression for each firm year :

(4.3)

where:

is the daily excess return of stock i in day t, is the daily value-weighted excess

market return, is the small-minus-big size factor return, and is the high-

minus-low book-to-market factor return. The daily returns for the small-minus-big

( and high-minus-low ( ) factors are drawn from French‟s website at

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_library.html.

c) Use Brockman and Yan‟s (2009) model

In Brockman and Yan (2009), the idiosyncratic volatility is estimated by controlling

market and industry factors. This is done by regressing firms‟ daily return on

contemporaneous and lagged daily market return, as well as contemporaneous and

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_library.html

136

lagged daily industry return for each firm-year observation, as indicated in Equation 4.4.

Such an approach was adopted by Gul et al. (2010) and Gul et al. (2011a).

(4.4)

where:

is the daily excess return of stock i in day t, is the contemporaneous daily value-

weighted market return, is the lagged daily value-weighted market return,

is the contemporaneous daily industry return and is the lagged daily industry

return.

The industry return for a specific day is created using all firms with the same two-digit

Standard Industrial Classification (SIC) codes. Lagged market and industry return is

included in the regression to control for informed trading that can affect the timing of

incorporation of the market and industry information into stock prices (Piotroski &

Roulstone, 2004; Brockman & Yan, 2009).

4.5.3 Proxies of Information Asymmetry

Prior studies have identified a number of proxies to measure information asymmetry

(Atiase, 1985; Aboody & Lev, 2000; Armstrong et al., 2010; Chung et al., 2010). This

study selects three proxies of information asymmetry, namely, firm size, analyst

following and bid-ask spreads and each of them are measured as follows:

137

a) Firm size

Following Beaver (1968), this study uses the book value of total assets to represent firm

size as one of the proxies for the amount of information available in a firm. Many

empirical studies on information asymmetry tend to use other measures as the proxy for

firm size such as market capitalization (Atiase, 1985; Freeman, 1987) or the market in

which firm shares are traded, for example, over-the-counter versus New York Stock

Exchange (NYSE) (Grant, 1980). The value of total assets portrays more steady patterns

over the years as opposed to market value of equity, thus the former is preferred in this

study. Higher book value of total assets indicates larger firm size.

b) Analyst following

In the empirical literature of information asymmetry, analyst following is represented by

the number of analysts that follow a firm (Piotroski & Roulstone, 2004; Frankel et al.,

2006; Armstrong et al., 2010).

c) Bid-ask spreads

Bid-ask spread is computed as the mean of difference between ask price and bid price

for each stock at the close of trade on the last trading day of the year, scaled by the

average of ask and bid price (Welker, 1995; Richardson, 2000; Chung et al., 2010).

Following Chung et al. (2010), the quoted bid ask spread for a company i at time t is

calculated as follows:

138

(4.5)

where:

is the bid-ask spread for stock i at time t, is the ask price for stock i at

time t, and is the bid price for stock i at time t.

Bid-ask spread for each firm is firstly calculated at the end of the trading day of each

month and then the average bid-ask spread for the year will be computed for each firm.

4.5.4 Control Variables

Control variables are included in the regression. It is preferred econometrically to

include control variables whenever possible (Durnev et al., 2004). In this study, a

similar set of control variables are used for each of the corporate expenditures examined

as these factors generally influence corporate expenditure decisions. Some control

variables that are specific to certain corporate expenditure, such as the variables free

cash flow and employee intensity are added in examining CAPEX and SGA costs

respectively.

The control variables applied are categorized into firm characteristics and corporate

governance control variables. Each of them are outlined and measured as follows:

139

4.5.4.1 Firm Characteristics Control Variables

There are 20 firm characteristics that may influence corporate expenditure decisions and

each of them are delineated below.

a) Firm size

Firm size (SIZE) is an important determinant for all corporate expenditure and it is

expected that as a firm grows, expenditure on R&D, capital projects and SGA increases

(Rothwell, 1984; Vogt, 1994; Banker et al., 2011b). It is measured in this study as the

natural logarithm of total assets and its squared value at the end of the fiscal year (Coles,

Lemmon & Meschke, 2012). Total assets are commonly used to represent firm size in

the empirical research of the learning theory (Bakke & Whited, 2010; Frésard, 2012)

and of stock price informativeness (Gul et al., 2010; Gul et al., 2011a).

b) Analyst following

Analyst following (ANALYST) is measured as the number of analysts following a firm

(Chen et al., 2007; Ferreira et al., 2011). Analyst following is strongly related with firm

size (Bhushan, 1989a), thus firms with higher analyst following is larger and thus are

more financially feasible to invest in R&D initiatives, CAPEX and SGA expenditure. A

positive association between analyst following and corporate expenditure is predicted.

140

c) Firm age

Firm age (AGE) is measured as the natural logarithm of the number of years since the

firm‟s stock is exchange-listed (Coles, Daniel & Naveen, 2008; Ferreira et al., 2011).

Haynes and Hillman (2010) find firm age is positively related to changes in corporate

expenditure. A positive association between firm age and corporate expenditure is

expected as older firms are normally bigger and financially stronger to invest more in

R&D costs, CAPEX and SGA expenditure.

d) Volatility of return on equity

The volatility of return on equity (StdROE) representing business riskiness is calculated

as the standard deviation of annual return on equity (ROE) over the last three years.25

ROE is measured as earnings before extraordinary items divided by the book value of

equity at year end. Banker et al. (2011b) suggest that firms operating under an unstable

environment are more likely to increase their SGA costs in view of its future value-

creation ability. Applying the same rationale, R&D investment contributes substantially

to productivity and its benefit can last for years (Lev, 1999). Therefore, a positive

association is expected between StdROE with R&D expenditure and SGA costs

respectively. However, the relationship sign between StdROE and CAPEX cannot be

predicted because the contribution of CAPEX to future economic value is dependent

25 Volatility of ROE is represented by variance of annual ROE over the last three years in Gul et al.

(2011b). This study uses standard deviation of annual ROE instead.

141

upon whether pecking order model or free cash flow hypothesis prevail in the

companies examined (Vogt, 1994).

e) Cash flow volatility

Cash flow volatility (StdCF) is measured as the standard deviation of cash flow for the

past five years (Harford, Mansi & Maxwell, 2008). This is a second proxy of uncertain

business condition. Similar to StdROE, firms operating under ambiguous cash flow

positions are more likely to invest more in corporate expenditure such as R&D

expenditure and SGA costs to derive more future benefits, therefore a positive

relationship is expected. The predicted sign for the relationship between StdCF and

CAPEX, however, is uncertain depending whether pecking order model or free cash

flow hypothesis exists in the companies examined (Vogt, 1994).

f) Stock return volatility

Stock return volatility (StdRET) is measured by the average standard deviation of daily

stock returns during the fiscal year (Demsetz & Lehn, 1985; Gul et al., 2011b). It

proxies for an ambiguous information environment about firms facing investors in the

capital markets (Bhagat & Bolton, 2008) and “noise” in evaluating managers‟ action

(Gillan, Hartzell & Starks, 2003). Intuitively, when stock return is volatile indicating

higher investors‟ uncertainty on firms‟ information, managers may not be able to obtain

the appropriate feedback from the stock markets and delay in making investment in

R&D expenditure, CAPEX and SGA costs, and vice versa. This reflects a negative

142

association between StdRET and corporate expenditure. However, the StdRET measures

the extent of unstable business environment and is predicted to be positively associated

with corporate expenditure as higher level of R&D expenditure and SGA costs will be

incurred to reap the benefits. As such, the net effect of StdRET on corporate expenditure

is unclear.

g) Market-to-book ratio

The market-to-book ratio (MB) measures firms‟ investment opportunity sets or their

growth prospects, given by the ratio of market capitalization to the book value of

shareholders‟ funds at balance sheet date (Linck, Netter & Yang, 2008). Growth

opportunities are found to be positively related to SGA costs (Banker et al., 2011b), thus,

a positive relationship is predicted between market-to-book ratio and corporate

expenditure.

h) Return on assets

Return on assets (ROA) is the earnings before extraordinary items deflated by total

assets at the end of each financial year (Ferreira et al., 2011). Gordon and Iyengar (1996)

highlight that return on investment is positively associated with CAPEX. Return on

investment is closely related to ROA, hence a positive relationship is predicted between

ROA and CAPEX. Since R&D expenditure and SGA costs are expensed off to the

current year income statements, the earnings figures are negatively affected. As ROA is

143

derived from current year‟s earnings, it is expected that the former is negatively

associated with both R&D expenditure and SGA costs.

i) Working capital ratio

Following Finkelstein and Hambrick (1990), working capital (WC) ratio represents the

degree of availability of immediate resources, referred to as “slack”. It is calculated by

dividing working capital with sales. “Slack” is spare resource that shields the companies

in meeting short-term needs and opportunities (Bourgeois, 1981). Finkelstein and

Hambrick (1990) document a negative association between working capital ratio and a

composite index measuring strategic persistence.26

This indicates that firms with higher

slack will have a lower tendency to change their corporate costs. In this study, a

negative association between working capital ratio and corporate expenditure is

expected as any immediate needs can be fulfilled by slack, hence reducing any need to

incur immediate costs especially in the area of SGA costs.

j) Dividend dummy

Annual dividend dummy (DIV) equals to “one” if firm pays dividend for each fiscal

year, and “zero” otherwise (Gul et al., 2011b). Vogt (1994) suggests managers make

low dividend payout in order for firms to preserve cash flows for CAPEX. The same

26 The composite index of strategic persistence is a summation of variance score of six cost indicators,

including R&D expenditure, CAPEX and SGA costs. A higher strategic persistence index indicates that

firms are persistent in adopting their corporate costs structure.

144

rationale could be applied to R&D expenditure and SGA costs. Therefore, a negative

relationship between dividend dummy and corporate expenditure is predicted.

k) Merger dummy

The merger dummy (MERGER) equals to “one” if there is any merger and acquisition

activity in year t and “zero” otherwise (Gul et al., 2011b). Intuitively, if merger and

acquisition takes place in a firm during the year, CAPEX is expected to be reduced as

the former is itself a form of capital investment. Nevertheless, it is difficult to predict the

SGA expenditure level when merger and acquisition activities occur. By merging or

acquiring other business units, firms are expected to incur higher SGA costs. Managers,

however, may want to consolidate their business operations by removing some

redundancies in human resources or office space, thereby reducing SGA expenditure.

Consequently, the net effect of merger and acquisition activity on SGA expenditure is

unclear. The likelihood for managers to undertake more risky R&D projects when

merger and acquisition activity takes place during the year is dependent upon the risk-

taking behaviour of managers and funding availability. Judging from this analysis, the

direction of the association between merger/acquisition activities and R&D expenditure

is yet to be ascertained.

l) Restructuring dummy

Restructuring dummy (RESTRUCT) is equals to “one” if there is any restructuring in

year t and “zero” otherwise. As in the case of merger dummy, it is predicted that firm

145

managers will reduce CAPEX. The tendency for firms undergoing restructuring is high

in taking up more risky R&D investment and incur more SGA costs, for example,

expenditure in human resources and marketing. The restructuring dummy is predicted to

be positively associated with R&D and SGA costs separately.

m) Loss dummy

Loss dummy (LOSS) equals to “one” if firm is in a loss situation in the current year and

“zero” otherwise (Gul et al., 2010). This is to isolate the effect of loss-making

companies as they may make different sets of management decisions when compared to

profitable firms. R&D expenditure is generally a strategy of high-risk high-return that

provides future gains to shareholders. Firms investing in R&D projects for long-term

growth may not have sales in the early years of development (Kor, 2006), resulting in

them incurring loss. Furthermore, firms that emphasize strategic control encourage long-

term risk taking and evaluate long-term performance by rewarding innovation (Shadab,

2008). Their managers are more likely to support R&D activities even though the firms

are incurring loss, especially if they are well represented in the board of directors

(Baysinger et al., 1991). Consequently, loss dummy is expected to be positively

associated with R&D expenditure.

In a study covering 40 countries, earnings are found positively associated with CAPEX

regardless of the form of legal systems and the extent of financial development (Inci et

146

al., 2009). In view of the scarcity of internally generated cash flow, loss dummy is

predicted to be negatively related to CAPEX.

Uncertainty arises in predicting the relationship between loss dummy and SGA costs.

Loss-making firms may adopt cost-cutting strategy, hence reducing SGA expenditure. In

view of the value-creation ability of SGA expenditure, loss-making firms may in turn

invest in training and marketing costs, thereby boosting SGA costs level. The predicted

sign for the relationship between loss dummy and SGA costs is yet to be ascertained due

to the contrasting views.

n) Leverage

Leverage is measured as total debts divided by total assets. An inverse relationship is

predicted between leverage and R&D investment as managers are discouraged from

investing in R&D initiatives so as to increase current cash flows to service their debts

(Barker & Mueller, 2002). The same justification can be extended to CAPEX and SGA

expenditure, thus a negative association between leverage and corporate expenditure is

expected.

o) Other corporate expenditure

Due to resource constraints, firms that invest more in tangible assets (CAPEX) tend to

spend less on intangibles (R&D expenditure and SGA costs) (Banker et al., 2011b). A

147

negative association is predicted between CAPEX and R&D expenditure as well as

between CAPEX and SGA costs.

p) Diversification

Diversification is determined by using an entropy27

measure of a firm‟s sales share in

different business sectors (DVS_BIZ) or geographic areas (DVS_GEO) (Palepu, 1985;

Wiersema & Bowen, 2008). The extent of diversification is negatively related with

R&D expenditure in diversified firms as suggested by Baysinger and Hoskisson (1989).

This is because highly diversified firms apply strict financial control (Gupta, 1987) and

their managers are more likely to avoid risky R&D projects to meet short-term financial

performance goals. A negative association between diversification and R&D

expenditure is therefore predicted.

Banker et al. (2011b) show that firms operating in a competitive industry tend to invest

more in SGA expenditure. High diversification level signifies more intense competition,

hence diversification is predicted to be positively connected to SGA costs. Similarly,

more intense competition could imply higher needs for capital expenditure, thus a

positive association between diversification and CAPEX is expected.

27 Entropy measure of diversification = ∑

, being the share of the ith segment in the total

sales of the firm.

148

q) Year and Industry dummies

Year dummies are included in the regression due to divergent business environments

and market conditions for each year. Industry membership determines the level of firms‟

R&D investments due to varying extent of scientific knowledge in the field firms

operate (Baysinger & Hoskisson, 1989). Similarly, different industries have different

needs for CAPEX and SGA costs and it is therefore important to control for industry

membership in the regression. Industry dummies are measured by one-digit SIC code in

this study.

r) Free cash flow

Free cash flow (FCF) is an important determinant for CAPEX, thus, it is included

specifically to examine the relationship between stock price informativeness and

CAPEX. Following Gul and Tsui (1998) and Gul (2001), FCF is measured by operating

income before depreciation minus interest expense, taxes, preferred dividend and

ordinary dividend, and scaled by total assets. The free cash flow hypothesis suggests

that managers prefer to over-invest firms‟ free cash flow wastefully on capital projects

for empire building as opposed to paying it out as dividends and debt-financed share

repurchase (Jensen, 1986). Strong and Meyer (1990) and Devereux and Schiantarelli

(1990) found a significant positive relationship between residual cash flows and

discretionary capital investment. Therefore, if free cash flow hypothesis prevails, a

positive relationship is predicted between free cash flow and CAPEX.

149

s) Employee intensity

Employee intensity (EMP) influences SGA costs because firms with a higher number of

employees are more likely to invest in human capital costs such as payroll and training

costs (Banker et al., 2011b; Chen et al., 2012b). It is frequently measured as the number

of employees scaled by sales. This variable is included in the regression model to

examine the association between stock price informativeness and SGA costs. However,

due to the fluctuating nature of sales revenue, EMP is calculated as the number of

employees divided by total assets in this study. A positive association is expected

between employee intensity and SGA costs.

4.5.4.2 Corporate Governance Control Variables

Corporate governance factors that are included as control variables are audit quality,

earnings management and use of a Governance index. An additional variable that is used

in the robustness test is the variable percentage of independent directors.

a) Audit quality

Auditing is found to ease information asymmetry between insiders and investors by

improving the quality of information presented by financial statements (Dopuch &

Simunic, 1982). It improves transparency and is essential to control the effect of audit

quality as a proxy for corporate governance. Gul et al. (2010) suggest that appointment

150

of high-quality auditors (proxied by “Big-4 auditors”) in the Chinese market helps to

facilitate a better flow of firm-specific information to the market. However, “Big-4

auditors” is not a suitable proxy for audit quality in the US context as nearly all listed

firms in developed markets such as the NYSE engage Big-4 auditors. In Gul, Fung and

Jaggi (2009), an industry-specialist auditor has a higher likelihood to detect irregularities

and fraud. These auditors are shown to provide audit services of higher quality even if

there is deficiency of client-specific knowledge due to short audit tenure. Therefore, in

this study, audit quality (AUDIT) is represented by an industry-specialist auditor who

possesses the highest share of clients‟ total assets in the two-digit SIC industry code.

Audit quality is predicted to be positively linked to corporate expenditure.

b) Earnings management

Following Jones (1991) model, earnings management (EM) is measured by the value of

firm-specific residuals from an industry regression of total accruals on the reciprocals of

total assets, revenue growth and fixed assets. Agency theory suggests that managers are

risk averse and emphasize on meeting short-term performance goals, hence may manage

earnings and accruals for personal gain (Jensen & Meckling, 1976). Therefore, EM may

lead to lower R&D expenditure and SGA costs to show higher earnings in the current

year. Moreover, self-interested managers are reluctant to invest in both R&D and capital

projects as these investments do not yield short-term returns. Thus, EM is predicted to

be negatively linked to corporate expenditure.

151

c) Governance index

A Governance index, GINDEX was developed by Gompers, Ishii and Metrick (2003)

and it measures shareholder rights in each firm from 24 distinct corporate governance

provisions. It covers five specific areas, namely, tactics for delaying hostile bidders,

voting rights, protection of directors and officers, other takeover defences as well as

state laws. The GINDEX assigned to firms range from five (the strongest shareholder

rights) to 14 (the weakest shareholder rights), indicating that corporate governance level

deteriorates as GINDEX increases. Therefore, it is predicted that GINDEX is negatively

associated with corporate expenditure as better corporate governance fosters greater

investment in R&D projects, CAPEX and SGA costs.

d) Percentage of independent directors

Percentage of independent directors (INDDIRPCT) is measured by the independent non-

executive directors as a percentage of all directors on board. It is an alternative corporate

governance variable used in this study as a robustness check. Independent board of

directors play a fundamental corporate governance role in disciplining managerial

actions. Their main priority is to advocate shareholders‟ wealth (Jensen & Meckling,

1976). Baysinger and Hoskisson (1990) suggest firms with outsider-dominated board of

directors tend to incur lower R&D expenditure if they emphasize financial controls.

Thus, a negative relationship is predicted between independent director‟s percentage and

R&D expenditure.

152

On the other hand, the expected association between percentage of independent directors

and CAPEX is dependent upon whether market value maximisation hypothesis or size

maximisation hypothesis prevails in firms (McConnell & Muscarella, 1985). If firm

managers invest in CAPEX to maximise firm value for the benefit of the shareholders,

then the CAPEX decisions will be supported by the independent directors. Hence, a

positive association between independent directors and CAPEX is expected. In

accordance with size maximisation hypothesis, firm managers could, however, over-

invest for empire building (Jensen, 1986). The independent directors, in view of their

assigned role to uphold investors‟ wealth, would not favour the CAPEX decisions by

management. A negative relationship between independent directors and CAPEX is then

expected. As such, the net effect of the relationship between independent directors and

CAPEX cannot be predicted.

A positive association is predicted between percentage of independent directors and

SGA costs in light of the ability of SGA expenditure in creating future value.

4.6 Model Specification

This section delineates the model specification for the main model in examining the

hypotheses developed as well as for the robustness tests conducted.

153

4.6.1 Main Model

The first hypothesis (H1) of the current study examines whether current year‟s stock

price informativeness motivates firms to change corporate expenditure in the subsequent

year. This can be empirically tested using a regression indicated in Equation 4.6.

∑ ∑ ∑

(4.6)

where:

represents corporate expenditure for firm i of the subsequent year, t+1. Three

proxies of corporate expenditures are employed in this study, namely, R&D expenditure,

CAPEX and SGA costs in this study. The coefficient is the intercept while

coefficient is the coefficient of interest. The variable represents idiosyncratic

volatility for firm i of the current year, t and denotes a set of control variables.

Year and Industry dummies are included in the model while represents unspecified

random factors.

This study adopts a lead-lag approach as recent work in finance and strategic

management show that the Efficient Market Hypothesis (Fama, 1970) is not always

reliable as the market takes time to incorporate public information (Eberhart et al., 2004).

Furthermore, the lead-lag structure can reduce reverse causality concern, as it specifies

154

the impact‟s direction from stock price informativeness to corporate expenditure, and

not in a reverse situation.

Hypothesis 1 (H1) of this study posits that the stock price informativeness of the current

year is negatively associated with the subsequent year‟s corporate expenditure.

Therefore, the sign for the coefficient for idiosyncratic volatility, represented by , is

predicted to be negative.

This study also examines whether the relationship between stock price informativeness

and corporate expenditure is dependent on information asymmetry. Three proxies of

information asymmetry, namely, firm size, analyst following and bid-ask spreads are

employed separately. Two sub-samples are firstly segregated based on the median value

of measurement for firm size (natural logarithm of total assets), analyst following

(number of analysts that follow a firm) and bid-ask spreads (value of bid-ask spreads)

respectively. The regression model presented in Equation 4.6 is then applied. The

significance levels of coefficient for the two sub-samples of each of the proxy of

information asymmetry are compared to determine which sub-sample reflects a stronger

relationship between stock price informativeness and corporate expenditure.

For example, in the case of firm size proxy, the full sample is split to two sub-samples

based on the value of firm‟s natural logarithm of total assets. The sub-sample with value

of the natural logarithm of total assets above the median value of the full sample is

155

considered as large firms, the others are small firms. As Hypothesis 2a conjectures that

the relationship between a current year‟s stock price informativeness and the subsequent

year‟s corporate expenditure is likely to be stronger for small firms, the significance

level of coefficient for small firms is predicted to be greater than the significance

level of coefficient for large firms. A similar interpretation applies to the other two

proxies of information asymmetry by looking at the significance level of coefficient

of their respective sub-samples. Hypothesis 2b conjectures that the relationship between

a current year‟s stock price informativeness and the subsequent year‟s corporate

expenditure is likely to be stronger for firms with low analyst following. Therefore, the

significance level of coefficient for firms with low analyst following is predicted to

be greater than that of firms with high analyst following. Hypothesis 2c predicts that the


year‟s corporate expenditure is likely to be stronger for firms with high bid-ask spreads.

Hence, the significance level of for firms with high bid-ask spreads is expected to be

greater than that of firms with low bid-ask spreads.

4.6.2 Robustness Tests

Endogeneity problems are prevalent in empirical research in the areas of accounting and

finance. In order to tackle the potential endogeneity issue, a lead-lag approach is

adopted in this study while a change model as well as a two-stage least squares (2SLS)

156

regression are conducted. The model specifications of the change model and 2SLS

regression are outlined in items 4.6.2.1 and 4.6.2.2 respectively.

4.6.2.1 Change Model

In this study, „change model‟ examines the changes to firms‟ corporate expenditure

following changes in stock price informativeness. Consistent with the main model

presented in Equation 4.6 outlined in item 4.6.1, the change model follows a lead-lag

structure. It deals with the problem of reverse causality as the direction of impact is

specified from current year‟s stock price informativeness to subsequent year‟s corporate

expenditure.

The change model can be empirically tested using the regression indicated in Equation

4.7.

∑ ∑ ∑

(4.7)

where:

is changes in corporate expenditure for firm i from year t to year t+1. The

coefficient is the intercept while the coefficient is the coefficient of interest.

is changes in idiosyncratic volatility from year t-1 to year t and is

157

changes in a set of control variables (excluded dummy variables) from year t-1 to year t.

Year and Industry dummies are included in the model while is unspecific random

factors.

The coefficient captures the difference in corporate expenditure one year after the

changes in idiosyncratic volatility and the sign of indicates whether corporate

expenditure increases or decreases following the changes in idiosyncratic volatility. The

sign of the coefficient is predicted to be negative as Hypothesis 1 of this study

conjectures that a current year‟s stock price informativeness is negatively associated

with the subsequent year‟s corporate expenditure.

The change model is able to display how fast managers react to changes in idiosyncratic

volatility when making their corporate expenditure decisions as idiosyncratic volatility

strengthens or weakens. As such, the full sample is separated into two sub-samples of

observations with increasing and decreasing idiosyncratic volatilities. Analyses are then

performed by applying the regression model presented in Equation 4.7.

4.6.2.2 Two-Stage Least Squares Regression

It is possible that corporate expenditure and stock price informativeness are

endogenously determined as firms with high level of spending in corporate expenditure

could have greater stock price informativeness. The instrument variable estimation

158

method is applied using a 2SLS regression to mitigate the econometrical problems

arising when the outcome variable (corporate expenditure) and the regressor (stock price

informativeness) are endogenous (Larcker & Rusticus, 2010). Endogeneity arises when

the independent variable (idiosyncratic volatility) is correlated with the residual term

(error term) and leads to inconsistent regression estimates (Lev & Sougiannis, 1996). An

appropriate instrument variable is chosen as a substitute for the independent variable

(idiosyncratic volatility) of regression indicated in Equation 4.6 as shown in item 4.6.1.

It would be a variable which is correlated with the original explanatory variable

(idiosyncratic volatility) but unrelated with the error term in the regression (Lev &

Sougiannis, 1996; Wooldridge, 2009, pp. 508-509).

It is often difficult to find a suitable instrument variable that is exogenous. Larcker and

Rusticus (2010) suggest that several instrument variables frequently used in the

accounting literature are not suitable, for instance, industry averages, ranked

endogenous regressors, or lagged endogenous regressors. Hamermesh (2000) opines

that a superior instrument should be beyond the control of the decision-makers and is

able to portray the behaviour of the population.

This study follows the Ferreira and Laux (2007) model by selecting all variables used as

instrument variables. This is because their study concludes that idiosyncratic volatility is

significantly correlated with the Gompers et al. (2003) governance index. The control

variables used in their model are: returns on equity, variance of return on equity,

159

leverage, market-to-book ratio, market capitalization, a dividend dummy, firm age and a

diversification dummy.

In the stage one regression, a predicted value of idiosyncratic volatility ( ) is

obtained by following the Ferreira and Laux (2007) model, that is, by regressing firms‟

idiosyncratic volatility, on the Gompers Governance index (GINDEX) and several

other explanatory variables for each firm-year as presented in Equation 4.8.

∑ ∑ (4.8)

where:

represents idiosyncratic volatility of the current year, t and GINDEX is the

governance index developed by Gompers et al. (2003) in the previous year, t-1. The

control variables included are ROE (return on equity), VROE (variance of return on

equity), LEV (leverage), MB (market-to-book ratio), (market capitalization), DIV

(dividend dummy), AGE (firm age) and DIVER (diversification dummy). Year and

Industry dummies are included in the model while is the error term. The symbol i and

t denote firms and yearly time index respectively.

160

In the second stage regression, Equation 4.9 is estimated using the fitted value of

idiosyncratic volatility, as a substitute for the actual value of firms‟ actual

value of idiosyncratic volatility, as follows:

∑ ∑ ∑

(4.9)

where:

represents corporate expenditure of firm i for the subsequent year, t+1 and

three proxies of corporate expenditures are employed in this study: R&D expenditure,

CAPEX and SGA costs. The coefficient is the intercept while coefficient is the

coeffient of interest. Variable is the fitted value of idiosyncratic volatility

and denotes a set of control variables. Year and Industry dummies are

included in the model and represents unspecified random factors.

It is crucial to impose exclusion restrictions on the model, which means that the

equation of the first and the second stage models generally contain different exogenous

variables (Wooldridge, 2009, p. 521). As such, several variables appearing in the first

stage model in Equation 4.8 such as firm age (AGE), leverage (LEV) and dividend

dummy (DIV) are excluded in the second stage regression in Equation 4.9. The variable

StdROE which is measured by standard deviation of ROE is also not applied as a

control variable in Equation 4.9 as it is closely related to another measure of volatility of

return on earnings, VROE represented by variance of ROE. Moreover, the variable

161

market-to-book ratio (MB) used in stage one model in Equation 4.8 is substituted by the

variable earning-to-price ratio (EP) in Equation 4.9. The variable GINDEX is not

suitable to serve as a corporate governance variable in Equation 4.9 as it is the

independent variable used in Equation 4.8 (stage one regression model), hence GINDEX

is replaced by variable percentage of independent directors (INDDIRPCT) in the stage

two regression.

Table 4.1 presents the definitions of variables used in this study and the sources of the

data. The variables are displayed across five panels: panels A to E define the dependent

variable (corporate expenditure variables), independent variable (stock price

informativeness variable), variables representing information asymmetry, control

variables and variables used for the two-stage least squares regression respectively.

162

Table 4.1 Definition of Variables

Variable Represented by Definition Source

of data

Panel A Dependent Variable - Corporate Expenditure Variables

Research and development

expenditure (value)

R&D (value) Research and development expenditure measured in US

dollar value

Compustat

Research and development

expenditure

R&D Natural logarithm of annual research and development

expenditure scaled by total assets

Compustat

Capital expenditure (value) CAPEX (value) Capital expenditure measured in US dollar value

Compustat

Capital expenditure CAPEX Natural logarithm of annual capital expenditure scaled by

total assets

Compustat

Selling, general and

administrative costs (value)

SGA(value) Selling, general and administrative costs measured in US

dollar value

Compustat

Selling, general and

administrative costs

SGA Natural logarithm of annual selling, general and

administrative costs scaled by total assets

Compustat

Panel B Independent Variable – Stock Price Informativeness Variables

Logistic relative idiosyncratic

volatility

ψ Annual logistic transformed relative idiosyncratic volatility

estimated from the market model

CRSP

Relative idiosyncratic volatility 1-R2

Annual relative idiosyncratic volatility given by the ratio of

idiosyncratic variances to total variance

CRSP

163

Table 4.1 Definition of Variables (Continued)


of data

Panel C Information Asymmetry Variable

Total assets AT Firm‟s total assets at the end of fiscal year in US dollar value

Compustat

Firm size SIZE Natural logarithm of total assets at the end of the fiscal year

Compustat

Analyst following ANALYST

Number of analysts that follow a firm I/B/E/S

Bid-ask spreads BIDASK Annual average bid ask spreads measured as the mean of

difference between closing ask and bid price, scaled by the

average of ask and bid price.

CRSP

Panel D Control Variables

Firm size SIZE Natural logarithm of total assets at the end of the fiscal year Compustat

Analyst following

ANALYST Number of analyst that follow a firm I/B/E/S

Firm age (years) FIRM AGE Number of years that the firm reported assets on Compustat

Compustat

Firm age (log) AGE

Natural logarithm of number of years that the firm reported

assets on Compustat

Compustat

Volatility of return on equity

StdROE

Standard deviation of a firm‟s annual return on equity over

the last three years

Compustat

164



of data

Cash flow volatility StdCF

Standard deviation of cash flow for the past five years

Compustat

Stock return volatility StdRET

Standard deviation of daily stock return over the past one

year

CRSP

Market-to-book ratio MB

Total market value of equity divided by book value of

shareholder funds at the end of fiscal year

Compustat

Return on assets ROA

Earnings before extraordinary items over total assets at the

end of fiscal year

Compustat

Working capital ratio WC

Working capital divided by sales

Compustat

Dividend dummy DIV

Annual dummy variable equals to “1” if firm pays dividend

during the fiscal year and “0” otherwise

Compustat

Merger dummy

MERGER

Dummy variable equals to “1” if there is merger or

acquisition activities during the fiscal year and “0” otherwise

Compustat

Restructuring dummy RESTRUCT

Dummy variable equals to “1” if there is restructuring

activities during the fiscal year and “0” otherwise

Compustat

Loss dummy LOSS

Dummy variable equals to “1” if firm is in loss financial

position during the fiscal year and “0” otherwise

Compustat

Leverage

LEV Total debts divided by total assets Compustat

165



of data

Diversification (business) DVS_BIZ Entropy measure of a firm‟s sales share in different lines of

business

Compustat

Diversification (geography)

DVS_GEO Entropy measure of a firm‟s sales share in different

geographic areas

Compustat

Free cash flow FCF

The operating income before depreciation minus interest

expenses, taxes, preferred dividend and ordinary dividend

and divided by total assets

Compustat

Employee intensity EMP Number of employees divided by total assets Compustat

Audit quality AUDIT Dummy variable equals to “1” if a firm is audited by an

industry-specialist auditor and “0” otherwise. Industry-

specialist auditor possesses the highest share of clients‟ total

assets in the two-digit SIC industry code.

Compustat

Earnings management EM

Firm-specific residuals from an industry regression of total

accruals on the reciprocals of total assets, revenue growth and

fixed assets using the Jones (1991) model

Compustat

Governance index GINDEX Gompers et al. (2003) index based on 24 corporate

governance provisions

Risk Metrics

166



of data

Panel E Two-stage Least Squares Regression Variable

Predicted value for idiosyncratic

volatility Fitted value for idiosyncratic volatility estimated based on the

Ferreira and Laux (2007) model

CRSP &

Compustat

Return on equity ROE Earnings before extraordinary items divided by equity

Compustat

Variance of return on equity VROE Sample variance of annual returns on equity over the last

three years

Compustat

Market capitalization MCAP Market value of firms‟ equity at the end of fiscal year Compustat

Diversification dummy DIVER Internal diversification dummy equals to “1” if there is

internal diversification and “0” otherwise

Compustat

Earnings-to-price ratio EP Earnings per share for firms at the end of the fiscal year,

scaled by the closing market price of firms‟ outstanding

shares nine months prior to its balance sheet date

Compustat &

CRSP

Percentage of independent

directors

INDDIRPCT Independent non-executive director as a percentage of all

directors on board

Execucomp

167

4.7 Sample Selection Procedure

Table 4.2 outlines the sample selection procedure to arrive at the final sample for each

of the corporate expenditure.

Table 4.2 Sample Selection Procedure

Description Firm-year

observations

Number of observations with idiosyncratic volatility

43,257

(-) financial institution (SIC code between 6000 and 6999) and

utilities (SIC code between 4900 and 4999)

(15,872)

Initial Sample

27,385

(-) observations without control variables

(10,765)

Number of observations with idiosyncratic volatility and control

variables (Years 2003-2011)

16,620

After deleting missing value for each of the corporate expenditure:

Final Sample for H1, H2a (firm size) and H2b (analyst following)

Research & development expenditure

Capital expenditure

Selling, general and administrative costs

Sample size

8,513

15,443

14,318

After deleting missing value for bid ask spreads:

Final sample for H2c (bid-ask spreads)

Research & development expenditure

Capital expenditure

Selling, general and administrative costs

8,510

15,439

14,315

168

The annual idiosyncratic volatility is estimated using daily returns of US PLCs from

CRSP database for the years 2003 to 2009. Data prior to 2003 are excluded to minimise

any possible impact arising from the Sarbanes-Oxley Act executed in year 2002. After

excluding firms in the financial institutions (SIC code between 6000 and 6999) and

utilities (SIC code between 4900 and 4999) as these two sectors are regulated in nature,

an initial sample of 27,385 firm-year observations is obtained. This sample is then

reduced to 16,620 firm-year observations after merging with both Compustat (financial

data) and I/B/E/S database (analyst data), followed by removing observations without

control variables. The sample size is further reduced due to missing values for corporate

expenditure and the requirement to use lead value (t+1) of corporate expenditure. The

final sample for the study ranges from 8,513 (R&D expenditure) to 15,443 (CAPEX)

observations. This final sample is used for the examination of H1 and H2a (using firm

size as the proxy of information asymmetry) and H2b (using analyst following as the

proxy of information asymmetry).

A slightly smaller sample is obtained to investigate H2c when bid-ask spreads are used

as the third proxy of information asymmetry. The standard data filtering procedure

identified in the microstructure literature are employed to “clean” the data of trade and

quotes for errors and outliers (Huang & Stoll, 1996; Chung et al., 2010). This filtering

process would remove:

169

i) quotes if either the bid or ask price is negative,

ii) quotes if the bid-ask spread is more than USD4 or negative, and

iii) trades when the price or volume is negative.

4.8 Statistical Analyses

The following sections describe data screening, data analyses and data validation that is

performed in this study. Computer software SAS Version 9.3 is employed in this study

to facilitate data screening and data analysis process.

4.8.1 Data Screening

Data collected from a variety of databases is firstly reviewed for missing values, shape

of the data distribution and determining outliers. This process improves the chances of

data correctness by detecting and rectifying any possible apparent errors (O'Rourke,

Hatcher & Stepanski, 2009, p. 90).

Missing data occurs when the valid value of an observation of either the dependent

variable or any of the independent variables are not available. Observations with

missing information that cannot be subjected to data analysis are excluded in the

multiple regression analyses. Missing data reduces the sample size available from the

population and may result in less accurate estimation in the data analysis, but it does not

170

violate the random sampling assumption if the observation is missing at random

(Wooldridge, 2009, p. 332).

The shape of the data distribution depicts the extent to which the sample distribution

deviates from the normality by observing its skewness and kurtosis. It is preferred to

have data that is normally distributed28

to avoid inaccurate statistical inferences and

biased correlation coefficients (O'Rourke et al., 2009, p. 100). Hence, a distribution that

is not too skewed and without extreme kurtosis is favoured (Field, 2009, p. 138).

Skewness and kurtosis of a distribution can be inferred using a histogram or univariate

analysis. Skewness measures symmetric (or asymmetric) distribution by viewing its

long “tail” on either left or right side of the distribution. A skewness value of zero

indicates a symmetric distribution of the mean, a positive skewness value is related to a

right-skewed distribution while a negative skewed value shows a left-skewed

distribution (Cody, 2011, p. 28). Kurtosis, on the other hand, ascertains whether a

distribution is either taller and leaner or flatter than a normal distribution. A kurtosis

value of zero indicates a normal distribution, a positive value for kurtosis shows that the

distribution is too peaked (tall and lean) while a negative value depicts a flat distribution

(O'Rourke et al., 2009, p. 94). Univariate analysis such as use of Shapiro-Wilk statistics

can be used to examine the normality of the distribution (O'Rourke et al., 2009, p. 104).

28 A normal distribution is symmetrical and bell shaped distribution of values (O'Rourke et al., 2009, p.

99).

171

If a data is not normally distributed, a logarithm transformation is usually performed to

produce a distribution that is closer to normal (Wooldridge, 2009, p. 119).

A departure from normality can be caused by outliers too (O'Rourke et al., 2009, p. 102).

Outliers are extreme values that diverge significantly from the other values in the

distribution. The problem of outliers can be due to error in data entry or when one or

several members of the population behave differently from the rest of a small population.

The marked difference of outliers from the other observations may substantially affect

the outcome of the regression especially in small datasets (Wooldridge, 2009, p. 325).

Outliers can be identified by observing the extreme values of the variables analysed that

fall at the outer range (the highest or the lowest) of the distribution from a univariate

analysis. The outliers can also be viewed graphically by using a probability plot or box

plot (Cody, 2011, p. 37). Wooldridge (2009, p. 325) opines that whether to keep or to

drop outliers in a regression analysis is a difficult decision to make. The author proceeds

to suggest that certain function forms, for example, logarithm transformation of most

economic variables, significantly reduce the range of data and therefore are less

sensitive to outliers. Following empirical research in accounting and finance, all

variables are winsorised at the bottom and top one per cent levels in this study to

minimise outlier effects and spurious inferences.

172

4.8.2 Data Analyses

The screened data is then analysed using univariate and multivariate tests. Additional

tests are conducted to verify the robustness of the main model specified in item 4.6.1.

4.8.2.1 Univariate Tests

Descriptive statistics and Pearson correlations are used to obtain a basic understanding

of the data collected. Graphs are presented to illustrate the trends or associations among

the key variables in Figures 5.1 to 5.7 displayed in Chapter 5.

4.8.2.2 Multivariate Tests

This study employs multiple linear regressions using ordinary least squares (OLS)

estimation method to examine the hypotheses developed. OLS attempts to fit a

regression line through the observations in the sample to obtain the intercept and slope

estimates where the sum of squared residuals is the lowest. A t-test is used to determine

whether an alternative hypothesis, H1 can be supported. A p-value for an observed value

of t statistics represents the smallest significance level at which the alternative

hypothesis would be supported. The p-value of the t-test will be compared to the pre-

determined α-value and if the p-value is smaller than the α-value chosen, the alternative

hypothesis is supported (Wooldridge, 2009, pp. 120-124).

173

This study has fulfilled six assumptions required to obtain unbiased and efficient OLS

estimators. According to Wooldridge (2009, pp. 84-102), the six assumptions are

outlined as follows:

a) Linearity: the relationship between independent variables and dependent variable

is assumed to be linear.

b) Random sampling: observations are randomly drawn from the population and are

representative of the population.

c) No perfect collinearity: No perfect correlation should exist among the independent

variables in a model.29

Variables with a correlation value of 0.80 and above are

considered to be highly correlated while variance inflation factor (VIF) above cut-

off threshold of 10 signals multicollinearity problem (Field, 2009, p. 224).30

d) Zero conditional mean: the error term is unrelated to the independent variable(s).

e) Homoskedasticity: the variances of the error term are constant for any value of the

independent variables.

f) Normality: The error terms are assumed to be normally distributed.

It is important to measure the goodness-of-fit of a regression model. The coefficient of

determination, R2 of the regression is commonly used to assess how well the

29 Wooldridge (2009, pp. 98-99), however, comments that high correlations among control variables do

not affect the relationship between the main variables.

30 Wooldridge (2009, p. 99) warns that these cut-off value are arbitrarily determined and must be applied

with care.

174

independent variable (idiosyncratic volatility) explains the dependent variable (corporate

expenditure) in an OLS regression. The value of the R2

of a regression model sums up

how well the OLS regression fits the data, i.e., the percentage of the sample variance in

the dependent variable that can be explained by the regression model. The range of R2

value is between zero and one where higher value of R2 indicates a better fit of the OLS

regression model to the data (Wooldridge, 2009). The second measure of goodness-of-

fit generally used is the F statistic which determines the overall significance of a

regression model. When F statistic is large and significant (as the p-value is very small),

the null hypothesis is rejected and the regression model is concluded to be statistically

significant (O'Rourke et al., 2009, p. 232).

In this study, H1 is stated in an alternative form and it hypothesizes that the stock price

informativeness of a current year is negatively associated with corporate expenditure of

the subsequent year, while holding other variables constant. If the p-value of the t-test

generated from the computer statistical software is smaller than the α-value chosen, say

5%, then H1 is supported. This indicates that an association between a current year‟s

stock price informativeness and the subsequent year‟s corporate expenditure is

significant at 95% confidence level.

Hypothesis 2a conjectures that the relationship between a current year‟s stock price


for small firms. Multiple linear regressions are carried out on each group of the firm size

175

to examine whether the relationship examined is stronger for large or small firms.

Similar procedures are applied to examine H2b for analyst following and H2c for bid-

ask spreads.

Other challenges faced by accounting and finance researchers are the cross-sectional and

time series correlation prevalent in panel data sets (data sets that contain observations of

multiple firms in multiple years). Time series dependence occurs when the residuals of a

firm may be correlated across years for that firm resulting in unobserved firm effect, for

example, the observation of firm A in year t can be correlated with that of this firm

(Firm A) in year t+1. Cross-sectional correlation indicates the residuals of a year may be

correlated across varying firms giving rise to time effect, for example, the observation of

firm A in year t can be correlated with that of firm B in the same year (Petersen, 2009;

Gow, Ormazabal & Taylor, 2010). In econometrics and finance research, common

methods used to address these concerns are Fama and MacBeth (1973) procedure, the

Newey and West (1987) procedure and one way cluster-robust standard errors (Petersen,

2009; Gow et al., 2010). Fama-Macbeth (1973) procedure deals with the issue of

possible correlation in the cross-sectional error structure. It estimates cross-sectional

regressions for each period and inferences are made based on the mean and standard

deviation of the estimated coefficients. The Newey and West (1987) procedure is used

to mitigate both heteroskedasticity and time series autocorrelation. The one-way cluster-

robust standard errors, also known as the Huber-White standard errors or Rogers

standard errors, was proposed by White (1984) to adjust White‟s (1980) standard

176

errors31

to account for possible correlation within a cluster. The one-way cluster-robust

standard errors method performs clustering of standard errors along a cross-sectional

dimension, for example, in relation to firms, industries or countries. Alternatively, the

one-way cluster-robust standard errors method can be performed along a time-series

dimension such as year. However, Petersen (2009) opines that these three methods,

being the Fama and MacBeth (1973) procedure, the Newey and West (1987) procedure

and the one way cluster-robust standard errors consider either cross-sectional or time-

series correlation separately, but not simultaneously. Thompson (2011) criticises that the

standard errors produced by the one-way clustering method do not adjust correctly for

simultaneous correlation across both firms and time span.

The econometrics literature demonstrates that the two-way cluster-robust standard errors

method is robust to both time-series and cross-sectional correlation (Petersen, 2009;

Cameron, Gelbach & Miller, 2011; Thompson, 2011). Petersen (2009) found the

coefficient estimated using the two-way cluster-robust standard error method account

correctly for the presence of both time and firm effect in panel data sets, thus generating

unbiased standard errors when there are sufficient number of clusters in each dimension.

Unbiased standard error is crucial for research using standard hypothesis testing to

31 The White‟s (1980) variance correction method are used to mitigate heteroskedasticity problem in the regressions.

Heteroskedasticity occurs when the variance of the error term is not constant.

177

arrive at correct statistical inferences as any moderate changes in standard errors can

have a significant impact on statistical inferences (Cameron et al., 2011).

According to Cameron et al. (2011) and Thompson (2011), the covariance estimator is

equal to the estimator that clusters by firm plus the estimator that clusters by time, and

minus the heteroskedasticity-robust OLS variance matrix (White, 1980). The standard

errors can be calculated using any statistical package with a clustering command, such

as SAS or Stata.

Gow et al. (2010) evaluate the appropriateness of other approaches applied in empirical

accounting research such as the Fama-Macbeth-i, Z2 statistics and Newey-West

corrected Fama-Macbeth statistics. Fama-Macbeth-i is a modification of the Fama and

MacBeth (1973) procedure which involves estimation of time-series regressions for each

firm and makes inferences based on the mean and standard deviation of the coefficients.

Z2 statistics is firstly used in Barth (1994) to adjust for cross-sectional and serial

correlation. The Newey-West corrected Fama-Macbeth (FM-NW) procedure is used to

modify the Fama and MacBeth (1973) approach. It applies the Newey and West (1987)

procedure to the time-series of coefficient estimates and adjusts the standard errors to

mitigate time series autocorrelation. Gow et al. (2010) find that these approaches

examined do not correct for both cross-sectional and time-series correlation, leading to

misspecified test statistics and materially varying research inferences. They conclude

178

that the two-way cluster-robust standard errors method is required to generate well-

founded research conclusions particularly in the area of corporate finance.

In this study, the White‟s (1980) variance correction method is used to avoid the

hetereoskedasticity inherent in the regressions. Statistical tests are also carried out based

on the Petersen (2009) clustering method to control for clustered standard errors by both

firm and year, hence cross-sectional and time-series correlation are accounted for. These

tests are conducted using SAS, a standard econometric software package. Two other

procedures are also conducted as additional test to verify the robustness of statistical

tests conducted, namely:

a) The Fama and MacBeth (1973) procedure to control for the possible correlation

in the cross-sectional error structure; and

b) The Newey-West corrected Fama-Macbeth (FM-NW) procedure to mitigate time

series autocorrelation.

4.8.2.3 Additional Tests

The following additional tests are conducted to examine the robustness of the model

specified in this study:

a) using different measures of idiosyncratic volatility generated using methods

outlined in item 4.5.2, namely,

179

(i) different market index,

(ii) Fama & French three factor model (Fama & French, 1993, 1995, 1996), and

(iii) Brockman and Yan‟s (2009) model,

b) exclude 2008 year data to examine whether the association between current year‟s

stock price informativeness and subsequent year‟s corporate expenditure is driven

by the global financial crisis

4.9 Chapter Summary

This chapter (Chapter 4) outlines the research methodology used in this study. A

quantitative research method is adopted within the positivism paradigm. This chapter

describes the principal population data as well as sources of the secondary data. The

measurement of variables are then elaborated, followed by a detailed overview of the

model specification and sample selection procedure undertaken to examine four

hypotheses developed in Chapter 3. Finally, the statistical analysis conducted in this

study is delineated.

The next chapter, Chapter 5 presents the findings followed by a discussion of results of

the current study.

180

CHAPTER 5

RESEARCH FINDINGS AND DISCUSSION

5.1 Introduction

The previous chapter (Chapter 4) presents the research methodology employed in this

study. The research paradigm adopted in this study is firstly elaborated, followed by a

description of the principal population data, sources of secondary data and variables

measurement. The chapter then presents an overview of the model specification, sample

selection procedure as well as the statistical methodology conducted for this study.

This chapter (Chapter 5) provides the research findings followed by a discussion of the

results. After the introductory note, Section 5.2 describes the univariate results while

Section 5.3 presents and discusses the multivariate results with regards to the association

between stock price informativeness and corporate expenditure. Section 5.4 exhibits

how this association observed between stock price informativeness and corporate

expenditure is dependent on information asymmetry, proxied by firm size, analyst

following and bid-ask spreads. Section 5.5 summarises the results of additional tests

conducted in verifying the robustness of the model applied. A summary of this chapter

is presented in Section 5.6.

181

5.2 Univariate Results

Univariate results are reported in the following manner:

a) use of descriptive statistics of the full sample to explain corporate expenditure

which are represented by R&D, CAPEX and SGA,

b) explain the Pearson correlations between variables used in the study, and

c) provide descriptive statistics of each corporate expenditure by segregating the

full sample of the study into two sub-groups using three proxies of information

asymmetry, namely, firm size, analyst following and bid-ask spreads.

5.2.1 Descriptive Statistics of Corporate Expenditure

Table 5.1 presents the descriptive statistics of each of the corporate expenditure in US

dollars (USD) for the sample of this study for the years 2004 to 2010. The descriptive

statistics are presented for each individual year as well as for the full sample.

182

Table 5.1 US Corporate Expenditure of Years 2004 - 2010 (in USD million)

Year N Mean Lower

Quartile

Median Upper

Quartile

Standard

Deviation

Research & Development Expenditure

2004 1,238 126.42 7.26 21.08 61.00 545.93

2005 1,249 132.51 7.30 21.84 61.27 548.43

2006 1,207 161.29 8.09 24.80 68.30 639.91

2007 1,210 166.39 8.53 26.01 75.70 637.13

2008 1,193 178.68 8.74 26.68 81.82 681.18

2009 1,211 156.98 8.04 24.69 71.36 625.77

2010 1,205 177.21 8.51 26.08 75.94 727.52

Full Sample 8,513

156.80

8.00

24.40

70.91

631.63

Capital Expenditure

2004 2,090 164.83 3.92 17.06 74.14 979.71

2005 2,139 191.41 4.08 18.92 85.94 1,042.10

2006 2,097 235.94 4.78 22.23 102.50 1,307.83

2007 2,098 257.09 4.98 23.11 113.46 1,159.64

2008 2,151 278.29 5.23 24.34 123.00 1,259.52

2009 2,171 200.95 3.88 17.68 85.86 1,021.77

2010 2,220 232.18 5.02 21.57 97.80 1,159.34

Full Sample 14,966

223.06

4.56

20.68

95.37

1,138.89

183

Table 5.1 US Corporate Expenditure of Years 2004-2010 (in USD million) (Continued)

Year N Mean Lower

Quartile

Median Upper

Quartile

Standard

Deviation

Selling, General & Administrative Costs

2004 1,944 566.34 41.03 103.44 306.18 2,183.05

2005 1,989 584.60 41.34 109.95 334.38 2,267.47

2006 1,952 657.58 45.94 124.78 362.61 2,524.58

2007 1,949 713.74 47.39 130.15 401.90 2,732.19

2008 2,004 740.49 48.28 135.92 423.21 2,847.01

2009 2,019 693.66 45.01 121.20 381.20 2,819.91

2010 2,067 735.33 45.85 128.20 417.00 2,934.21

Full Sample 13,924

670.99

44.89

120.51

370.88

2,633.85

184

Figure 5.1 depicts the movement of each of the three corporate expenditures during the

years 2004 to 2010.

Figure 5.1 Trend Analysis of US Corporate Expenditure from 2004-2010

The graphs presented in Figure 5.1 reveal that SGA costs have the highest monetary

value among the three corporate expenditures. R&D costs on average forms about 25

per cent of SGA costs. Trend analyses portray that the mean of R&D expenditure

increases from year 2004 to 2010 except in 2009 where firms experienced an average

decline of 12 per cent or USD 21.70 million. A similar trend is also found in CAPEX

and SGA costs. A general reduction of 28 per cent (USD 77.34 million) and six per cent

(USD 46.83 million) are registered in CAPEX and SGA costs respectively in year 2009.

-

100

200

300

400

500

600

700

800

2004 2005 2006 2007 2008 2009 2010

USD

mill

ion

Year

SGA Costs

CAPEX

R&D Costs

185

This reflects the global financial crisis that took place in the year 2008 has influenced

managerial decisions in curbing firms‟ corporate expenditure budgets and consequently

led to a lower actual investment in R&D costs, CAPEX and SGA costs of the

subsequent year. An additional test is carried out in item 5.5.2 by excluding data for the

year 2008 from the sample and the results show that the empirical evidence found in this

study remains robust and is not driven by the impact of the 2008 global financial crisis.

Tables 5.2, 5.3 and 5.4 provide descriptive statistics for all the variables used in the

analysis of US corporate expenditure in relation to:

(a) research and development expenditure;

(b) capital expenditure; and

(c) selling, general and administrative costs respectively.

The sample for each of the corporate expenditure is divided into two sub-samples, that is,

when idiosyncratic volatility is increasing and when it is decreasing. The descriptive

statistics of these two sub-groups are presented simultaneously with the full sample in

Tables 5.2 to 5.4.

186

Table 5.2 Descriptive Statistics – R&D Expenditure

Full Sample

Variable N Mean Lower

Quartile

Median Upper

Quartile

Std Dev

R&D t+1(Value) 8,513 $156.80 $8.00 $24.40 $70.91 $631.63

R&D t+1 8,513 -2.943 -3.780 -2.749 -2.017 1.383

Ψ 8,513 1.909 0.856 1.527 2.569 1.667

1-R2 8,513 0.797 0.702 0.822 0.929 0.158

AT (Value) 8,513 $3,649.84 $114.51 $379.72 $1,587.09 $15,481.43

SIZE 8,513 6.121 4.741 5.939 7.370 1.909

ANALYST 8,513 7.154 2.000 5.000 10.000 6.585

BIDASK 8,510 0.006 0.001 0.002 0.006 0.010

FIRM AGE (Years) 8,513 20.457 10.000 15.000 25.000 14.956

AGE 8,513 2.783 2.303 2.708 3.219 0.677

StdROE 8,513 0.769 0.030 0.074 0.215 9.994

StdCF 8,513 90.215 5.127 13.993 43.762 388.443

StdRET 8,513 0.034 0.022 0.031 0.042 0.019

MB 8,513 3.988 1.574 2.519 4.125 68.048

ROA 8,513 -0.061 -0.081 0.032 0.078 0.352

WC 8,513 16.693 0.185 0.378 0.783 675.765

DIV 8,513 0.296 0.000 0.000 1.000 0.456

MERGER 8,513 0.081 0.000 0.000 0.000 0.273

RESTRUCT 8,513 0.408 0.000 0.000 1.000 0.491

LOSS 8,513 0.374 0.000 0.000 1.000 0.484

LEV 8,513 0.442 0.231 0.404 0.586 0.314

AUDIT 8,513 0.248 0.000 0.000 0.000 0.432

EM 8,513 -0.006 -0.041 0.007 0.051 0.191

DVS_GEO 7,194 0.750 0.275 0.707 1.132 0.557

DVS_BIZ 6,471 0.380 0.000 0.000 0.687 0.554

GINDEX 3,033 9.136 7.000 9.000 11.000 2.496

Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in

USD million.

187

Table 5.2 Descriptive Statistics – R&D Expenditure (Continued)

Increasing ψ Decreasing ψ

Variable N Mean Median Std Dev N Mean Median Std Dev

R&D t+1 (Value) 3,729 $175.63 $25.40 $683.62 4,784 $142.12 $23.61 $587.58

R&D t+1 3,729 -2.865 -2.670 1.359 4,784 -3.003 -2.808 1.398

Ψ 3,729 2.438 1.902 1.906 4,784 1.496 1.226 1.313

1-R2 3,729 0.846 0.870 0.133 4,784 0.758 0.773 0.164

AT (Value) 3,729 $3,843.45 $387.15 $16,209.85 4,784 $3,498.92 $375.27 $14,888.92

SIZE 3,729 6.123 5.959 1.949 4,784 6.119 5.928 1.878

ANALYST 3,729 7.489 5.000 7.163 4,784 6.894 5.000 6.084

BIDASK 3,728 0.006 0.002 0.009 4,782 0.005 0.002 0.010

FIRM AGE

(Years)

3,729 20.431 15.000 14.825 4,784 20.476 15.000 15.059

AGE 3,729 2.788 2.708 0.665 4,784 2.779 2.708 0.686

StdROE 3,729 0.889 0.079 12.510 4,784 0.675 0.071 7.467

StdCF 3,729 90.139 14.399 401.265 4,784 90.275 13.810 378.189

StdRET 3,729 0.034 0.029 0.023 4,784 0.035 0.032 0.016

MB 3,729 3.382 2.565 20.709 4,784 4.461 2.482 88.915

ROA 3,729 -0.070 0.027 0.380 4,784 -0.054 0.037 0.329

WC 3,729 23.988 0.398 878.253 4,784 11.006 0.361 459.801

DIV 3,729 0.294 0.000 0.455 4,784 0.298 0.000 0.457

MERGER 3,729 0.089 0.000 0.284 4,784 0.075 0.000 0.263

RESTRUCT 3,729 0.421 0.000 0.494 4,784 0.397 0.000 0.489

LOSS 3,729 0.393 0.000 0.488 4,784 0.359 0.000 0.480

LEV 3,729 0.439 0.400 0.330 4,784 0.444 0.406 0.301

AUDIT 3,729 0.256 0.000 0.436 4,784 0.242 0.000 0.428

EM 3,729 -0.006 0.004 0.179 4,784 -0.006 0.008 0.200

DVS_GEO 3,151 0.755 0.708 0.555 4,043 0.746 0.705 0.559

DVS_BIZ 2,838 0.363 0.000 0.556 3,633 0.393 0.000 0.551

GINDEX 1,522 9.064 9.000 2.474 1,511 9.209 9.000 2.516

Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in

USD million.

188

Table 5.3 Descriptive Statistics – Capital Expenditure

Full Sample


Quartile

Median Upper

Quartile

Std Dev

CAPEXt+1 (Value) 14,966 $223.06 $4.56 $20.68 $95.37 $1,138.89

CAPEXt+1 14,966 -3.485 -4.113 -3.470 -2.816 1.042

Ψ 14,966 1.855 0.824 1.473 2.491 1.658

1-R2 14,966 0.791 0.695 0.814 0.923 0.158

AT (Value) 14,966 $3,634.68 $171.32 $571.69 $1,988.03 $13,760.20

SIZE 14,966 6.432 5.144 6.349 7.595 1.793

ANALYST 14,966 7.267 2.000 5.000 10.000 6.485

BIDASK 14,962 0.005 0.001 0.002 0.005 0.010

FIRM AGE (Years) 14,966 20.659 10.000 15.000 27.000 14.544

AGE 14,966 2.800 2.303 2.708 3.296 0.675

StdROE 14,966 0.924 0.023 0.056 0.150 52.873

StdCF 14,966 87.982 6.096 17.108 53.319 324.939

StdRET 14,966 0.033 0.021 0.029 0.040 0.017

MB 14,966 4.293 1.453 2.261 3.705 49.083

ROA 14,966 -0.009 -0.016 0.041 0.082 0.218

WC 14,966 9.955 0.093 0.229 0.495 509.067

DIV 14,966 0.365 0.000 0.000 1.000 0.482

MERGER 14,966 0.075 0.000 0.000 0.000 0.263

RESTRUCT 14,966 0.328 0.000 0.000 1.000 0.469

LOSS 14,966 0.291 0.000 0.000 1.000 0.454

LEV 14,966 0.454 0.284 0.454 0.605 0.213

AUDIT 14,966 0.257 0.000 0.000 1.000 0.437

EM 14,966 0.000 -0.036 0.007 0.049 0.141

FCF 14,966 0.039 0.029 0.075 0.117 0.201

DVS_GEO 12,208 0.574 0.000 0.523 0.984 0.560

DVS_BIZ 11,278 0.363 0.000 0.000 0.675 0.517

GINDEX 5,204 9.047 7.000 9.000 11.000 2.498

Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are

stated in USD million.

189

Table 5.3 Descriptive Statistics – Capital Expenditure (Continued)



CAPEXt+1 (Value) 6,313 $215.25 $19.81 $1,222.36 8,653 $228.77 $21.36 $1,073.94

CAPEXt+1 6,313 -3.520 -3.486 1.015 8,653 -3.459 -3.456 1.061

Ψ 6,313 2.401 1.861 1.913 8,653 1.457 1.202 1.306

1-R2 6,313 0.844 0.865 0.133 8,653 0.752 0.769 0.164

AT (Value) 6,313 $3,739.13 $568.77 $14,433.99 8,653 $3,558.48 $573.70 $13,247.35

SIZE 6,313 6.417 6.343 1.825 8,653 6.442 6.352 1.770

ANALYST 6,313 7.531 5.000 6.904 8,653 7.074 5.000 6.153

BIDASK 6,311 0.005 0.002 0.009 8,651 0.005 0.002 0.010

FIRM AGE

(Years)

6,313 20.816 15.000 14.558 8,653 20.545 15.000 14.534

AGE 6,313 2.812 2.708 0.666 8,653 2.791 2.708 0.681

StdROE 6,313 1.637 0.058 81.161 8,653 0.404 0.054 5.402

StdCF 6,313 86.257 17.161 333.291 8,653 89.240 17.098 318.720

StdRET 6,313 0.032 0.027 0.019 8,653 0.033 0.030 0.016

MB 6,313 3.975 2.314 15.629 8,653 4.525 2.228 63.156

ROA 6,313 -0.018 0.037 0.237 8,653 -0.001 0.044 0.203

WC 6,313 14.497 0.244 675.109 8,653 6.641 0.221 340.159

DIV 6,313 0.362 0.000 0.481 8,653 0.368 0.000 0.482

MERGER 6,313 0.082 0.000 0.274 8,653 0.070 0.000 0.255

RESTRUCT 6,313 0.348 0.000 0.476 8,653 0.313 0.000 0.464

LOSS 6,313 0.314 0.000 0.464 8,653 0.274 0.000 0.446

LEV 6,313 0.449 0.447 0.213 8,653 0.458 0.459 0.213

AUDIT 6,313 0.262 0.000 0.440 8,653 0.254 0.000 0.435

EM 6,313 -0.002 0.005 0.133 8,653 0.002 0.009 0.146

FCF 6,313 0.029 0.071 0.221 8,653 0.047 0.078 0.184

DVS_GEO 5,163 0.579 0.527 0.558 7,045 0.571 0.520 0.562

DVE_BIZ 4,795 0.354 0.000 0.523 6,483 0.370 0.000 0.513

GINDEX 2,565 9.002 9.000 2.496 2,639 9.091 9.000 2.500

Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in

USD million.

190

Table 5.4 Descriptive Statistics – Selling, General and Administrative Costs

Full Sample


Quartile

Median Upper

Quartile

Std Dev

SGAt+1 (Value) 13,780 $675.70 $45.19 $121.72 $374.94 $2,646.73

SGAt+1 13,780 -1.586 -2.082 -1.442 -0.942 0.961

Ψ 13,780 1.808 0.809 1.451 2.441 1.545

1-R2 13,780 0.788 0.692 0.810 0.920 0.158

AT (Value) 13,780 $3,686.18 $182.26 $593.86 $2,018.73 $14,148.17

SIZE 13,780 6.468 5.205 6.387 7.610 1.769

ANALYST 13,780 7.304 2.000 5.000 10.000 6.543

BIDASK 13,777 0.005 0.001 0.002 0.005 0.010

FIRM AGE (Years) 13,780 20.959 10.000 15.000 28.000 14.676

AGE 13,780 2.814 2.303 2.708 3.332 0.676

StdROE 13,780 0.489 0.023 0.054 0.142 8.691

StdCF 13,780 89.851 6.215 17.474 53.809 333.856

StdRET 13,780 0.032 0.021 0.028 0.040 0.017

MB 13,780 4.133 1.439 2.228 3.626 50.784

ROA 13,780 0.006 -0.008 0.044 0.084 0.184

WC 13,780 0.707 0.096 0.224 0.459 13.857

DIV 13,780 0.373 0.000 0.000 1.000 0.484

MERGER 13,780 0.077 0.000 0.000 0.000 0.267

RESTRUCT 13,780 0.340 0.000 0.000 1.000 0.474

LOSS 13,780 0.271 0.000 0.000 1.000 0.445

LEV 13,780 0.454 0.286 0.453 0.603 0.211

AUDIT 13,780 0.258 0.000 0.000 1.000 0.438

EM 13,780 0.001 -0.036 0.007 0.048 0.121

EMP 13,780 0.007 0.002 0.004 0.007 0.019

DVS_GEO 11,543 0.589 0.000 0.544 0.997 0.561

DVS_BIZ 10,327 0.377 0.000 0.000 0.685 0.522

GINDEX 4,922 9.081 7.000 9.000 11.000 2.512

Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are

stated in USD million.

191

Table 5.4 Descriptive Statistics – Selling, General and Administrative Costs

(Continued)



SGAt+1 (Value) 5,809 $741.49 $127.58 $2,790.27 7,971 $627.75 $118.15 $2,536.12

SGAt+1 5,809 -1.519 -1.397 0.919 7,971 -1.636 -1.473 0.988

ψ 5,809 2.315 1.826 1.720 7,971 1.438 1.186 1.284

1-R2 5,809 0.841 0.861 0.133 7,971 0.750 0.766 0.164

AT (Value) 5,809 $3,819.08 $597.61 $14,884.76 7,971 $3,589.34 $592.49 $13,586.36

SIZE 5,809 6.459 6.393 1.801 7,971 6.475 6.384 1.746

ANALYST 5,809 7.593 5.000 6.979 7,971 7.093 5.000 6.198

BIDASK 5,807 0.005 0.002 0.009 7,970 0.005 0.002 0.010

FIRM AGE

(Years)

5,809 21.111 15.000 14.695 7,971 20.849 15.000 14.662

AGE 5,809 2.825 2.708 0.668 7,971 2.806 2.708 0.681

StdROE 5,809 0.629 0.056 11.676 7,971 0.388 0.053 5.588

StdCF 5,809 88.558 17.508 344.571 7,971 90.794 17.429 325.843

StdRET 5,809 0.031 0.027 0.018 7,971 0.033 0.030 0.016

MB 5,809 3.733 2.277 13.915 7,971 4.424 2.197 65.707

ROA 5,809 -0.002 0.040 0.193 7,971 0.012 0.046 0.177

WC 5,809 0.724 0.237 8.467 7,971 0.695 0.217 16.725

DIV 5,809 0.370 0.000 0.483 7,971 0.374 0.000 0.484

MERGER 5,809 0.084 0.000 0.278 7,971 0.072 0.000 0.259

RESTRUCT 5,809 0.360 0.000 0.480 7,971 0.325 0.000 0.469

LOSS 5,809 0.292 0.000 0.455 7,971 0.256 0.000 0.436

LEV 5,809 0.448 0.445 0.211 7,971 0.458 0.460 0.211

AUDIT 5,809 0.264 0.000 0.441 7,971 0.254 0.000 0.435

EM 5,809 -0.001 0.004 0.128 7,971 0.003 0.009 0.116

EMP 5,809 0.007 0.004 0.020 7,971 0.007 0.004 0.018

DVS_GEO 4,890 0.595 0.554 0.558 6,653 0.584 0.538 0.564

DVS_BIZ 4,383 0.367 0.000 0.527 5,944 0.384 0.000 0.518

GINDEX 2,421 9.032 9.000 2.507 2,501 9.129 9.000 2.517

Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in

USD million.

192

The descriptive statistics reported in Tables 5.2 to 5.4 suggest that the means of the

monetary values for all three corporate expenditures of the subsequent year are on

average higher than their median. This phenomenon reflects skewness of the distribution

on the right tail and therefore, a logarithm transformation is performed to “normalize”

the sample distribution (Wooldridge, 2009, p. 119). The mean values of relative

idiosyncratic volatility (1-R2) range from 0.788 (SGA costs) to 0.797 (R&D costs) while

its medians range from 0.810 (SGA costs) to 0.822 (R&D costs). The relative

idiosyncratic volatility (1-R2) of this study is computed using the same specification of

the market model used by Ferreira and Laux (2007), i.e., based on the correlation

between firms‟ daily stock return and return of the corresponding market. The relative

idiosyncratic volatility is higher when firms‟ stock returns do not move simultaneously

with the market, reflecting higher informativeness of stock prices. The value of 1-R2

computed in this study is lower when compared to the reported mean and median values

of 1-R2 by Ferreira and Laux‟s (2007),

32 that is, 0.854 and 0.907 respectively.

Nevertheless, the standard deviation of 1-R2 reported in this study, that is, 0.158 for each

of the corporate expenditure is comparable to 0.155 reported by Ferreira and Laux

(2007).

32 It is possible that the value of 1-R

2 is lower in this study compared to Ferreira and Laux (2007) because

of different sample size and years covered. Ferreira and Laux (2007) use a total of 161,691 observations

for the years 1990 to 2001 while the sample sizes of this study range from 8,513 (R&D costs) to 14,966

(CAPEX) for the years 2003 to 2009.

193

This study compares two sub-samples with opposing direction of the movement in

idiosyncratic volatility. The mean and median level of the relative idiosyncratic

volatility, 1-R2, is higher for the group that is experiencing improvement in idiosyncratic

volatility for all three proxies of corporate expenditure when compared to the group with

declining idiosyncratic volatility. For both R&D costs and CAPEX groups, the mean

value of market-to-book ratio (representing growth) is higher but the working capital

ratio (indicating “slack” resources) is lower for the sub-sample showing a deterioration

in idiosyncratic volatility. However, there are no significant differences noted between

these two sub-samples for all three types of corporate expenditure, in terms of their

monetary value of the subsequent year, firm size, analyst following, bid-ask spreads and

firm age.

5.2.2 Pearson Correlations

Tables 5.5 to 5.7 present the matrix of Pearson pair-wise correlations between all

variables used in the analysis for the three proxies of corporate expenditure, namely,

R&D expenditure, CAPEX and SGA costs respectively.

194

Table 5.5 Pearson Correlations – R&D Expenditure

Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

N 8,513 8,513 8,513 8,513 8,510 8,513 8,513 8,513 8,513 8,513 8,513

(1) R&Dt+1 8,513 1.000 0.247a

-0.439a

-0.050a

0.155a

-0.348a

0.030a

-0.142a

0.256a

0.017 -0.362a

(2) ψ 8,513 1.000 -0.591a

-0.380a

0.501a

-0.275a

0.018c

-0.152a

0.281a

-0.005 -0.282a

(3) SIZE 8,513 1.000 0.661a

-0.467a

0.472a

-0.024b

0.428a

-0.450a

-0.001 0.374a

(4) ANALYST 8,513 1.000 -0.346a

0.159a

-0.029a

0.334a

-0.292a

0.009 0.197a

(5) BIDASK 8,510 1.000 -0.155a

0.018 -0.095a

0.480a

-0.005 -0.286a

(6) AGE 8,513 1.000 0.009 0.223a

-0.283a

0.011 0.208a

(7) StdROE 8,513 1.000 -0.004 0.053a

0.008 -0.050a

(8) StdCF 8,513 1.000 -0.118a

0.002 0.072a

(9) StdRET 8,513 1.000 0.010 -0.391a

(10) MB 8,513 1.000 0.005

(11) ROA 8,513

1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10%

levels respectively.

195

Table 5.5 Pearson Correlations - R&D Expenditure (Continued)

Variable N (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22)

N 8,513 8,513 8,513 8,513 8,513 8,513 8,513 8,513 7,194 6,471 3,030

(1) R&Dt+1 8,513 0.034a -0.415a -0.036a -0.083a 0.401a -0.104a -0.049a -0.087a 0.050a -0.349a -0.197a

(2) ψ 8,513 0.015 -0.252a -0.102a -0.200a 0.277a -0.047a -0.118a -0.014 -0.195a -0.235a -0.098a

(3) SIZE 8,513 -0.015 0.442a 0.150a 0.312a -0.402a 0.182a 0.183a 0.009 0.231a 0.448a 0.169a

(4) ANALYST 8,513 0.000 0.142a 0.082a 0.104a -0.256a 0.027b 0.122a -0.018c 0.158a 0.150a -0.058a

(5) BIDASK 8,510 -0.004 -0.158a -0.077a -0.093a 0.293a 0.029a -0.107a -0.005 -0.112a -0.146a -0.093a

(6) AGE 8,513 -0.017 0.500a 0.036a 0.163a -0.290a 0.154a 0.041a 0.069a 0.107a 0.401a 0.332a

(7) StdROE 8,513 -0.001 -0.035a -0.016 0.005 0.038a 0.097a -0.010 -0.012 0.026b -0.002 0.014

(8) StdCF 8,513 -0.004 0.191a 0.033a 0.073a -0.103a 0.085a 0.081a 0.010 0.074a 0.195a -0.021

(9) StdRET 8,513 0.003 -0.300a -0.037a -0.036a 0.422a 0.089a -0.103a -0.116a -0.070a -0.227a -0.223a

(10) MB 8,513 -0.000 -0.004 -0.006 0.005 0.014 0.012 0.013 -0.019c 0.020c 0.014 0.008

(11) ROA 8,513 -0.009 0.186a 0.030a 0.021b -0.515a -0.348a 0.056a 0.281a 0.104a 0.156a 0.081a

(12) WC 8,513 1.000 -0.016 -0.007 0.017 0.031a -0.028b -0.013 -0.001 -0.046a -0.018 -0.023

(13) DIV 8,513 1.000 0.000 0.118a -0.315a 0.151a 0.047a 0.049a 0.024b 0.345a 0.279a

(14) MERGER 8,513 1.000 0.130a -0.019c 0.013 0.020b -0.040a 0.034a 0.043a 0.016

(15) RESTRUCT 8,513 1.000 0.037a 0.172a 0.104a -0.022b 0.179a 0.146a 0.078a

(16) LOSS 8,513 1.000 0.084a -0.045a -0.202a -0.048a -0.231a -0.104a

(17) LEV 8,513 1.000 0.012 -0.142a -0.014 0.130a 0.183a

(18) AUDIT 8,513 1.000 -0.013 0.089a 0.066a 0.045b

(19) EM 8,513 1.000 0.006 0.044a 0.021

(20) DVS_GEO 7,194 1.000 -0.049a -0.065a

(21) DVS_BIZ 6,471 1.000 0.245a

(22) GINDEX 3,033 1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels respectively.

196

Table 5.5 shows that idiosyncratic volatility is positively related to subsequent year‟s

R&D costs. The correlation values between all variables are found to be lower than 0.80,

thus they are not highly correlated with each other. There is also no indication of

multicollinearity problem as the variance inflation factors (VIF) of the relevant

regressions are found to be less than the cut-off value of 10. It is also observed that

idiosyncratic volatility is negatively related to firm size indicating that a greater amount

of private information is impounded in stock prices of small firms. This is consistent

with the empirical findings of Chen et al. (2007) and Bakke and Whited (2010). Further,

idiosyncratic volatility is found to be negatively associated with analyst following and

positively associated with bid-ask spreads in the R&D costs sample, in line with

findings by Chen et al. (2007).

A summary of information reported in Table 5.5 reveals that higher R&D investment of

the subsequent year is observed in firms that are smaller, younger and less diversified.

Firms with higher R&D investment portray greater return volatility and higher leverage.

They are also more likely to incur losses while less likely to pay dividend. The sign of

variables such as return on assets (ROA), loss dummy (LOSS), dividend dummy (DIV),

leverage (LEV) and business diversification level (DVS_BIZ) in Table 5.5 is consistent

with the prediction made in item 4.5.4.1. The correlations between R&D costs and

variables such as firm size (SIZE) and firm age (AGE) reported in Table 5.5 are not as

expected in item 4.5.4.1.

197

Table 5.6 presents the Pearson correlations values between all variables used in the

analysis for the CAPEX sample.

In Table 5.6, idiosyncratic volatility is negatively related to CAPEX of the subsequent

year. All variables examined for the CAPEX sample are not highly correlated with each

other as the correlation values shown are not greater than 0.80. One exception is the

correlation value reported between two control variables, i.e., return on assets (ROA)

and free cash flow (FCF) that is at 0.875. Wooldridge (2009, pp. 98-99) opines that high

correlations among control variables do not affect the relationship between the

dependent (CAPEX of the subsequent year) and independent variable (idiosyncratic

volatility). As an alternative, the VIF of the relevant regressions are examined and they

are all found to be less than the cut-off value of 10, indicating no sign of

multicollinearity problem.

It is also noted that idiosyncratic volatility for CAPEX sample is negatively related to

firm size and analyst following but positively related to bid-ask spreads. These results

provide empirical support to the research findings by Chen et al. (2007) and Bakke and

Whited (2010) when they conclude that greater private information is integrated in stock

prices of small firms, firms with low analyst following and high bid-ask spreads.

198

Table 5.6 Pearson Correlations – Capital Expenditure

Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

N 14,966 14,966 14,966 14,966 14,962 14,966 14,966 14,966 14,966 14,966 14,966 14,966

(1) CAPEXt+1 14,966 1.000 -0.100a

0.201a

0.159a

-0.153a

0.060a

0.006 0.075a

-0.180a

-0.002 0.219a

-0.011

(2) ψ 14,966 1.000 -0.549a

-0.362a

0.468a

-0.233a

0.018b

-0.160a

0.158a

-0.002 -0.238a

0.012

(3) SIZE 14,966 1.000 0.634a

-0.421a

0.411a

-0.004 0.448a

-0.398a

-0.010 0.333a

-0.016b

(4) ANALYST 14,966 1.000 -0.320a

0.131a

-0.010 0.341a

-0.284a

0.007 0.199a

-0.001

(5) BIDASK 14,962 1.000 -0.117a

0.017b

-0.097a

0.457a

-0.005 -0.267a

-0.003

(6) AGE 14,966 1.000 -0.017b

0.221a

-0.241a

0.004 0.177a

-0.014c

(7) StdROE 14,966 1.000 -0.002 0.010 0.015c

-0.006 -0.000

(8) StdCF 14,966 1.000 -0.114a

0.004 0.070a

-0.004

(9) StdRET 14,966

1.000 00.013 -0.391a

0.004

(10) MB 14,966

1.000 -0.019b

-0.000

(11) ROA 14,966

1.000 -0.031a

(12) WC 14,966

1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels

respectively.

199

Table 5.6 Pearson Correlations – Capital Expenditure (Continued)

(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)

N 14,966 14,966 14,966 14,966 14,966 14,966 14,966 14,966 12,208 11,278 5,204

(1) CAPEXt+1 14,966 0.137a

-0.064a

-0.130a

-0.204a

0.105a

-0.053a

-0.017a

0.259a

-0.088a

0.021a

0.037a

(2) ψ 14,966 -0.234a

-0.077a

-0.131a

0.223a

-0.108a

-0.107a

-0.002 -0.024a

-0.168a

-0.175a

-0.090a

(3) SIZE 14,966 0.414a

0.121a

0.210a

-0.328a

0.399a

0.181a

0.001 0.335a

0.146a

0.332a

0.152a

(4) ANALYST 14,966 0.143a

0.054a

0.054a

-0.227a

0.068a

0.129a

-0.022a

0.194a

0.122a

0.083a

-0.027b

(5) BIDASK 14,962 -0.148a

-0.052a

-0.051a

0.270a

-0.011 -0.091a

-0.005 -0.126a

-0.093a

-0.104a

-0.075a

(6) AGE 14,966 0.430a

0.024a

0.146a

-0.212a

0.179a

0.036a

0.054a

0.162a

0.105a

0.309a

0.314a

(7) StdROE 14,966 -0.010 -0.003 -0.005 0.018b

0.031a

-0.007 0.001 -0.006 -0.007 0.016c

0.012

(8) StdCF 14,966 0.181a

0.035a

0.082a

-0.077a

0.129a

0.080a

0.006 0.064a

0.083a

0.158a

0.014

(9) StdRET 14,966 -0.279a

-0.019b

-0.003 0.430a

-0.005 -0.088a

-0.066a

-0.316a

-0.033a

-0.177a

-0.164a

(10) MB 14,966 -0.009 -0.008 0.001 0.020b

0.069a

0.010 -0.024a

-0.028a

0.017c

0.008 0.006

(11) ROA 14,966 0.205a

0.009 -0.063a

-0.599a

-0.024a

0.050a

0.309a

0.875a

0.023b

0.113a

0.051a

(12) WC 14,966 -0.014c

-0.005 -0.011 0.030a

-0.024a

-0.010 -0.000 -0.039a

-0.029a

-0.015 -0.016

(13) DIV 14,966 1.000 -0.013 0.055a

-0.265a

0.209a

0.063a

0.048a

0.138a

-0.006 0.240a

0.227a

(14) MERGER 14,966 1.000 0.108a

-0.005 0.048a

0.003 -0.034a

-0.035a

0.045a

0.041a

0.021

(15) RESTRUCT 14,966 1.000 0.102a

0.161a

0.060a

-0.038a

-0.003

0.239a

0.157a

0.093a

(16) LOSS 14,966 1.000 0.008 -0.046a

-0.215a

-0.486a

0.015c

-0.138a

-0.072a

(17) LEV 14,966

1.000 0.043a

-0.071a

-0.020b

-0.057a

0.163a

0.172a

(18) AUDIT 14,966

1.000 -0.009 0.048a

0.056a 0.041

a 0.031

b

(19) EM 14,966

1.000 0.145a

0.006 0.035a 0.018

(20) FCF 14,966 1.000 -0.036b -0.100

a 0.033

b

(21) DVS_GEO 12,208

1.000 0.018c 0.005

(22) DVS_BIZ 11,278

1.000 0.204a

(23) GINDEX 5,204

1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10%

levels respectively.

200

Further, Table 5.6 reports that CAPEX of the subsequent year is positively associated

with several control variables such as firm size (SIZE), analyst following (ANALYST),

firm age (AGE), return on assets (ROA) as well as free cash flow (FCF). A negative

association is shown between CAPEX and dividend dummy (DIV), merger dummy

(MERGER), restructuring dummy (RESTRUCT) and loss dummy (LOSS). These

finding are consistent with predictions made in item 4.5.4.1.

Table 5.7 presents the Pearson correlations values between all variables for corporate

expenditure represented by SGA costs.

Table 5.7 reveal a positive correlation between a current year‟s idiosyncratic volatility

and the subsequent year‟s SGA costs. All variables examined for this sample are not

highly correlated to each other as their correlation values is lower than 0.80. There is

also no sign of multicollinearity because the VIF of the relevant regressions are found to

be less than the cut-off value of 10. Data reported in Table 5.7 also show that

idiosyncratic volatility is negative associated to firm size and analyst following while

positively associated to bid-ask spreads. These findings are in line with empirical studies

such as Chen et al. (2007) and Bakke and Whited (2010).

201

Table 5.7 Pearson Correlations – Selling, General and Administrative Costs

Variable N (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

N 13,780 13,780 13,780 13,780 13,777 13,780 13,780 13,780 13,780 13,780 13,780 13,780

(1) SGAt+1 13,780 1.000 0.246a

-0.426a

-0.172a

0.166a

-0.078a

0.029a

-0.148a

0.105a

0.013 -0.201a

0.002

(2) ψ 13,780 1.000 -0.566a

-0.368a

0.470a

-0.234a

0.029a

-0.162a

0.129a

-0.005 -0.221a

0.011

(3) SIZE 13,780 1.000 0.636a

-0.414a

0.396a

-0.027a

0.451a

-0.377a

-0.005 0.295a

-0.016c

(4) ANALYST 13,780 1.000 -0.311a

0.117a

-0.023a

0.342a

-0.275a

0.009 0.204a

-0.014c

(5) BIDASK 13,777 1.000 -0.108a

0.025a

-0.094a

0.447a

-0.008 -0.269a

-0.003

(6) AGE 13,780 1.000 -0.014c

0.215a

-0.230a

0.008 0.158a

-0.029a

(7) StdROE 13,780 1.000 -0.002 0.042a

0.019b

-0.041a

-0.000

(8) StdCF 13,780 1.000 -0.107a

0.005 0.064a

-0.009

(9) StdRET 13,780 1.000 0.008 -0.384a

0.014

(10) MB 13,780 1.000 -0.008 -0.001

(11) ROA 13,780

1.000 -0.042a

(12) WC 13,780

1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels respectively.

202

Table 5.7 Pearson Correlations – Selling, General and Administrative Costs (Continued)

N (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)

N 13,780 13,780 13,780 13,780 13,780 13,780 13,780 13,780 11,543 10,327 4,922

(1) SGAt+1 13,780 -0.206a

-0.022a

0.063a

0.159

-0.200a

-0.061a

-0.039a

0.113a

0.032a

-0.143a

-0.044a

(2) ψ 13,780 -0.236a

-0.077a

-0.138a

0.211a

-0.102a

0.110a

-0.003 0.061a

-0.170a

-0.177a

-0.088a

(3) SIZE 13,780 0.395a

0.117a

0.208a

-0.290a

0.397a

0.181a

-0.007 -0.068a

0.142a

0.319a

0.159a

(4) ANALYST 13,780 0.131a

0.054a

0.051a

-0.215a

0.059a

0.135a

-0.025a

-0.047a

0.123a

0.071a

-0.024c

(5) BIDASK 13,777 -0.142a

-0.051a

-0.052a

0.267a

-0.005

-0.091a

-0.010 0.035a

-0.091a

-0.096a

-0.079a

(6) AGE 13,780 0.429a

0.019b

0.138a

-0.187a

0.172a

0.035a

0.054a

0.016c

0.098a

0.302a

0.315a

(7) StdROE 13,780 -0.025a

-0.006 0.007 0.031a

0.064a

-0.012 -0.018 0.003 0.020b

-0.003 0.012

(8) StdCF 13,780 0.173a

0.034a

0.081a

-0.066a

0.127a

0.079a

0.003 -0.043a

0.083a

0.151a

0.013

(9) StdRET 13,780 -0.267a

-0.016c

0.003

0.421a

0.011

-0.086a

-0.117a

-0.012 -0.025a

-0.162a

-0.167a

(10) MB 13,780 -0.007 -0.007 0.008 0.015c

0.063a

0.012 -0.007

-0.001 0.018c

0.011 0.007

(11) ROA 13,780 0.191a

0.009

0.101a

-0.619a

-0.054a

0.048a

0.349a

0.016c

0.001

0.082a

0.044a

(12) WC 13,780 -0.027a

-0.008 -0.016c

0.047a

-0.021b

0.006 -0.002 -0.012 -0.008

-0.022b

-0.120a

(13) DIV 13,780 1.000 -0.020b

0.048a

-0.238a

0.205a

0.059a

0.051a

0.010 -0.014

0.229a

0.228a

(14) MERGER 13,780 1.000 0.108a

0.007

0.045a

0.003

-0.039a

-0.033a

0.046a

0.035a

0.019

(15) RESTRUCT 13,780 1.000 0.128a

0.162a

0.063a

-0.056a

-0.040a

0.233a

0.147a

0.095a

(16) LOSS 13,780 1.000 0.032a

-0.044a

-0.236a

-0.018b

-0.032a

-0.112a

-0.064a

(17) LEV 13,780

1.000 0.036a

-0.072a

0.009 -0.062a

0.166a

0.178a

(18) AUDIT 13,780 1.000 -0.004 0.008 0.056a 0.044

a 0.026

c

(19) EM 13,780 1.000 -0.001 0.001

0.037a

0.021

(20) EMP 13,780 1.000 -0.096a

-0.036a 0.016

(21) DVS_GEO 11,543

1.000 0.015 -0.000

(22) DVS_BIZ 10,327

1.000 0.207a

(23) GINDEX 4,922 1.000

Note: All variables are defined in Table 4.1. Superscripts a, b and c stand for statistical significance at a p-value of less than 1%, 5% and 10% levels

respectively.

203

Data reported in Table 5.7 suggests that SGA costs in the subsequent year is negatively

associated with several variables such as firm size (SIZE), analyst following

(ANALYST), firm age (AGE), cash flow volatility (StdCF), return on assets (ROA),

dividend dummy (DIV), leverage (LEV) and business diversification (DVS_BIZ). The

findings in Table 5.7 also show that SGA of the subsequent year is positively associated

with return volatility (StdRET), restructuring dummy (RESTRUCT) and employee

intensity (EMP). The sign of variables DIV, RESTRUCT, LOSS, LEV and EMP are

consistent with the predictions made in item 4.5.4.1.

5.2.3 Descriptive Statistics by Firm Size

The descriptive statistics of all variables used in the analysis by firm size of three

corporate expenditures, namely, R&D expenditure, CAPEX and SGA costs are

exhibited in Tables 5.8 to 5.10 respectively. These tables summarize the analysed data

for the full sample as well as two sub-samples being large and small firms. Large (small)

firms are firms that are above (below) the median value of the natural logarithm of the

total assets of the full sample.

204

Table 5.8 Descriptive Statistics – R&D Expenditure by Firm Size Full Sample Large Firms Small Firms

Variable N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev

R&D t+1 (Value) 8,513 $156.80 $24.40 $631.63 4,257 $295.43 $65.10 $871.21 4,256 $18.14 $11.17 $21.42

R&D t+1 8,513 -2.943 -2.749 1.383 4,257 -3.499 -3.354 1.298 4,256 -2.387 -2.251 1.233

ψ 8,513 1.909 1.527 1.667 4,257 1.061 1.019 0.890 4,256 2.756 2.391 1.824

1-R2 8,513 0.797 0.822 0.158 4,257 0.713 0.735 0.151 4,256 0.880 0.916 0.114

AT (Value) 8,513 $3,649.84 $379.72 $15,481.43 4,257 $7,160.69 $1,587.09 $21,323.19 4,256 $138.15 $114.50 $99.11

SIZE 8,513 6.121 5.939 1.909 4,257 7.650 7.370 1.325 4,256 4.592 4.741 0.926

ANALYST 8,513 7.154 5.000 6.585 4,257 10.547 9.000 7.316 4,256 3.761 3.000 3.189

BIDASK 8,510 0.006 0.002 0.010 4,254 0.002 0.001 0.003 4,256 0.009 0.005 0.012

FIRM AGE (Years) 8,513 20.457 15.000 14.956 4,257 26.508 19.000 17.363 4,256 14.404 12.000 8.524

AGE 8,513 2.783 2.708 0.677 4,257 3.047 2.944 0.698 4,256 2.518 2.485 0.537

StdROE 8,513 0.769 0.074 9.994 4,257 0.756 0.050 13.529 4,256 0.781 0.115 4.090

StdCF 8,513 90.215 13.993 388.443 4,257 171.281 40.654 537.115 4,256 9.130 5.637 11.598

StdRET 8,513 0.034 0.031 0.019 4,257 0.027 0.024 0.014 4,256 0.041 0.037 0.022

MB 8,513 3.988 2.519 68.048 4,257 4.469 2.507 87.376 4,256 3.507 2.539 40.327

ROA 8,513 -0.061 0.032 0.352 4,257 0.033 0.052 0.117 4,256 -0.156 -0.021 0.465

WC 8,513 16.693 0.378 675.765 4,257 9.878 0.271 485.526 4,256 23.509 0.561 823.193

DIV 8,513 0.296 0.000 0.456 4,257 0.462 0.000 0.499 4,256 0.129 0.000 0.336

MERGER 8,513 0.081 0.000 0.273 4,257 0.117 0.000 0.322 4,256 0.045 0.000 0.207

RESTRUCT 8,513 0.408 0.000 0.491 4,257 0.553 1.000 0.497 4,256 0.262 0.000 0.440

LOSS 8,513 0.374 0.000 0.484 4,257 0.203 0.000 0.402 4,256 0.545 1.000 0.498

LEV 8,513 0.442 0.404 0.314 4,257 0.505 0.506 0.237 4,256 0.378 0.293 0.365

AUDIT 8,513 0.248 0.000 0.432 4,257 0.321 0.000 0.467 4,256 0.174 0.000 0.379

EM 8,513 -0.006 0.007 0.191 4,257 -0.006 0.004 0.108 4,256 -0.007 0.009 0.248

DVS_GEO 7,194 0.750 0.707 0.557 3,899 0.856 0.878 0.520 3,295 0.624 0.584 0.574

DVS_BIZ 6,471 0.380 0.000 0.554 3,366 0.565 0.427 0.625 3,105 0.179 0.000 0.371

GINDEX 3,033 9.136 9.000 2.496 2,492 9.323 9.000 2.487 541 8.279 8.000 2.357

Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms

above (below) the median value of natural logarithm of total assets of the full sample.

205

Table 5.9 Descriptive Statistics – Capital Expenditure by Firm Size Full Sample Large Firms Small Firms


CAPEXt+1 (Value) 14,966 $223.06 $20.68 $1,138.89 7,483 $433.26 $88.81 $1,582.77 7,483 $12.86 $4.73 $27.58

CAPEXt+1 14,966 -3.485 -3.470 1.042 7,483 -3.318 -3.341 0.919 7,483 -3.652 -3.634 1.128

ψ 14,966 1.855 1.473 1.658 7,483 1.099 1.050 0.942 7,483 2.611 2.194 1.862

1-R2 14,966 0.791 0.814 0.158 7,483 0.718 0.741 0.153 7,483 0.864 0.900 0.125

AT (Value) 14,966 $3,634.68 $571.69 $13,760.20 7,483 $7,058.82 $1,988.03 $18,847.70 7,483 $210.54 $171.32 $152.72

SIZE 14,966 6.432 6.349 1.793 7,483 7.864 7.595 1.197 7,483 4.999 5.144 0.946

ANALYST 14,966 7.267 5.000 6.485 7,483 10.483 9.000 7.127 7,483 4.051 3.000 3.553

BIDASK 14,962 0.005 0.002 0.010 7,480 0.002 0.001 0.004 7,482 0.008 0.004 0.012

FIRM AGE (Years) 14,966 20.659 15.000 14.544 7,483 25.794 20.000 16.597 7,483 15.524 13.000 9.740

AGE 14,966 2.800 2.708 0.675 7,483 3.024 2.996 0.698 7,483 2.575 2.565 0.568

StdROE 14,966 0.924 0.056 52.873 7,483 1.141 0.042 73.967 7,483 0.707 0.078 10.964

StdCF 14,966 87.982 17.108 324.939 7,483 164.984 50.300 446.245 7,483 10.979 6.758 13.773

StdRET 14,966 0.033 0.029 0.017 7,483 0.027 0.023 0.015 7,483 0.038 0.034 0.018

MB 14,966 4.293 2.261 49.083 7,483 3.630 2.251 13.186 7,483 4.956 2.273 68.146

ROA 14,966 -0.009 0.041 0.218 7,483 0.043 0.051 0.095 7,483 -0.060 0.022 0.284

WC 14,966 9.955 0.229 509.067 7,483 1.174 0.159 53.512 7,483 18.736 0.348 717.855

DIV 14,966 0.365 0.000 0.482 7,483 0.533 1.000 0.499 7,483 0.198 0.000 0.399

MERGER 14,966 0.075 0.000 0.263 7,483 0.097 0.000 0.297 7,483 0.052 0.000 0.222

RESTRUCT 14,966 0.328 0.000 0.469 7,483 0.412 0.000 0.492 7,483 0.243 0.000 0.429

LOSS 14,966 0.291 0.000 0.454 7,483 0.166 0.000 0.372 7,483 0.416 0.000 0.493

LEV 14,966 0.454 0.454 0.213 7,483 0.536 0.541 0.186 7,483 0.373 0.336 0.207

AUDIT 14,966 0.257 0.000 0.437 7,483 0.316 0.000 0.465 7,483 0.198 0.000 0.399

EM 14,966 0.000 0.007 0.141 7,483 0.001 0.006 0.090 7,483 0.000 0.008 0.178

FCF 14,966 0.039 0.075 0.201 7,483 0.087 0.084 0.070 7,483 -0.008 0.058 0.267

DVS_GEO 12,208 0.574 0.523 0.560 6,413 0.635 0.627 0.554 5,795 0.507 0.369 0.560

DVS_BIZ 11,278 0.363 0.000 0.517 5,682 0.501 0.322 0.580 5,596 0.223 0.000 0.398

GINDEX 5,204 9.047 9.000 2.498 4,023 9.261 9.000 2.531 1,181 8.315 8.000 2.231

Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms above (below) the

median value of natural logarithm of total assets of the full sample.

206

Table 5.10 Descriptive Statistics – Selling, General and Administrative Costs by Firm Size Full Sample Large Firms Small Firms


SGAt+1 (Value) 13,780 $675.70 $121.72 $2,646.73 6,890 $1,277.52 $366.68 $3,644.44 6,890 $73.88 $50.78 $70.23

SGAt+1 13,780 -1.586 -1.442 0.961 6,890 -1.934 -1.767 0.982 6,890 -1.238 -1.151 0.802

ψ 13,780 1.808 1.451 1.545 6,890 1.084 1.038 0.911 6,890 2.532 2.142 1.701

1-R2 13,780 0.788 0.810 0.158 6,890 0.715 0.738 0.153 6,890 0.861 0.895 0.127

AT (Value) 13,780 $3,686.18 $593.86 $14,148.17 6,890 $7,150.56 $2,018.73 $19,399.43 6,890 $221.81 $182.26 $159.08

SIZE 13,780 6.468 6.387 1.769 6,890 7.878 7.610 1.185 6,890 5.058 5.205 0.938

ANALYST 13,780 7.304 5.000 6.543 6,890 10.528 9.000 7.201 6,890 4.079 3.000 3.603

BIDASK 13,777 0.005 0.002 0.010 6,888 0.002 0.001 0.004 6,889 0.008 0.004 0.012

FIRM AGE (Years) 13,780 20.959 15.000 14.676 6,890 26.002 20.000 16.723 6,890 15.917 13.000 10.013

AGE 13,780 2.814 2.708 0.676 6,890 3.031 2.996 0.700 6,890 2.598 2.565 0.574

StdROE 13,780 0.489 0.054 8.691 6,890 0.295 0.042 4.599 6,890 0.684 0.071 11.396

StdCF 13,780 89.851 17.474 333.856 6,890 168.556 50.695 458.630 6,890 11.146 6.871 14.254

StdRET 13,780 0.032 0.028 0.017 6,890 0.027 0.023 0.015 6,890 0.037 0.034 0.017

MB 13,780 4.133 2.228 50.784 6,890 3.677 2.256 13.690 6,890 4.589 2.198 70.502

ROA 13,780 0.006 0.044 0.184 6,890 0.044 0.051 0.094 6,890 -0.032 0.030 0.237

WC 13,780 0.707 0.224 13.857 6,890 0.563 0.163 18.172 6,890 0.852 0.318 7.333

DIV 13,780 0.373 0.000 0.484 6,890 0.533 1.000 0.499 6,890 0.212 0.000 0.409

MERGER 13,780 0.077 0.000 0.267 6,890 0.100 0.000 0.300 6,890 0.054 0.000 0.226

RESTRUCT 13,780 0.340 0.000 0.474 6,890 0.423 0.000 0.494 6,890 0.257 0.000 0.437

LOSS 13,780 0.271 0.000 0.445 6,890 0.165 0.000 0.371 6,890 0.378 0.000 0.485

LEV 13,780 0.454 0.453 0.211 6,890 0.535 0.541 0.187 6,890 0.373 0.339 0.202

AUDIT 13,780 0.258 0.000 0.438 6,890 0.318 0.000 0.466 6,890 0.199 0.000 0.399

EM 13,780 0.001 0.007 0.121 6,890 0.001 0.006 0.090 6,890 0.002 0.008 0.146

EMP 13,780 0.007 0.004 0.019 6,890 0.006 0.003 0.018 6,890 0.008 0.004 0.019

DVS_GEO 11,543 0.589 0.544 0.561 5,953 0.649 0.642 0.553 5,590 0.525 0.410 0.562

DVS_BIZ 10,327 0.377 0.000 0.522 5,200 0.510 0.340 0.583 5,127 0.242 0.000 0.410

GINDEX 4,922 9.081 9.000 2.512 3,749 9.318 9.000 2.536 1,173 8.327 8.000 2.277

Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Large (small) firms are defined as firms above (below) the

median value of natural logarithm of total assets of the full sample.

207

Descriptive statistics reported in Tables 5.8 to 5.10 suggest that large firms invest more

in corporate expenditure of the subsequent year, namely, R&D expenditure, CAPEX and

SGA costs. This is consistent with prior empirical findings in the areas of R&D costs

(Rothwell, 1984), CAPEX (Vogt, 1994) and SGA costs (Banker et al., 2011b). However,

large firms display a lower level of stock price informativeness represented by

idiosyncratic volatility as shown in all three corporate expenditure samples in this study.

This is consistent with findings by Chen et al. (2007) and Bakke and Whited (2010) that

firm size is negatively associated with idiosyncratic volatility and are supported by the

Pearson correlations results displayed for the three corporate expenditure samples

displayed in Tables 5.5 to 5.7.

Tables 5.8 to 5.10 also reveal that for all three proxies of corporate expenditure, namely,

R&D expenditure, CAPEX and SGA costs, large firms are widely followed by analysts.

They are older, more profitable and are highly-leveraged. These firms also possess

higher free cash flows and are more likely to pay dividends but the volatilities of their

cash flows is are greater. It is also found in Tables 5.8 to 5.10 that a majority of large

firms are audited by specialist auditors. Large firms are more diversified and are

intensive in merger and restructuring activities. Small firms, on the other hand, are more

likely to grow faster (proxied by market-to-book value) though incurring losses. The

availability of slack resources (represented by working capital ratio) and employee

intensity are found to be greater in small firms.

208

5.2.4 Descriptive Statistics by Analyst Following

The descriptive statistics of all variables used in the study by analyst following for all

three corporate expenditures are presented in Tables 5.11 to 5.13 respectively. These

tables summarize the analysed data for the full sample as well as two sub-samples, i.e.,

firms with high and low analyst following. Firms with high (low) analyst following are

firms above (below) the median value of the sample on the number of analysts that

follows a firm.

209

Table 5.11 Descriptive Statistics – R&D Expenditure by Analyst Following Full Sample High Analyst Following Low Analyst Following


R&D t+1 (Value) 8,513 $156.80 $24.40 $631.63 4,025 $304.56 $64.50 $885.09 4,488 $24.28 $10.72 $130.94

R&D t+1 8,513 -2.943 -2.749 1.383 4,025 -3.051 -2.838 1.311 4,488 -2.846 -2.645 1.438

ψ 8,513 1.909 1.527 1.667 4,025 1.195 1.137 0.961 4,488 2.548 2.170 1.891

1-R2 8,513 0.797 0.822 0.158 4,025 0.734 0.757 0.152 4,488 0.853 0.898 0.141

AT (Value) 8,513 $3,649.84 $379.72 $15,481.43 4,025 $7,009.70 $1,292.35 $21,201.66 4,488 $636.59 $142.28 $5,686.52

SIZE 8,513 6.121 5.939 1.909 4,025 7.278 7.164 1.706 4,488 5.084 4.958 1.425

ANALYST 8,513 7.154 5.000 6.585 4,025 12.262 10.000 6.315 4,488 2.573 2.000 1.444

BIDASK 8,510 0.006 0.002 0.010 4,023 0.002 0.001 0.002 4,487 0.009 0.005 0.012

FIRM AGE (Years) 8,513 20.457 15.000 14.956 4,025 23.059 16.000 17.192 4,488 18.123 14.000 12.155

AGE 8,513 2.783 2.708 0.677 4,025 2.870 2.773 0.733 4,488 2.704 2.639 0.611

StdROE 8,513 0.769 0.074 9.994 4,025 0.506 0.061 7.605 4,488 1.004 0.092 11.726

StdCF 8,513 90.215 13.993 388.443 4,025 167.690 34.444 545.634 4,488 20.733 7.041 95.028

StdRET 8,513 0.034 0.031 0.019 4,025 0.029 0.026 0.014 4,488 0.039 0.035 0.022

MB 8,513 3.988 2.519 68.048 4,025 5.158 2.929 96.236 4,488 2.939 2.136 21.820

ROA 8,513 -0.061 0.032 0.352 4,025 0.006 0.052 0.190 4,488 -0.121 0.007 0.442

WC 8,513 16.693 0.378 675.765 4,025 15.691 0.353 524.678 4,488 17.591 0.402 787.029

DIV 8,513 0.296 0.000 0.456 4,025 0.358 0.000 0.480 4,488 0.240 0.000 0.427

MERGER 8,513 0.081 0.000 0.273 4,025 0.109 0.000 0.312 4,488 0.056 0.000 0.229

RESTRUCT 8,513 0.408 0.000 0.491 4,025 0.471 0.000 0.499 4,488 0.351 0.000 0.477

LOSS 8,513 0.374 0.000 0.484 4,025 0.254 0.000 0.436 4,488 0.481 0.000 0.500

LEV 8,513 0.442 0.404 0.314 4,025 0.462 0.452 0.253 4,488 0.424 0.360 0.359

AUDIT 8,513 0.248 0.000 0.432 4,025 0.302 0.000 0.459 4,488 0.199 0.000 0.399

EM 8,513 -0.006 0.007 0.191 4,025 -0.011 0.003 0.159 4,488 -0.002 0.010 0.216

DVS_GEO 7,194 0.750 0.707 0.557 3,540 0.831 0.854 0.541 3,654 0.671 0.655 0.562

DVS_BIZ 6,471 0.380 0.000 0.554 3,066 0.462 0.000 0.619 3,405 0.305 0.000 0.475

GINDEX 3,033 9.136 9.000 2.496 2,054 9.111 9.000 2.452 979 9.190 9.000 2.585

Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are defined as firms

above (below) the median value of the full sample on the number of analysts that follow the firms.

210

Table 5.12 Descriptive Statistics - Capital Expenditure by Analyst Following Full Sample High Analyst Following Low Analyst Following


CAPEXt+1 (Value) 14,966 $223.06 $20.68 $1,138.89 7,259 $406.99 $68.60 $1,567.32 7,707 $49.82 $7.25 $378.59

CAPEXt+1 14,966 -3.485 -3.470 1.042 7,259 -3.339 -3.343 0.979 7,707 -3.622 -3.594 1.081

ψ 14,966 1.855 1.473 1.658 7,259 1.206 1.154 0.960 7,707 2.467 2.040 1.923

1-R2 14,966 0.791 0.814 0.158 7,259 0.735 0.760 0.152 7,707 0.844 0.885 0.145

AT (Value) 14,966 $3,634.68 $571.69 $13,760.20 7,259 $6,637.07 $1,572.99 $18,676.44 7,707 $806.82 $228.05 $4,762.66

SIZE 14,966 6.432 6.349 1.793 7,259 7.443 7.361 1.583 7,707 5.479 5.430 1.420

ANALYST 14,966 7.267 5.000 6.485 7,259 12.219 10.000 6.071 7,707 2.603 2.000 1.443

BIDASK 14,962 0.005 0.002 0.010 7,257 0.002 0.001 0.002 7,705 0.008 0.004 0.012

FIRM AGE (Years) 14,966 20.659 15.000 14.544 7,259 22.400 16.000 16.061 7,707 19.019 15.000 12.739

AGE 14,966 2.800 2.708 0.675 7,259 2.860 2.773 0.710 7,707 2.743 2.708 0.634

StdROE 14,966 0.924 0.056 52.873 7,259 0.339 0.045 5.699 7,707 1.475 0.070 73.469

StdCF 14,966 87.982 17.108 324.939 7,259 154.151 38.162 446.662 7,707 25.659 8.654 95.540

StdRET 14,966 0.033 0.029 0.017 7,259 0.028 0.025 0.013 7,707 0.037 0.033 0.019

MB 14,966 4.293 2.261 49.083 7,259 5.076 2.684 68.899 7,707 3.556 1.931 14.366

ROA 14,966 -0.009 0.041 0.218 7,259 0.032 0.055 0.143 7,707 -0.047 0.024 0.265

WC 14,966 9.955 0.229 509.067 7,259 8.716 0.199 390.749 7,707 11.122 0.258 599.546

DIV 14,966 0.365 0.000 0.482 7,259 0.422 0.000 0.494 7,707 0.312 0.000 0.463

MERGER 14,966 0.075 0.000 0.263 7,259 0.092 0.000 0.289 7,707 0.058 0.000 0.234

RESTRUCT 14,966 0.328 0.000 0.469 7,259 0.358 0.000 0.480 7,707 0.299 0.000 0.458

LOSS 14,966 0.291 0.000 0.454 7,259 0.191 0.000 0.393 7,707 0.385 0.000 0.487

LEV 14,966 0.454 0.454 0.213 7,259 0.477 0.486 0.203 7,707 0.433 0.422 0.220

AUDIT 14,966 0.257 0.000 0.437 7,259 0.310 0.000 0.462 7,707 0.208 0.000 0.406

EM 14,966 0.000 0.007 0.141 7,259 -0.004 0.005 0.126 7,707 0.004 0.009 0.153

FCF 14,966 0.039 0.075 0.201 7,259 0.076 0.088 0.126 7,707 0.005 0.060 0.246

DVS_GEO 12,208 0.574 0.523 0.560 6,118 0.634 0.619 0.566 6,090 0.514 0.406 0.548

DVS_BIZ 11,278 0.363 0.000 0.517 5,371 0.405 0.000 0.561 5,907 0.325 0.000 0.471

GINDEX 5,204 9.047 9.000 2.498 3,583 9.035 9.000 2.463 1,621 9.073 9.000 2.574

Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are defined as firms

above (below) the median value of the full sample on the number of analysts that follow the firms.

211

Table 5.13 Descriptive Statistics – Selling, General and Administrative Costs by Analyst Following Full Sample High Analyst Following Low Analyst Following


SGAt+1 (Value) 13,780 $675.70 $121.72 $2,646.73 6,708 $1,226.16 $295.65 $3,663.01 7,072 $153.57 $56.97 $603.08

SGAt+1 13,780 -1.586 -1.442 0.961 6,708 -1.743 -1.564 0.983 7,072 -1.438 -1.317 0.916

ψ 13,780 1.808 1.451 1.545 6,708 1.192 1.147 0.930 7,072 2.392 1.985 1.770

1-R2 13,780 0.788 0.810 0.158 6,708 0.733 0.759 0.152 7,072 0.840 0.879 0.146

AT (Value) 13,780 $3,686.18 $593.86 $14,148.17 6,708 $6,739.18 $1,612.35 $19,207.03 7,072 $790.33 $239.94 $4,787.11

SIZE 13,780 6.468 6.387 1.769 6,708 7.466 7.385 1.563 7,072 5.522 5.480 1.394

ANALYST 13,780 7.304 5.000 6.543 6,708 12.261 10.000 6.154 7,072 2.601 2.000 1.441

BIDASK 13,777 0.005 0.002 0.010 6,707 0.002 0.001 0.002 7,070 0.008 0.004 0.013

FIRM AGE (Years) 13,780 20.959 15.000 14.676 6,708 22.545 16.000 16.158 7,072 19.455 15.000 12.939

AGE 13,780 2.814 2.708 0.676 6,708 2.866 2.773 0.711 7,072 2.765 2.708 0.637

StdROE 13,780 0.489 0.054 8.691 6,708 0.337 0.045 5.907 7,072 0.634 0.065 10.680

StdCF 13,780 89.851 17.474 333.856 6,708 157.740 38.495 458.730 7,072 25.457 8.818 95.273

StdRET 13,780 0.032 0.028 0.017 6,708 0.028 0.025 0.013 7,072 0.037 0.032 0.019

MB 13,780 4.133 2.228 50.784 6,708 5.062 2.652 71.555 7,072 3.251 1.891 12.941

ROA 13,780 0.006 0.044 0.184 6,708 0.041 0.056 0.125 7,072 -0.028 0.027 0.221

WC 13,780 0.707 0.224 13.857 6,708 0.439 0.201 3.284 7,072 0.961 0.247 19.073

DIV 13,780 0.373 0.000 0.484 6,708 0.424 0.000 0.494 7,072 0.323 0.000 0.468

MERGER 13,780 0.077 0.000 0.267 6,708 0.095 0.000 0.293 7,072 0.060 0.000 0.238

RESTRUCT 13,780 0.340 0.000 0.474 6,708 0.369 0.000 0.483 7,072 0.312 0.000 0.463

LOSS 13,780 0.271 0.000 0.445 6,708 0.177 0.000 0.382 7,072 0.360 0.000 0.480

LEV 13,780 0.454 0.453 0.211 6,708 0.475 0.484 0.202 7,072 0.435 0.423 0.217

AUDIT 13,780 0.258 0.000 0.438 6,708 0.312 0.000 0.463 7,072 0.208 0.000 0.406

EM 13,780 0.001 0.007 0.121 6,708 -0.002 0.005 0.092 7,072 0.005 0.009 0.143

EMP 13,780 0.007 0.004 0.019 6,708 0.006 0.003 0.015 7,072 0.008 0.004 0.022

DVS_GEO 11,543 0.589 0.544 0.561 5,762 0.649 0.638 0.566 5,781 0.528 0.438 0.550

DVS_BIZ 10,327 0.377 0.000 0.522 4,930 0.415 0.000 0.565 5,397 0.342 0.000 0.477

GINDEX 4,922 9.081 9.000 2.512 3,356 9.071 9.000 2.472 1,566 9.104 9.000 2.596

Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) analyst following are

defined as firms above (below) the median value of the full sample on the number of analysts that follow the firms.

212

The data reported in Tables 5.11 to 5.13 further shows that firms with high analyst

following tend to invest more in the subsequent year‟s R&D costs, CAPEX and SGA

costs. High analyst coverage is closely related to large firm size as supported by the

Pearson correlations results in Tables 5.5 to 5.7 and this is consistent with the findings

documented by Bhushan (1989b). As predicted in item 4.5.4.1(b), firms with higher

analyst following are larger, hence are more financially sound to invest more in

corporate expenditure in the subsequent year.

It is also observed from Tables 5.11 to 5.13 that firms with low analyst following

possess higher idiosyncratic volatility. These findings provide empirical support to the

study by Chen et al. (2007), highlighting that private information is negatively related

with analyst following. Firms with high analyst following appear to exhibit higher cash

flow volatility, are more fast-growing and tend to be more profitable. These firms

possess higher free cash flow and tend to pay dividends. They are also more diversified

and are mostly audited by the specialist auditors.

5.2.5 Descriptive Statistics by Bid-ask Spreads

Two sub-groups are formed to analyse the characteristics of firms with high and low

bid-ask spreads based on the median value of average bid-ask spreads of the full sample.

Tables 5.14 to 5.16 provide the relevant descriptive statistics of all variables used in the

study by bid-ask spreads for the three corporate expenditures, namely, R&D expenditure,

CAPEX and SGA costs.

213

Table 5.14 Descriptive Statistics – R&D Expenditure by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads


R&D t+1 (Value) 8,510 $156.78 $24.40 $631.72 4,255 $33.60 $11.48 $140.71 4,255 $279.95 $54.59 $864.92

R&D t+1 8,510 -2.942 -2.749 1.383 4,255 -2.658 -2.457 1.410 4,255 -3.227 -3.034 1.293

Ψ 8,510 1.909 1.528 1.667 4,255 2.683 2.355 1.894 4,255 1.134 1.113 0.877

1-R2 8,510 0.797 0.822 0.158 4,255 0.866 0.913 0.133 4,255 0.727 0.753 0.149

AT (Value) 8,510 $3,650.16 $379.51 $15,484.09 4,255 $672.34 $124.22 $3,443.74 4,255 $6,627.98 $1,127.74 $21,212.54

SIZE 8,510 6.121 5.939 1.909 4,255 4.985 4.822 1.465 4,255 7.256 7.028 1.602

ANALYST 8,510 7.154 5.000 6.585 4,255 3.710 3.000 3.617 4,255 10.598 9.000 7.065

BIDASK 8,510 0.006 0.002 0.010 4,255 0.010 0.006 0.012 4,255 0.001 0.001 0.001

FIRM AGE (Years) 8,510 20.446 15.000 14.948 4,255 16.558 13.000 11.464 4,255 24.334 17.000 16.890

AGE 8,510 2.782 2.708 0.676 4,255 2.615 2.565 0.602 4,255 2.950 2.833 0.705

StdROE 8,510 0.769 0.074 9.996 4,255 1.026 0.115 9.910 4,255 0.511 0.051 10.076

StdCF 8,510 90.225 13.990 388.510 4,255 27.507 6.669 160.668 4,255 152.943 31.085 517.912

StdRET 8,510 0.034 0.031 0.019 4,255 0.042 0.038 0.023 4,255 0.027 0.025 0.011

MB 8,510 3.988 2.519 68.060 4,255 3.926 2.146 91.840 4,255 4.051 2.840 28.824

ROA 8,510 -0.061 0.032 0.352 4,255 -0.159 -0.025 0.458 4,255 0.036 0.057 0.139

WC 8,510 16.698 0.378 675.884 4,255 19.897 0.433 809.037 4,255 13.500 0.332 509.101

DIV 8,510 0.296 0.000 0.456 4,255 0.185 0.000 0.389 4,255 0.406 0.000 0.491

MERGER 8,510 0.081 0.000 0.273 4,255 0.053 0.000 0.223 4,255 0.110 0.000 0.312

RESTRUCT 8,510 0.408 0.000 0.491 4,255 0.350 0.000 0.477 4,255 0.466 0.000 0.499

LOSS 8,510 0.374 0.000 0.484 4,255 0.559 1.000 0.497 4,255 0.188 0.000 0.391

LEV 8,510 0.442 0.404 0.314 4,255 0.435 0.361 0.374 4,255 0.449 0.443 0.239

AUDIT 8,510 0.248 0.000 0.432 4,255 0.201 0.000 0.401 4,255 0.295 0.000 0.456

EM 8,510 -0.006 0.007 0.191 4,255 -0.009 0.009 0.248 4,255 -0.004 0.004 0.109

DVS_GEO 7,191 0.750 0.707 0.557 3,404 0.667 0.654 0.569 3,787 0.824 0.834 0.536

DVS_BIZ 6,468 0.379 0.000 0.554 3,213 0.271 0.000 0.457 3,255 0.486 0.078 0.616

GINDEX 3,030 9.136 9.000 2.496 818 8.853 9.000 2.466 2,212 9.240 9.000 2.500

Note: All variables are defined in Table 4.1. The variables R&D t+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are

defined as firms above (below) the median value of the average bid-ask spreads of the full sample.

214

Table 5.15 Descriptive Statistics – Capital Expenditure by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads


CAPEXt+1 (Value) 14,962 $223.10 $20.67 $1,139.03 7,481 $53.88 $6.13 $284.36 7,481 $392.32 $58.56 $1,567.43

CAPEXt+1 14,962 -3.485 -3.470 1.042 7,481 -3.631 -3.607 1.112 7,481 -3.338 -3.348 0.945

ψ 14,962 1.855 1.473 1.658 7,481 2.575 2.214 1.920 7,481 1.136 1.125 0.881

1-R2 14,962 0.791 0.814 0.158 7,481 0.854 0.902 0.140 7,481 0.728 0.755 0.149

AT (Value) 14,962 $3,634.85 $571.45 $13,761.99 7,481 $958.94 $200.72 $3,808.75 7,481 $6,310.76 $1,428.93 $18,707.77

SIZE 14,962 6.431 6.348 1.793 7,481 5.445 5.302 1.495 7,481 7.417 7.265 1.501

ANALYST 14,962 7.267 5.000 6.485 7,481 4.047 3.000 4.032 7,481 10.488 9.000 6.863

BIDASK 14,962 0.005 0.002 0.010 7,481 0.009 0.005 0.012 7,481 0.001 0.001 0.000

FIRM AGE (Years) 14,962 20.654 15.000 14.539 7,481 17.492 13.000 12.043 7,481 23.815 17.000 16.056

AGE 14,962 2.799 2.708 0.675 7,481 2.658 2.565 0.628 7,481 2.941 2.833 0.690

StdROE 14,962 0.924 0.056 52.880 7,481 1.578 0.080 74.582 7,481 0.270 0.042 5.438

StdCF 14,962 87.989 17.106 324.981 7,481 31.874 8.214 139.073 7,481 144.104 35.456 430.813

StdRET 14,962 0.033 0.029 0.017 7,481 0.039 0.035 0.020 7,481 0.026 0.024 0.011

MB 14,962 4.293 2.261 49.090 7,481 4.326 1.953 65.736 7,481 4.261 2.594 22.331

ROA 14,962 -0.008 0.041 0.218 7,481 -0.068 0.014 0.277 7,481 0.051 0.059 0.107

WC 14,962 9.958 0.230 509.135 7,481 11.981 0.262 608.871 7,481 7.934 0.199 384.369

DIV 14,962 0.365 0.000 0.482 7,481 0.262 0.000 0.440 7,481 0.469 0.000 0.499

MERGER 14,962 0.075 0.000 0.263 7,481 0.053 0.000 0.223 7,481 0.097 0.000 0.296

RESTRUCT 14,962 0.328 0.000 0.469 7,481 0.293 0.000 0.455 7,481 0.362 0.000 0.481

LOSS 14,962 0.291 0.000 0.454 7,481 0.439 0.000 0.496 7,481 0.143 0.000 0.350

LEV 14,962 0.454 0.454 0.213 7,481 0.437 0.422 0.223 7,481 0.472 0.482 0.201

AUDIT 14,962 0.257 0.000 0.437 7,481 0.213 0.000 0.410 7,481 0.302 0.000 0.459

EM 14,962 0.000 0.007 0.141 7,481 -0.001 0.009 0.176 7,481 0.001 0.006 0.093

FCF 14,962 0.039 0.075 0.201 7,481 -0.010 0.053 0.258 7,481 0.089 0.090 0.097

DVS_GEO 12,204 0.574 0.523 0.560 5,856 0.510 0.388 0.555 6,348 0.633 0.615 0.558

DVS_BIZ 11,274 0.363 0.000 0.517 5,698 0.292 0.000 0.455 5,576 0.436 0.072 0.564

GINDEX 5,200 9.046 9.000 2.498 1,467 8.839 9.000 2.498 3,733 9.128 9.000 2.494

Note: All variables are defined in Table 4.1. The variables CAPEXt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are

defined as firms above (below) the median value of the average bid-ask spreads of the full sample

215

Table 5.16 Descriptive Statistics – Selling, General and Administrative Costs by Bid-ask Spreads Full Sample Large Bid-ask Spreads Small Bid-ask Spreads


SGAt+1 (Value) 13,777 $675.82 $121.73 $2,647.01 6,889 $185.46 $53.84 $640.96 6,888 $1,166.25 $278.97 $3,622.61

SGAt+1 13,777 -1.586 -1.441 0.961 6,889 -1.444 -1.287 0.968 6,888 -1.728 -1.577 0.933

ψ 13,777 1.808 1.451 1.545 6,889 2.488 2.152 1.767 6,888 1.127 1.120 0.852

1-R2 13,777 0.788 0.810 0.158 6,889 0.850 0.896 0.142 6,888 0.726 0.754 0.149

AT (Value) 13,777 $3,686.55 $593.83 $14,149.67 6,889 $1,011.27 $212.53 $3,920.75 6,888 $6,362.21 $1,442.24 $19,255.92

SIZE 13,777 6.468 6.387 1.770 6,889 5.514 5.359 1.492 6,888 7.422 7.274 1.488

ANALYST 13,777 7.305 5.000 6.544 6,889 4.096 3.000 4.127 6,888 10.514 9.000 6.930

BIDASK 13,777 0.005 0.002 0.010 6,889 0.009 0.005 0.013 6,888 0.001 0.001 0.000

FIRM AGE (Years) 13,777 20.956 15.000 14.673 6,889 17.957 14.000 12.300 6,888 23.956 18.000 16.167

AGE 13,777 2.814 2.708 0.676 6,889 2.683 2.639 0.633 6,888 2.945 2.890 0.692

StdROE 13,777 0.489 0.054 8.692 6,889 0.704 0.074 10.909 6,888 0.274 0.042 5.659

StdCF 13,777 89.861 17.460 333.891 6,889 33.678 8.464 149.297 6,888 146.053 35.493 440.898

StdRET 13,777 0.032 0.028 0.017 6,889 0.039 0.035 0.019 6,888 0.026 0.024 0.011

MB 13,777 4.133 2.229 50.790 6,889 4.003 1.900 68.045 6,888 4.264 2.589 23.001

ROA 13,777 0.006 0.044 0.184 6,889 -0.044 0.019 0.230 6,888 0.056 0.060 0.099

WC 13,777 0.707 0.224 13.858 6,889 0.732 0.249 6.947 6,888 0.683 0.203 18.327

DIV 13,777 0.373 0.000 0.483 6,889 0.275 0.000 0.447 6,888 0.470 0.000 0.499

MERGER 13,777 0.077 0.000 0.267 6,889 0.056 0.000 0.230 6,888 0.098 0.000 0.298

RESTRUCT 13,777 0.340 0.000 0.474 6,889 0.308 0.000 0.462 6,888 0.371 0.000 0.483

LOSS 13,777 0.271 0.000 0.445 6,889 0.409 0.000 0.492 6,888 0.133 0.000 0.340

LEV 13,777 0.454 0.453 0.211 6,889 0.439 0.423 0.221 6,888 0.470 0.480 0.199

AUDIT 13,777 0.258 0.000 0.438 6,889 0.212 0.000 0.409 6,888 0.304 0.000 0.460

EM 13,777 0.001 0.007 0.121 6,889 0.001 0.008 0.147 6,888 0.001 0.006 0.088

EMP 13,777 0.007 0.004 0.019 6,889 0.007 0.004 0.020 6,888 0.006 0.003 0.018

DVS_GEO 11,540 0.589 0.544 0.561 5,616 0.527 0.425 0.557 5,924 0.647 0.631 0.559

DVS_BIZ 10,324 0.377 0.000 0.522 5,227 0.314 0.000 0.467 5,097 0.441 0.083 0.566

GINDEX 4,919 9.082 9.000 2.512 1,447 8.878 9.000 2.515 3,472 9.166 9.000 2.506

Note: All variables are defined in Table 4.1. The variables SGAt+1 (Value) and AT (Value) are stated in USD million. Firms with high (low) bid-ask spreads are

defined as firms above (below) the median value of the average bid-ask spreads of the full sample.

216

Analysed data summarized in Tables 5.14 to 5.16 reveal that firms with large bid-ask

spreads on average spend less in corporate expenditure of the subsequent year, namely,

R&D expenditure, CAPEX and SGA costs. Nevertheless, idiosyncratic volatility is

found to be higher in firms with large bid-ask spreads for all three corporate

expenditures. These findings is consistent with those reported by Chan et al. (2013) that

idiosyncratic volatility is positively associated with bid-ask spreads.

Bid-ask spreads are larger in small-sized firms and this finding is consistent with those

reported in Stoll (2000). Large bid-ask spreads are also observed in firms with relatively

lower analyst coverage. In terms of firm characteristics, firms with large bid-ask spreads

appear to be younger and they possess lower cash flow volatility but higher working

capital ratio (representing immediate slack resources). These firms mostly incur losses

and they are less diversified. Further, they have a lower tendency to pay dividends and

undertake fewer merger and restructuring activities.

5.3 Hypothesis 1 - Stock Price Informativeness and Corporate Expenditure

Drawing from the learning theory, Hypothesis 1 of the current study is developed to

examine the association between a current year‟s stock price informativeness and

corporate expenditure in the subsequent year. The results of multivariate and robustness

tests of the main model employed in this study are presented in items 5.3.1 and 5.3.2

respectively.

217

5.3.1 Multivariate Tests

Hypothesis 1 is empirically tested using a regression analysis which is presented in

Equation 5.1:

∑ ∑ ∑

(5.1)

where:

is the corporate expenditure for firm i of the subsequent year ( ). As

mentioned earlier, the three proxies of corporate expenditure employed in this study are

R&D costs, CAPEX and SGA costs. The coefficient is the intercept while coefficient

is the coefficient of interest. The independent variable represents idiosyncratic

volatility of the current year and denotes a set of control variables in the

same year. Year and Industry dummies are also included in the model while

represents unspecified random factors.

Hypothesis 1 of the study postulates that a current year‟s idiosyncratic volatility is

negatively associated with the subsequent year‟s corporate expenditure, thus the

coefficient of interest, is expected to be negative. Tables 5.17 to 5.19 present the

regression results of multivariate tests of the main model that examines the association

between a current year‟s idiosyncratic volatility and three proxies of corporate

expenditure in the subsequent year, namely, R&D expenditure, CAPEX and SGA costs.

The reported t-statistics are adjusted using standard errors corrected for

heteroskedasticity by applying the White (1980) variance correction method. Besides,

218

statistical tests are conducted using the two-way cluster-robust standard errors, i.e., by

both firm and year based on the Petersen (2009) procedure to account for cross-sectional

and time-series correlation. Table 5.17 reports regression results of eight models when

corporate expenditure is represented by R&D costs. The eight models presented are as

follows:

a) Models 1a and 1b. This is the initial sample obtained using the sample selection

procedure outlined in item 4.7.

b) Models 2a and 2b when variable CAPEX of the subsequent year is added as a

control variable.

c) Models 3a and 3b when two diversification variables, DVS_GEO and DVS_BIZ

are included as control variables.

d) Models 4a and 4b when GINDEX is included to control for firms‟ corporate

governance.

219

Table 5.17 Effect of Stock Price Informativeness on R&D Expenditure (H1)

∑ ∑ ∑

(Model 1a) (Model 1b) Expected

White-adjusted

Petersen Clustering

Direction Coefficient t-statistic Coefficient t-statistic

Intercept 0.270 0.82 0.408 0.82

ψ ?

-0.064 -6.28 ***

-0.049 -2.94 **

SIZE +ve

-0.510 -10.41 ***

-0.498 -5.14 ***

SIZE2 ?

0.012 3.25 ***

0.011 1.49

ANALYST +ve

0.072 27.19 ***

0.072 13.83 ***

AGE +ve

-0.063 -2.74 ***

-0.064 -1.57

StdROE +ve

0.003 4.77 ***

0.003 4.74 ***

StdCF +ve

0.000 0.29

0.000 .

StdRET ?

-0.937 -1.00

-2.983 -2.08 *

MB +ve

0.000 1.01

0.000 0.97

ROA -ve

-0.582 -6.64 ***

-0.595 -4.04 ***

WC -ve

0.000 1.49

0.000 .

DIV -ve

-0.467 -14.81 ***

-0.477 -6.73 ***

MERGER ?

0.057 1.39

0.042 0.87

RESTRUCT +ve

0.136 5.41 ***

0.128 3.23 **

LOSS +ve

0.481 13.65 ***

0.484 9.82 ***

LEV -ve

-0.343 -6.24 ***

-0.336 -3.91 ***

AUDIT +ve

0.054 2.11 **

0.052 1.25

EM -ve

-0.022 -0.35

-0.028 -0.48

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.445

0.441

F-Statistics

214.05

226.06

p-value

<0.0001

<0.0001

N 8,513

8,513

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,

5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White

(1980) and (b) clustering by firm and year using the Petersen (2009) procedure.

220


(Continued)

∑

∑

∑

(Model 2a)

(Model 2b)

Expected White-adjusted

Petersen Clustering


Intercept 0.081 0.24 0.259 0.51

ψ ? -0.065 -6.32 ***

-0.050 -3.01 **

SIZE +ve -0.510 -10.35 ***

-0.499 -5.27 ***

SIZE2 ? 0.012 3.27 **

0.011 1.53

ANALYST +ve 0.071 27.15 ***

0.071 13.95 ***

AGE +ve -0.064 -2.77 *

-0.066 -1.59

StdROE +ve 0.003 4.71 ***

0.003 4.61 ***

StdCF +ve 0.000 0.34

0.000 .

StdRET ? -1.292 -1.24

-3.629 -2.07 *

MB +ve 0.000 1.02

0.000 0.99

ROA -ve -0.579 -6.30 ***

-0.596 -3.82 ***

WC -ve 0.000 1.39

0.000 .

DIV -ve -0.461 -14.58 ***

-0.472 -6.77 ***

MERGER ? 0.047 1.13

0.032 0.64

RESTRUCT +ve 0.134 5.31 ***

0.126 3.18 **

LOSS +ve 0.471 13.01 ***

0.477 9.81 ***

LEV -ve -0.333 -6.01 ***

-0.326 -3.78 ***

AUDIT +ve 0.054 2.11 **

0.052 1.26

EM -ve -0.030 -0.44

-0.028 -0.43

CAPEXt+1 -ve -0.051 -3.52 ***

-0.046 -1.64

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.444

0.441

F-Statistics

206.44

217.74

p-value

<0.0001

<0.0001

N 8,483 8,483



(1980) and (b) clustering by firm and year using the Petersen (2009) procedure

221


(Continued)

∑ ∑ ∑

(Model 3a)

(Model 3b)


Petersen Clustering


Intercept 0.282 0.99

0.459 1.29

ψ ? -0.069 -5.32 ***

-0.054 -2.63 **

SIZE +ve -0.617 -9.79 ***

-0.606 -6.52 ***

SIZE2 ? 0.024 5.35 ***

0.023 3.34 **

ANALYST +ve 0.060 19.30 ***

0.060 11.25 ***

AGE +ve 0.020 0.71

0.014 0.29

StdROE +ve 0.002 5.14 ***

0.002 4.65 ***

StdCF +ve 0.000 2.01 **

0.000 .

StdRET ? -0.983 -0.56

-3.284 -2.19 *

MB +ve 0.000 1.10

0.000 .

ROA -ve -0.596 -6.95 ***

-0.609 -5.30 ***

WC -ve 0.001 1.28

0.002 1.11

DIV -ve -0.392 -10.78 ***

-0.400 -5.05 ***

MERGER ? 0.066 1.42

0.054 1.20

RESTRUCT +ve 0.128 4.22

0.122 2.53 **

LOSS +ve 0.323 8.22 ***

0.327 6.96 ***

LEV -ve -0.469 -5.82 ***

-0.464 -4.20 ***

AUDIT +ve 0.031 0.98

0.030 0.61

EM -ve -0.073 -0.55

-0.078 -0.41

CAPEXt+1 -ve -0.038 -1.84 *

-0.032 -0.95

DVS_GEO -ve 0.224 8.07 ***

0.227 4.72 **

DVS_BIZ -ve -0.218 -7.28 ***

-0.218 -4.34 **

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.403

0.401

F-Statistics

102.36

109.66

p-value

<0.0001

<0.0001

N 5,247

5,247




222


(Continued)

∑ ∑ ∑

(Model 4a)

(Model 4b)


Petersen Clustering


Intercept 2.434 5.04 ***

2.333 3.10 **

ψ ? -0.026 -0.99

-0.024 -0.86

SIZE +ve -0.965 -8.79 ***

-0.969 -5.84 ***

SIZE2 ? 0.040 5.78 ***

0.040 3.79 **

ANALYST +ve 0.070 18.80 ***

0.069 10.58 ***

AGE +ve 0.031 0.85

0.037 0.60

StdROE +ve 0.002 5.12 ***

0.002 4.68 ***

StdCF +ve 0.000 -1.55

0.000 .

StdRET ? -0.158 -0.05

-2.171 -0.42

MB +ve 0.000 1.03

0.000 .

ROA -ve -0.869 -4.12 ***

-0.860 -3.96 **

WC -ve 0.000 -0.28

0.000 -0.19

DIV -ve -0.420 -8.87 ***

-0.437 -5.07 ***

MERGER ? 0.139 2.31 **

0.155 2.76 *

RESTRUCT +ve 0.121 3.09 ***

0.126 2.03

LOSS +ve 0.192 3.10 ***

0.189 2.15 *

LEV -ve -0.489 -5.22 ***

-0.483 -3.52 **

AUDIT +ve 0.022 0.57

0.016 0.28

EM -ve 0.059 0.25

-0.016 -0.07

CAPEXt+1 -ve -0.105 -4.04 ***

-0.102 -2.27 *

GINDEX -ve -0.016 -2.07 **

-0.017 -1.28

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.406

0.403

F-Statistics

65.78

81.03

p-value

<0.0001

<0.0001

N 3,033

3,033

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than

1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of

White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.

223

Table 5.17 highlights that the coefficients for idiosyncratic volatility, ψ are negative at p

< 0.01 for Model 1a using the White (1980) procedure and at p < 0.05 for Model 1b

using the Petersen clustering procedure. These findings are in line with the Hypothesis 1,

indicating that the level of R&D expenditure in the subsequent year is high when stock

price informativeness is at low level during the current year, and vice versa.

To reflect the resource constraint situation faced by most firms, variable CAPEXt+1

(capital expenditure level in the subsequent year) is added in as a control variable in

Models 2a and 2b. As R&D expenditure forms part of SGA costs, the variable of SGAt+1

(SGA costs of the subsequent year) is not included in the model. As predicted in item

4.5.4.1(o), CAPEX level in the subsequent year is negatively associated with R&D

expenditure level in the subsequent year, as shown in Models 2a and 2b of Table 5.17.

Firms investing more in tangible assets (CAPEX) are more likely to spend less in

intangibles like R&D costs (Banker et al., 2011b). The coefficients of idiosyncratic

volatility, ψ remains both significantly negative at p < 0.01 using White-adjusted

procedure (Model 2a) and at p < 0.05 using Petersen clustering procedures (Model 2b)

at a lower sample size of 8,483 firm-year observations, supporting Hypothesis 1 of the

study.

Models 3a and 3b displayed in Table 5.17 include two variables of diversification,

namely, diversification in different geographic areas (DVS_GEO) and diversification in

different business lines (DIV_BIZ). The results are qualitatively similar to Models 1a

and 1b as well as Models 2a and 2b reported in Table 5.17 and this rules out the

224

possibility that the negative relationship between idiosyncratic volatility and R&D

investments are driven by a firm‟s diversification strategy. As expected in item

4.5.4.1(p), diversification in different business lines is negatively related to subsequent

year‟s R&D investment, because highly diversified firms emphasize in achieving short-

term financial performance targets, thus avoiding risky R&D projects. Contrary to the

prediction in item 4.5.4.1(p), geographic diversification is positively associated to R&D

expenditure. This could be due to geographically-diversified firms focusing on both

financial and strategic controls and practising openness in performance evaluation,

hence encouraging risk-taking behaviour in R&D projects.

When variable GINDEX is included in Models 4a and 4b as a control variable

representing corporate governance, the sample size is reduced to 3,033 firm-year

observations due to its missing values. GINDEX is negatively associated with R&D

expenditure as well-governed firms are expected to invest more in R&D to reap its

benefits (refer item 4.5.4.2(c)). However, the significance level for idiosyncratic

volatility, ψ disappears for both Models 4a and 4b that use White-adjusted and Petersen

clustering procedure respectively, possibly caused by a smaller sample size.

Consistent with prediction made in item 4.5.4.1, analyst following (ANALYST),

volatility of return on equity (StdROE), restructuring activities (RESTRUCT) and loss

dummy (LOSS) are positively related to subsequent year‟s R&D expenditure level,

while return on equity (ROA), dividend dummy (DIV) and leverage (LEV) are

negatively associated to R&D investment in the subsequent year. On the other hand, the

225

results show that young firms tend to invest more in R&D initiatives, against the earlier

prediction made in item 4.5.4.1(c) that older firms are normally larger and financially

stronger to invest in R&D. The negative coefficient of variable, firm size (SIZE) and the

positive coefficient of variable, squared value of firm size (SIZE2) imply a non-linear

relationship between firm size and R&D investment. When idiosyncratic volatility

increases from low levels, small firms tend to invest more in R&D projects, thereby

highlighting a negative association between idiosyncratic volatility and R&D

expenditure in the subsequent year. When idiosyncratic volatility reaches a certain

threshold value, the association between firm size and R&D costs changes to positive

indicating large firms are investing more in R&D activities compared to small firms.

Table 5.18 demonstrates the effect of current year‟s stock price informativeness on

subsequent year‟s capital expenditure. It reports regression results of eight models as

follows:

a) Models 5a and 5b using the initial sample obtained from the sample selection


b) Models 6a and 6b when variable SGA costs of the subsequent year is included as a

control variable.

c) Models 7a and 7b when two diversification variables, DVS_GEO and DVS_BIZ

are added as control variables.

d) Models 8a and 8b when GINDEX is included as a control variable of corporate

governance.

226

Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1)

∑ ∑ ∑

(Model 5a)

(Model 5b) Expected

White-adjusted

Petersen Clustering


Intercept -3.523 -20.32 *** -3.347 -13.51 ***

ψ ?

-0.018 -2.46 **

0.003 0.26

SIZE +ve

0.033 0.88

0.040 0.69

SIZE2 ?

-0.004 -1.75 *

-0.005 -1.39

ANALYST +ve

0.011 7.05 ***

0.012 4.26 ***

AGE +ve

-0.012 -0.86

-0.019 -0.80

StdROE ?

0.000 4.73 ***

0.000 .

StdCF ?

0.000 3.98 ***

0.000 .

StdRET ?

-2.441 -3.56 ***

-6.448 -5.82 ***

MB +ve

0.000 0.67

0.000 .

ROA +ve

-0.658 -5.32 ***

-0.562 -1.45

WC -ve

0.000 0.03

0.000 .

DIV -ve

0.115 6.20 ***

0.100 2.68 ***

MERGER -ve

-0.208 -8.21 ***

-0.227 -8.13 ***

RESTRUCT -ve

-0.164 -9.94 ***

-0.162 -7.18 ***

LOSS -ve

-0.179 -5.76 ***

-0.168 -6.54 ***

LEV -ve

0.304 6.67 ***

0.317 4.11 ***

AUDIT +ve

0.042 2.66 ***

0.046 1.90

EM -ve

-0.253 -2.29 **

-0.274 -4.01 ***

FCF +ve

1.467 9.72 ***

1.345 3.44 ***

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.248

0.239

F-Statistics

150.18

156.27

p-value

<0.0001

<0.0001

N 14,966 14,966




227

Table 5.18 Effect of Stock Price Informativeness on CAPEX (H1) (Continued)

∑ ∑ ∑

(Model 6a)

(Model 6b)


Petersen Clustering


Intercept -3.263 -19.64 *** -3.076 -14.22 ***

ψ ? -0.023 -3.02 ***

-0.003 -0.25

SIZE +ve -0.055 -1.57

-0.048 -1.03

SIZE2 ? 0.000 0.21

0.000 -0.11

ANALYST +ve 0.013 7.88 ***

0.013 4.39 ***

AGE +ve -0.025 -1.83 *

-0.033 -1.28

StdROE ? 0.001 0.83

0.001 1.29

StdCF ? 0.000 3.78 ***

0.000 .

StdRET ? -2.055 -2.78 ***

-6.237 -5.54 **

MB +ve 0.000 0.88

0.000 .

ROA +ve -0.540 -4.30 ***

-0.447 -1.12

WC -ve 0.000 0.27

0.000 0.29

DIV -ve 0.117 6.30 ***

0.102 2.59 **

MERGER -ve -0.220 -8.61 ***

-0.236 -9.42 ***

RESTRUCT -ve -0.162 -9.74 ***

-0.160 -7.35 ***

LOSS -ve -0.111 -4.05 ***

-0.101 -2.84 **

LEV -ve 0.321 6.97 ***

0.342 5.13 ***

AUDIT +ve 0.055 3.42 ***

0.057 2.31 *

EM -ve -0.346 -3.51 ***

-0.375 -3.50 **

FCF +ve 1.539 10.99 ***

1.413 3.25 **

SGAt+1 -ve -0.042 -3.49 ***

-0.039 -1.63

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.237

0.228

F-Statistics

128.40

137.35

p-value

<0.0001

<0.0001

N 13,971 13,971




228


∑ ∑ ∑

(Model 7a)

(Model 7b)


Petersen Clustering


Intercept -3.157 -15.38 ***

-2.972 -10.90 ***

ψ ? -0.037 -4.19 ***

-0.016 -1.03

SIZE +ve -0.055 -1.21

-0.046 -0.73

SIZE2 ? 0.001 0.42

0.000 0.03

ANALYST +ve 0.009 4.12 ***

0.010 2.84 **

AGE +ve -0.013 -0.72

-0.023 -0.85

StdROE ? 0.001 0.65

0.001 0.78

StdCF ? 0.000 2.17 **

0.000 .

StdRET ? -2.186 -2.32 **

-6.460 -6.62 ***

MB +ve 0.000 1.18

0.000 .

ROA +ve -0.623 -3.70 ***

-0.523 -1.39

WC -ve 0.001 0.49

0.001 0.36

DIV -ve 0.113 5.01 ***

0.099 2.30 *

MERGER -ve -0.193 -6.26 ***

-0.211 -6.13 ***

RESTRUCT -ve -0.120 -5.95 ***

-0.118 -5.66 ***

LOSS -ve -0.071 -1.93 *

-0.065 -1.36

LEV -ve 0.302 5.17 ***

0.328 4.02 ***

AUDIT +ve 0.077 3.96 ***

0.084 2.96 **

EM -ve -0.173 -1.45

-0.207 -1.98 *

FCF +ve 1.593 8.33 ***

1.440 3.02 **

SGAt+1 -ve -0.017 -1.15

-0.015 -0.58

DVS_GEO ? -0.056 -3.05 ***

-0.052 -1.60

DVS_BIZ ? -0.029 -1.65 *

-0.023 -0.80

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.226

0.216

F-Statistics

69.49

66.26

p-value

<0.0001

<0.0001

N 8,455 8,455




229


∑ ∑ ∑

(Model 8a)

(Model 8b)


Petersen Clustering


Intercept -2.647 -7.78 ***

-2.728 -5.83 ***

ψ ? -0.018 -1.34

-0.017 -0.71

SIZE +ve -0.254 -3.44 ***

-0.243 -2.51 *

SIZE2 ? 0.012 2.66 ***

0.012 1.92

ANALYST +ve 0.013 5.65 ***

0.012 4.02 **

AGE +ve 0.007 0.34

0.010 0.25

StdROE ? -0.001 -1.51

-0.001 -2.11

StdCF ? 0.000 2.84 ***

0.000 .

StdRET ? 2.423 1.42

1.373 0.48

MB +ve 0.000 0.69

0.000 .

ROA +ve -0.588 -1.77 *

-0.528 -1.89

WC -ve -0.202 -5.80 ***

-0.205 -3.45 **

DIV -ve 0.130 4.81 ***

0.123 3.04 **

MERGER -ve -0.206 -5.20 ***

-0.196 -6.00 ***

RESTRUCT -ve -0.130 -5.44 ***

-0.127 -3.97 **

LOSS -ve -0.035 -0.72

-0.032 -0.94

LEV -ve 0.137 1.87 *

0.133 1.42

AUDIT +ve 0.067 3.13 ***

0.063 2.23

EM -ve -0.276 -1.43

-0.320 -2.80 **

FCF +ve 2.916 7.04 ***

2.859 7.90 ***

SGAt+1 -ve -0.048 -2.85 ***

-0.045 -1.53

GINDEX -ve 0.008 1.91 *

0.008 1.03

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.290

0.288

F-Statistics

62.41

61.85

p-value

<0.0001

<0.0001

N 4,960 4,960




230

It is observed from Model 5a in Table 5.18 that idiosyncratic volatility is negatively

associated with subsequent year‟s capital expenditure at p <0.05 when White (1980)

procedure is applied to the regression of the main model. These findings indicate that

Hypothesis 1 is supported when corporate expenditure is represented by CAPEX. The

significance level for coefficient of idiosyncratic volatility, ψ increases to p < 0.01 when

variable SGAt+1 (SGA of the subsequent year) is added as a control variable in Model 6a.

The negative relationship between idiosyncratic volatility and CAPEX of the subsequent

year remains significant when two diversification variables in the form of geographical

diversification (DVS_GEO) and business lines diversification (DVS_BIZ) are added in

as additional control variables in Model 7a. This shows that at low level of idiosyncratic

volatility, the investment level in capital projects is higher, and vice versa, providing

support to Hypothesis 1.

Nevertheless, when Petersen‟s (2009) procedure is employed to control for cross-

sectional and time-series correlation, Model 5b shows insignificant positive results for

coefficient idiosyncratic volatility, ψ. Further, variables SGAt+1 (SGA in the subsequent

year) are added in Model 6b while two diversification variables, namely, DVS_GEO

(geographic diversification) and DVS_BIZ (business diversification) are added in Model

7b as additional control variables. Both Models 6b and 7b show insignificant negative

relationship between idiosyncratic volatility and CAPEX in the subsequent year. This

indicates that idiosyncratic volatility does not play a fundamental role in determining

CAPEX level of the subsequent year. Both Models 8a and 8b also demonstrate

231

insignificant negative relationships between idiosyncratic volatility and CAPEX of the

subsequent year when GINDEX is included by using White (1980) and Petersen (2009)

procedures respectively, probably due to a smaller sample size of 4,960.

The control variable analyst following is positively associated to CAPEX confirming the

expectation in item 4.5.4.1(b) that firms with higher analyst following invest more in

CAPEX. Also, within prediction made in item 4.5.4.1(r), free cash flow is found to be

positively associated with CAPEX, suggesting that firms with free cash flow invest

wastefully on capital projects for empire building (Jensen, 1986). As expected in item

4.5.4.1, CAPEX level is lower when firms undertake merger and restructuring activities,

make losses and when SGA costs of the subsequent year increased. Better audit quality

and lower earnings management are also shown to be positively associated with higher

CAPEX level, in line with predictions made in item 4.5.4.2. Higher stock return

volatility is negatively related to CAPEX indicating investors‟ reluctance in investing

capital projects due to uncertain information environment in evaluating managers‟

actions (Gillan et al., 2003). Contrary to predictions made in both items 4.5.4.1(j) and

4.5.4.1(n), firms that are more likely to pay dividends and those highly-leveraged firms

incur more CAPEX.

232

Table 5.19 presents the regression results of Hypothesis 1 for SGA costs using the

following eight models:

a) Models 9a and 9b using the initial sample derived from the sample selection


b) Models 10a and 10b when variable CAPEX of the subsequent year is controlled.

c) Models 11a and 11b when two diversification variables, DVS_GEO and

DVS_BIZ are added as control variables.

d) Models 12a and 12b when corporate governance variable, GINDEX is included as

a control variable.

233

Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1)

∑ ∑ ∑

(Model 9a)

(Model 9b) Expected

White-adjusted

Petersen Clustering


Intercept 0.346 2.39 ** 0.412 1.57

ψ ?

-0.029 -5.45 ***

-0.028 -2.40 *

SIZE +ve

-0.603 -25.34 ***

-0.604 -15.19 ***

SIZE2 ?

0.023 13.25 ***

0.023 7.59 ***

ANALYST +ve

0.025 18.22 ***

0.024 6.29 ***

AGE +ve

0.123 10.58 ***

0.122 5.06 ***

StdROE ?

0.001 3.69 ***

0.001 3.37 **

StdCF ?

0.000 -1.02

0.000 .

StdRET ?

-2.992 -5.94 ***

-3.533 -5.08 ***

MB +ve

0.000 0.78

0.000 0.69

ROA -ve

-0.311 -6.95 ***

-0.321 -5.38 ***

WC -ve

0.000 -1.03

0.000 -0.73

DIV -ve

-0.112 -7.03 ***

-0.115 -3.45 **

MERGER ?

0.017 0.79

0.014 0.64

RESTRUCT +ve

0.209 15.56 ***

0.206 6.15 ***

LOSS ?

0.045 2.56 ***

0.045 1.72

LEV -ve

0.003 0.08

0.011 0.16

AUDIT +ve

0.036 2.63 ***

0.034 1.39

EM -ve

-0.181 -3.52 ***

-0.184 -3.72 ***

EMP +ve

1.846 5.67 ***

1.811 3.40 **

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.478

0.476

F-Statistics

383.06

407.54

p-value

<0.0001

<0.0001

N 13,780 13,780




234

Table 5.19 Effect of Stock Price Informativeness on SGA Costs (H1) (Continued)

∑

∑

∑

(Model 10a)

(Model 10b) Expected White-adjusted

Petersen Clustering


Intercept 0.251 1.70 0.328 1.28

ψ ? -0.030 -5.54 ***

-0.028 -2.48 **

SIZE +ve -0.601 -25.20 ***

-0.602 -15.17 ***

SIZE2 ? 0.022 13.11 ***

0.023 7.49 ***

ANALYST +ve 0.025 18.40 ***

0.024 6.28 ***

AGE +ve 0.118 10.34 **

0.118 5.00 ***

StdROE ? 0.001 3.62 ***

0.001 3.31 **

StdCF ? 0.000 -0.85

0.000 .

StdRET ? -3.039 -5.71 ***

-3.682 -4.70 ***

MB +ve 0.000 0.80

0.000 0.72

ROA -ve -0.276 -6.14 ***

-0.288 -4.42 ***

WC -ve 0.000 -0.90

0.000 -0.65

DIV -ve -0.105 -6.65 ***

-0.108 -3.28 **

MERGER ? 0.010 0.48

0.008 0.34

RESTRUCT +ve 0.205 15.25 ***

0.203 5.98 ***

LOSS ? 0.042 2.39 **

0.042 1.65

LEV -ve 0.006 0.17

0.014 0.21

AUDIT +ve 0.036 2.59 ***

0.033 1.36

EM -ve -0.225 -3.95 ***

-0.226 -3.85 ***

EMP +ve 1.933 5.68 ***

1.890 3.47 **

CAPEXt+1 -ve -0.029 -3.91 ***

-0.028 -1.85

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.479

0.477

F-Statistics

373.00

400.18

p-value

<0.0001

<0.0001

N 13,753 13,753




235


(Continued)

∑

∑

∑

(Model 11a)

(Model 11b)


Petersen Clustering


Intercept 0.339 1.91 *

0.408 1.34

ψ ? -0.028 -4.20 ***

-0.028 -2.94 **

SIZE +ve -0.610 -19.83 ***

-0.612 -11.49 ***

SIZE2 ? 0.022 10.57 ***

0.023 6.02 ***

ANALYST +ve 0.024 14.10 ***

0.023 5.36 ***

AGE +ve 0.103 6.62 ***

0.104 3.70 ***

StdROE ? 0.001 2.25 **

0.001 1.79

StdCF ? 0.000 2.81 ***

0.000 .

StdRET ? -2.577 -3.96 ***

-3.056 -4.02 ***

MB +ve 0.000 0.34

0.000 .

ROA -ve -0.285 -4.48 ***

-0.293 -3.49 **

WC -ve 0.002 3.99

0.002 3.52 **

DIV -ve -0.035 -1.82 *

-0.038 -1.13

MERGER ? 0.060 2.43 **

0.059 2.37 *

RESTRUCT +ve 0.191 11.71 ***

0.189 5.11 ***

LOSS ? 0.008 0.36

0.005 0.22

LEV -ve 0.010 0.24

0.015 0.21

AUDIT +ve -0.002 -0.09

-0.005 -0.17

EM -ve -0.279 -3.49 ***

-0.294 -3.51 **

EMP +ve 3.111 2.83 ***

3.035 2.19 *

CAPEXt+1 -ve -0.015 -1.62

-0.014 -0.82

DVS_GEO +ve 0.116 7.97 ***

0.118 4.27 ***

DVS_BIZ +ve -0.051 -3.26 ***

-0.055 -1.92

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.450

0.447

F-Statistics

190.44

195.44

p-value

<0.0001

<0.0001

N 8,354 8,354




236


(Continued)

∑

∑

∑

(Model 12a)

(Model 12b)


Petersen Clustering


Intercept 1.658 5.57 ***

1.577 3.53 **

ψ ? 0.028 2.05 **

0.025 1.07

SIZE +ve -0.877 -14.69 ***

-0.873 -9.81 ***

SIZE2 ? 0.040 10.82 ***

0.040 7.29 ***

ANALYST +ve 0.018 9.12 ***

0.017 4.94 ***

AGE +ve 0.124 5.74 ***

0.127 3.64 **

StdROE ? 0.001 2.05 **

0.001 1.97

StdCF ? 0.000 -2.57 **

0.000 .

StdRET ? -5.676 -3.69 ***

-5.915 -2.11

MB +ve 0.000 1.31

0.000 .

ROA -ve 0.359 2.66 **

0.380 2.06

WC -ve -0.061 -4.37 ***

-0.062 -2.61 *

DIV -ve -0.075 -2.98 ***

-0.079 -2.15 *

MERGER ? -0.026 -0.70

-0.018 -0.43

RESTRUCT +ve 0.152 7.13 ***

0.154 4.23 **

LOSS ? 0.012 0.33

0.014 0.24

LEV -ve -0.044 -0.71

-0.046 -0.49

AUDIT +ve 0.045 2.18 **

0.042 1.24

EM -ve -0.565 -4.67 ***

-0.598 -3.24 **

EMP +ve 2.672 5.91 ***

2.608 3.62 **

CAPEXt+1 -ve -0.050 -3.42 ***

-0.048 -1.91

GINDEX -ve 0.000 -0.03

-0.001 -0.10

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.401

0.400

F-Statistics

100.82

99.28

p-value

<0.0001

<0.0001

N 4,919 4,919




237

There is a significant negative relationship between idiosyncratic volatility, ψ and SGA

costs of the subsequent year when applying White (1980) procedure (refer Model 9a of

Table 5.19). This finding indicates that Hypothesis 1 is supported. The main model

remains robust after including additional variables such as CAPEXt+1 (CAPEX of

subsequent year) in Model 10a as well as both geographic location (DVS_GEO) and

business lines diversification (DVS_BIZ) shown in Model 11a. Similar results are

obtained by employing the Petersen (2009) procedure in Models 9b, 10b and 11b even

though the statistical significance level achieved are lower. This proves that Hypothesis

1 is well supported when corporate expenditure is proxied by SGA costs. Firm managers

extract valuable feedback from the stock markets at low (high) levels of idiosyncratic

volatility and respond accordingly by achieving high (low) levels of selling, general and

administrative cost.

A positive relationship exists between firm size and SGA costs when firm size exceeded

a certain threshold value of idiosyncratic volatility. Prior to that threshold value, firm

size is negatively associated with SGA costs implying that small firms increase their

SGA expenditure as stock price informativeness improves. As expected in item 4.5.4.1,

higher levels of SGA costs are seen in firms that are older, have higher analyst following

and greater employee intensity. Firms with high SGA costs are more likely to undertake

restructuring activities but have a less tendency to pay dividend. SGA expenditure

reduces current year profit, hence is negatively associated with ROA, as expected in

item 4.5.4.1(h). In line with scarce resource constraint commonly faced by firms, the

variable, CAPEX of the subsequent year (CAPEXt+1) is negatively associated with SGA

238

expenditure level. Firms operating under a competitive environment tend to invest more

in SGA costs (Banker et al., 2011b), thus geographic diversification (DVS_GEO) is

found to be positively related to SGA costs in the subsequent year but diversification in

the form of business lines (DVS_BIZ) has a negative association with SGA of the

subsequent year. Stock return volatility (StdROE) is also found to be negatively

associated with SGA of the subsequent year, implying an ambiguous information

environment faced by investors (Bhagat & Bolton, 2008). This phenomenon prohibits

managers learning new firm-specific information and responding accordingly (refer to

item 4.5.4.1(f)).

In all the models reported in Table 5.19, corporate governance variable such as audit

quality (AUDIT) is positively associated with SGA in the subsequent year. Further, a

negative relationship is reported between earnings management (EM) and SGA of the

subsequent year. This is within the expectations made in item 4.5.4.2. However, when

GINDEX is included as a control variable, Model 12a presents a positive relationship

between idiosyncratic volatility and SGA of the subsequent year at p <0.05 when White

(1980) procedure is applied. Model 12b shows an insignificant result for coefficient

idiosyncratic volatility, ψ by applying Petersen (2009) procedure to control for cross-

sectional and time-series correlation. This finding is largely due to the smaller sample

size arising from missing values for GINDEX.

239

5.3.1.1 Direction of Idiosyncratic Volatility

Applying ideas from the learning theory, Hypothesis 1 posits that firm managers obtain

new private information from stock markets and react accordingly by making changes in

their R&D expenditure, CAPEX and SGA costs. Table 5.20 demonstrates how

idiosyncratic volatility is related to R&D expenditure level as firms‟ idiosyncratic

volatility improves (Models 13a and 13b) and deteriorates (Models 14a and 14b) from

the previous year.

240

Table 5.20 Effect of Stock Price Informativeness on R&D Expenditure

– by Direction of Idiosyncratic Volatility‟s Movement

∑

∑ ∑

Increasing ψ

(Model 13a)

(Model 13b)

White-adjusted

Petersen Clustering

Coefficient t-statistic Coefficient t-statistic


0.531 0.80

ψ -0.072 -5.23 ***

-0.057 -3.04 **

SIZE -0.555 -7.50 ***

-0.521 -5.00 ***

SIZE2 0.016 3.00 ***

0.013 1.93

ANALYST 0.069 18.72 ***

0.069 10.56 ***

AGE -0.105 -2.97 ***

-0.110 -2.25 *

StdROE 0.002 3.44 ***

0.002 3.81 ***

StdCF 0.000 0.19

0.000 .

StdRET 1.119 0.93

-0.407 -0.33

MB 0.003 4.70 ***

0.003 3.96 ***

ROA -0.489 -4.05 ***

-0.502 -2.37 *

WC 0.000 1.15

0.000 .

DIV -0.495 -10.25 ***

-0.502 -7.87 ***

MERGER 0.064 1.08

0.042 1.00

RESTRUCT 0.102 2.73 ***

0.094 2.17 *

LOSS 0.468 9.65 ***

0.473 9.50 ***

LEV -0.365 -4.48 ***

-0.364 -3.70 **

AUDIT 0.017 0.44

0.015 0.28

EM 0.230 2.12 **

0.226 2.35 *

Year dummies Included

Included

Industry dummies Included

Included

Adjusted R2 0.445

0.441

F-Statistics 94.55

103.23

p-value <0.0001

<0.0001

N 3,729 3,729

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value

less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using

the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.

241



(Continued)

∑

∑ ∑

Decreasing ψ

(Model 14a)

(Model 14b)

White-adjusted

Petersen Clustering


Intercept

0.546 1.45

0.726 1.47

ψ

-0.107 -5.92 ***

-0.091 -3.21 **

SIZE

-0.545 -8.49 ***

-0.543 -4.33 ***

SIZE2

0.012 2.61 ***

0.013 1.38

ANALYST

0.070 18.52 ***

0.071 12.84 ***

AGE

-0.032 -1.09

-0.032 -0.70

StdROE

0.004 2.70 ***

0.004 3.50 **

StdCF

0.000 0.28

0.000 .

StdRET

-4.062 -2.83 ***

-6.694 -2.69 **

MB

0.000 0.76

0.000 0.74

ROA

-0.687 -6.72 ***

-0.710 -5.93 ***

WC

0.000 3.02 ***

0.000 .

DIV

-0.467 -11.23 ***

-0.481 -6.20 ***

MERGER

0.039 0.68

0.034 0.50

RESTRUCT

0.157 4.62 ***

0.152 2.89 **

LOSS

0.479 10.65 ***

0.482 8.09 ***

LEV

-0.300 -4.81 ***

-0.294 -3.35 **

AUDIT

0.078 2.25 **

0.074 1.51

EM

-0.153 -1.88 *

-0.149 -2.09 *

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R2

0.450

0.446

F-Statistics

123.31

130.12

p-value

<0.0001

<0.0001

N 4,784 4,784




242

Table 5.20 reveals varying responsiveness of firm managers in making R&D investment

as firm-level idiosyncratic volatility moves in different directions. The coefficient

idiosyncratic volatility, ψ of 0.107 (0.091) presented in Model 14a (Model 14b) is larger

than 0.072 (0.057) shown in Model 13a (Model 13b) using White (1980) procedure

(Petersen (2009) procedure). This relationship between idiosyncratic volatility and R&D

level of the subsequent year are depicted in Figure 5.2.

Figure 5.2 Association between Current Year‟s Idiosyncratic

Volatility and R&D Expenditure of the Subsequent Year

Figure 5.2 illustrates how managers respond when firm-level idiosyncratic volatility

moves upwards or downwards. The relationship between idiosyncratic volatility and

R&D expenditure level of the subsequent year is stronger (weaker) when firm-level

stock price informativeness decreases (increases) from the previous year. This implies

R&Dt+1

Idiosyncratic Volatility, ψt

Decreasing ψ

Increasing ψ

243

that managers are more responsive when firms‟ idiosyncratic volatilities deteriorate

compared to when idiosyncratic volatilities of their firms improves from the previous

year.

Table 5.21 exhibits the association between idiosyncratic volatility and CAPEX level

when firm-level idiosyncratic volatility either advances (Models 15a and 15b) or

deteriorates (Models 16a and 16b) from the previous year.

244

Table 5.21 Effect of Stock Price Informativeness on CAPEX


∑ ∑ ∑

Increasing ψ

(Model 15a)

(Model 15b)

White-adjusted

Petersen Clustering


Intercept -3.506 -13.30 ***

-3.367 -10.22 ***

Ψ -0.009 -0.90

0.001 0.12

SIZE 0.047 0.90

0.062 0.76

SIZE2 -0.004 -1.23

-0.006 -0.96

ANALYST 0.009 3.82 ***

0.010 2.90 **

AGE -0.026 -1.21

-0.038 -1.62

StdROE 0.000 5.00 ***

0.000 .

StdCF 0.000 2.53 **

0.000 .

StdRET -2.819 -3.06 ***

-5.130 -6.15 ***

MB 0.000 0.26

0.000 0.19

ROA -0.719 -3.81 ***

-0.629 -1.72

WC 0.000 -4.25 ***

0.000 .

DIV 0.099 3.44 ***

0.090 1.77

MERGER -0.206 -5.30 ***

-0.232 -5.92 ***

RESTRUCT -0.133 -5.33 ***

-0.128 -5.26 ***

LOSS -0.250 -6.22 ***

-0.244 -8.42 ***

LEV 0.246 3.45 ***

0.258 3.22 **

AUDIT 0.015 0.64

0.020 0.75

EM -0.397 -2.61 ***

-0.404 -5.97 ***

FCF 1.333 6.20 ***

1.222 3.43 **


Included


Included

Adjusted R2 0.216

0.211

F-Statistics 53.83

55.89

p-value <0.0001

<0.0001

N 6,313 6,313

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-

value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)

heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using

the Petersen (2009) procedure.

245



(Continued)

∑ ∑ ∑

Decreasing ψ

(Model 16a)

(Model 16b)

White-adjusted

Petersen Clustering


Intercept -3.478 -15.18 ***

-3.271 -9.75 ***

ψ -0.030 -2.46 **

0.005 0.29

SIZE 0.006 0.13

0.012 0.23

SIZE2 -0.004 -1.07

-0.004 -1.23

ANALYST 0.013 6.20 ***

0.014 4.55 ***

AGE -0.001 -0.07

-0.008 -0.26

StdROE -0.001 -0.35

-0.001 -0.30

StdCF 0.000 3.03 ***

0.000 .

StdRET -2.192 -2.14 **

-7.738 -6.28 ***

MB 0.000 0.60

0.000 .

ROA -0.564 -3.52 ***

-0.479 -1.27

WC 0.000 6.04 ***

0.000 .

DIV 0.126 5.23 ***

0.111 2.32

MERGER -0.208 -6.19 ***

-0.218 -5.86 ***

RESTRUCT -0.183 -8.32 ***

-0.186 -7.05 ***

LOSS -0.102 -2.71 ***

-0.089 -3.10 **

LEV 0.361 6.10 ***

0.375 4.46 ***

AUDIT 0.065 3.08 ***

0.066 2.64 **

EM -0.146 -1.10

-0.179 -2.12 *

FCF 1.623 10.09 ***

1.510 3.34 **


Included


Included

Adjusted R2 0.271

0.260

F-Statistics 98.63

110.01

p-value <0.0001

<0.0001

N 8,653 8,653



the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)

procedure.

246

The impact of idiosyncratic volatility on CAPEX when firm-level idiosyncratic

volatility moves upwards or downwards is illustrated in Table 5.21. When White (1980)

procedures are applied, idiosyncratic volatility is negatively associated with CAPEX at p

< 0.05 when firm-level stock price informativeness is decreasing (Model 16a) and this

impact is greater compared to when idiosyncratic volatility is increasing (Model 15a).

However, by employing Petersen (2009) procedure, the coefficients of idiosyncratic

volatility, ψ are insignificant for both Models 15b and 16b, indicating that the direction

of movement in idiosyncratic volatility is irrelevant in determining subsequent year‟s

CAPEX investment. This is consistent with the insignificant results observed between

current year‟s idiosyncratic volatility and subsequent year‟s CAPEX in the main model

shown in Table 5.18.

Table 5.22 reports the impact of stock price informativeness on subsequent year‟s SGA

costs level when firm-level idiosyncratic volatility is either increasing (Models 17a and

17b) or decreasing (Models 18a and 18b) from the previous year.

247

Table 5.22 Effect of Stock Price Informativeness on SGA Costs


∑

∑ ∑

Increasing ψ

(Model 17a)

(Model 17b)

White-adjusted

Petersen Clustering



0.463 1.43

ψ -0.029 -3.82 ***

-0.021 -1.21

SIZE -0.617 -16.71 ***

-0.606 -15.06 ***

SIZE2 0.024 9.27 ***

0.023 7.16 ***

ANALYST 0.026 13.33 ***

0.025 4.51 ***

AGE 0.094 5.10 ***

0.094 3.24 **

StdROE 0.001 3.27 ***

0.001 4.31 ***

StdCF 0.000 -0.34

0.000 .

StdRET -1.373 -1.97 **

-2.460 -2.80 **

MB 0.003 2.32 **

0.003 2.58 **

ROA -0.258 -4.28 ***

-0.273 -3.73 ***

WC 0.001 0.98

0.001 0.96

DIV -0.080 -3.26 ***

-0.084 -2.11 *

MERGER 0.017 0.55

0.007 0.29

RESTRUCT 0.195 9.67 ***

0.195 5.13 ***

LOSS 0.028 1.06

0.028 1.16

LEV 0.028 0.51

0.028 0.38

AUDIT 0.041 1.98 **

0.041 1.65

EM -0.126 -1.52

-0.141 -1.97 *

EMP 1.735 4.27 ***

1.699 2.83 **


Included


Included

Adjusted R2 0.448

0.444

F-Statistics 143.60

148.06

p-value <0.0001

<0.0001

N 5,809 5,809



heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using


248



(Continued)

∑ ∑ ∑

Decreasing ψ

(Model 18a)

(Model 18b)

White-adjusted

Petersen Clustering


Intercept 0.550 3.05 ***

0.616 2.66 **

ψ -0.058 -6.30 ***

-0.060 -3.76 ***

SIZE -0.637 -19.58 ***

-0.644 -13.99 ***

SIZE2 0.024 10.45 ***

0.025 6.94 ***

ANALYST 0.022 11.54 ***

0.022 6.69 ***

AGE 0.136 9.12 ***

0.137 5.97 ***

StdROE 0.001 1.51

0.001 2.15 *

StdCF 0.000 -1.14

0.000 .

StdRET -4.497 -5.90 ***

-4.660 -5.75 ***

MB 0.000 -0.36

0.000 .

ROA -0.336 -5.07 ***

-0.343 -4.16 ***

WC 0.000 -3.47 ***

0.000 .

DIV -0.139 -6.70 ***

-0.140 -3.87 ***

MERGER 0.018 0.60

0.018 0.61

RESTRUCT 0.218 12.16 ***

0.216 6.23 ***

LOSS 0.058 2.40 **

0.056 1.85

LEV -0.014 -0.30

-0.002 -0.03

AUDIT 0.028 1.52

0.026 0.81

EM -0.206 -3.06 ***

-0.205 -3.79 ***

EMP 1.962 3.83 ***

1.933 2.80 **


Included


Included

Adjusted R2 0.498

0.496

F-Statistics 240.61

276.01

p-value <0.0001

<0.0001

N 7,971 7,971




249

Table 5.22 presents the impact of idiosyncratic volatility on SGA costs level when firms‟

stock price informativeness improves and diminishes from the previous year. When

White‟s (1980) procedure is applied, the effect of current year‟s idiosyncratic volatility

to subsequent year‟s SGA costs is greater when firm-level stock price informativeness is

decreasing (see Model 18a) compared when it is improving (see Model 17a). A more

apparent contrast is observed when Petersen‟s (2009) procedure is used in Models 17b

and 18b to control for time-series and cross-sectional correlation. A significant negative

relationship is noted between current year‟s idiosyncratic volatility and subsequent

year‟s SGA costs level when firms‟ stock price informativeness declines in Model 18b

whilst the coefficient of idiosyncratic volatility, ψ is insignificantly negative in Model

17b when the firms‟ stock price informativeness improves. The association between

idiosyncratic volatility and SGA is illustrated in Figure 5.3 when firm-level

idiosyncratic volatility moves in different directions.

Figure 5.3 Association between Current Year‟s Idiosyncratic

Volatility and SGA Costs of the Subsequent Year

SGAt+1

Idiosyncratic Volatility, ψt

Decreasing ψ

Increasing ψ

250

Figure 5.3 shows that the inverse relationship between current year‟s idiosyncratic

volatility and the subsequent year‟s SGA costs level is stronger (weaker) when firms are

experiencing worsened (improved) idiosyncratic volatility. This implies firm managers

respond differently to varying movements in idiosyncratic volatility.

5.3.1.2 Summary of Findings

Hypothesis 1 of this study conjectures that there is a negative association between stock

price informativeness of a current year and corporate expenditure in the subsequent year,

proxied by R&D expenditure, CAPEX and SGA costs. The multivariate tests conducted

suggest a significant negative relationship between current year‟s stock price

informativeness and R&D level in the subsequent year. This impact of stock price

informativeness on subsequent year‟s R&D expenditure level is greater when firm-level

idiosyncratic volatility diminishes as compared to a situation when it increases from the

previous year. A significant negative association is also noted between a current year‟s

stock price informativeness and SGA level of the subsequent year especially when firms‟

idiosyncratic volatilities worsen from the previous year. The result of the association

between a current year‟s stock price informativeness and the subsequent year‟s CAPEX

level is non-robust and insignificant. These findings indicate that Hypothesis 1 of the

study is supported when corporate expenditure is represented by R&D expenditure and

SGA costs, but not by capital expenditure.

251

5.3.2 Robustness Tests

It is possible that the association between stock price informativeness and corporate

expenditure might be driven by firms investing in high level of corporate expenditure

could have greater stock price informativeness. A change model and two-stage least

squares (2SLS) regression approach are carried out to address this endogeneity issue.

5.3.2.1 Change Model

Change model deals with reverse causality problem by examining the changes in

subsequent years‟ corporate expenditure (proxied by R&D, CAPEX and SGA) as a

result of a change in stock price informativeness, as presented in Equation 5.2.

∑ ∑ ∑

(5.2)

where:

is change in corporate expenditure for firm i from year t to year t+1, is

intercept and is the coefficient of interest. is change in idiosyncratic volatility

from year t-1 to year t, is changes in a set of control variables (excluded

dummy variables) from year t-1 to year t and is unspecific random factors.

Hypothesis 1 of this study predicts a negative relationship between stock price

informativeness and corporate expenditure, hence the sign for coefficient is expected

to be negative. Table 5.23 shows the results of change model when corporate

252

expenditure is represented by R&D costs for the full sample (Models 19a and 19b) as

well as when idiosyncratic volatility is either increasing (Models 20a and 20b) or

decreasing (Models 21a and 21b). This table also highlights changes in R&D

expenditure when there is a 20 per cent reduction in the relative idiosyncratic volatility,

1-R2 (see Models 22a and 22b).

253

Table 5.23 Changes in R&D Expenditure Following Changes in Stock Price

Informativeness

∑ ∑

∑

Full Sample

(Model 19a)

(Model 19b)

White-adjusted

Petersen Clustering


Intercept

-0.019 -0.13

-0.009 -0.20

∆ψ

0.002 0.35

0.008 1.16

∆SIZE

0.394 4.16 ***

0.388 3.62 **

∆SIZE2

-0.024 -3.02 ***

-0.022 -3.31 **

∆ANALYST

0.000 -0.10

-0.001 -0.20

∆AGE

0.128 1.20

0.138 1.12

∆StdROE

0.000 0.02

0.000 -0.38

∆StdCF

0.000 -1.08

0.000 .

∆StdRET

-0.428 -0.93

-1.095 -2.75

∆MB

0.000 -1.63

0.000 .

∆ROA

0.023 0.46

0.031 0.75

∆WC

0.000 3.89 ***

0.000 .

∆LEV

-0.061 -1.29

-0.050 -1.37

∆EM

0.037 1.31

0.037 1.94 *

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.053

0.033

F Statistics

14.62

5.87

p-value

<0.0001

<0.0001

N 6,636 6,636

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less

than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the

procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.

254


Informativeness (Continued)

∑ ∑

∑

Increasing ψ

(Model 20a) (Model 20b)

White-adjusted

Petersen Clustering


Intercept -0.202 -1.57

-0.159 -1.56

∆ψ 0.012 1.13

0.015 2.92 ** ∆SIZE 0.487 3.49 ***

0.511 2.56 ** ∆SIZE

2 -0.030 -2.43 **

-0.031 -2.19 * ∆ANALYST 0.001 0.56

0.001 0.28

∆AGE 0.295 1.81 *

0.360 1.30

∆StdROE 0.001 0.67

0.000 0.65

∆StdCF 0.000 -1.30

0.000 .

∆StdRET 0.197 0.41

0.082 0.24

∆MB 0.000 -0.18

0.000 .

∆ROA 0.064 0.86

0.059 2.31 * ∆WC 0.000 5.83 ***

0.000 .

∆LEV -0.091 -1.26

-0.066 -0.91

∆EM 0.037 0.78

0.042 1.02


Included


Included

Adjusted R2 0.059

0.042

F Statistics 7.90

16.67

p-value <0.0001

<0.0001

N 2,952 2,952 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value



procedure.

255



∑ ∑

∑

Decreasing ψ


White-adjusted

Petersen Clustering


Intercept

0.079 0.39

0.093 0.83

∆ψ

-0.016 -2.10 **

-0.010 -0.83

∆SIZE

0.307 2.42 **

0.271 2.06 *

∆SIZE2

-0.018 -1.69 *

-0.013 -1.21

∆ANALYST

-0.001 -0.64

-0.001 -0.80

∆AGE

-0.076 -0.54

-0.135 -1.32

∆StdROE

0.000 0.10

0.000 .

∆StdCF

0.000 -0.55

0.000 .

∆StdRET

-1.038 -1.75 *

-1.936 -3.91 ***

∆MB

0.000 -1.77 *

0.000 -1.62

∆ROA

-0.008 -0.14

0.007 0.10

∆WC

0.000 1.51

0.000 .

∆LEV

-0.069 -1.30

-0.063 -1.89

∆EM

0.030 0.82

0.027 0.79

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.050

0.031

F Statistics

8.21

3.83

p-value

<0.0001

<0.0001

N 3,684 3,684




procedure.

256

Table 5.23 Changes in R&D Following Changes in Stock Price


∑ ∑

∑

Decreasing ψ


20% reduction in 1-R2

20% reduction in 1-R2

White-adjusted

Petersen Clustering


Intercept

-0.106 -3.08 ***

-0.071 -2.83 **

∆ψ

-0.058 -3.19 ***

-0.056 -3.95 ***

∆SIZE

0.402 1.79 *

0.283 1.61

∆SIZE2

-0.022 -1.35

-0.013 -0.91

∆ANALYST

0.001 0.18

0.000 0.02

∆AGE

0.196 0.79

-0.029 -0.16

∆StdROE

-0.001 -0.39

-0.003 -4.34 ***

∆StdCF

0.000 0.16

0.000 .

∆StdRET

-0.462 -0.47

-2.829 -2.48 **

∆MB

0.000 1.84 *

0.000 .

∆ROA

0.080 0.93

0.094 1.54

∆WC

-0.001 -0.55

-0.001 -0.74

∆LEV

0.134 1.00

0.103 1.74

∆EM

0.067 0.98

0.054 2.72 **

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.089

0.055

F Statistics

3.85

180.08

p-value

<0.0001

<0.0001

N 794 794




257

It is observed from Table 5.23 that there is no significant change in the subsequent

year‟s R&D expenditure as a result of changes in a current year‟s idiosyncratic volatility

when White (1980) and Petersen (2009) procedures are conducted respectively (see

Models 19a and 19b). The lack of statistical significance could be attributable to the

confounding effect when there is no distinction made between idiosyncratic volatility-

increasing and idiosyncratic volatility-decreasing phenomena. Model 21a using White

(1980) method shows that when firms‟ idiosyncratic volatility is decreasing, a

significant negative association between changes in the subsequent year‟s R&D

expenditure following changes in a current year‟s idiosyncratic volatility, within the

prediction of Hypothesis 1. An insignificant positive association is, however, observed

between the changes in these two variables as firm-level idiosyncratic volatility

increases (Model 20a). These findings suggest that firm managers are exploiting

valuable information that they have yet to possess from the stock markets and react by

making changes to R&D expenditure in the subsequent year. However, this only takes

place in firms that are experiencing a downward movement in idiosyncratic volatility

from the previous year, but not otherwise.

The use of Petersen (2009) clustering procedure presents a slightly different result. A

significant positive relationship is found between changes in a current year‟s

idiosyncratic volatility and changes in the subsequent year‟s R&D expenditure when

firm-level stock price informativeness is strengthening (see Model 20b). This finding is

in contrast to the negative relationship expected by Hypothesis 1. It signifies that when

stock price informativeness improves, firm managers respond favourably to positive

258

feedback obtained from stock markets and intensify R&D activities in the subsequent

year to reap greater benefits from R&D investments.

An insignificant negative relationship, however, is observed between current year‟s

stock price informativeness and R&D investment in the subsequent year when firm-

level idiosyncratic volatility is decreasing (Model 21b). Firm managers seem to be

indifferent when stock price informativeness deteriorates. Further analyses are

performed to find out the impact of changes in the subsequent year‟s R&D expenditure

following changes in idiosyncratic volatility when the relative idiosyncratic volatility,33

1-R2 drops by trial and error, for example, by 5%, 10%, 15%, 20% and so forth. It is

observed that when firms experience a decrease in the relative idiosyncratic volatility by

20 per cent, the coefficient of ∆ψ (changes in idiosyncratic volatility) becomes

significantly negative as shown in Models 22a and 22b displayed in Table 5.23. This

finding reveals that when stock price informativeness worsens, managers do not initiate

immediate changes in R&D expenditure in the subsequent year if there is little

movement in idiosyncratic volatility. Managers are likely to assess whether the drop in

stock price informativeness is temporary or long-term in nature and by doing so, they

will only react by increasing R&D investment when it is critically compelling, that is,

when the firm-level 1-R2 decreases by 20 per cent. This asymmetric cost response

reflects the cost “stickiness” behaviour of firm managers in modifying R&D investment

33 Relative idiosyncratic volatility is used because it is more straightforward as compared to idiosyncratic

volatility which is measured in the form of natural logarithm.

259

as stock price informativeness changes. This result is consistent with prior studies

analysing asymmetric cost response particularly in the area of SGA costs (see Anderson

et al. (2003) and Balakrishnan and Gruca (2008)).

An attempt is made to find out the changes in subsequent year‟s R&D expenditure as a

result of a further decrease in 1-R2, for example, by 25 per cent. However, the sample

size becomes very small to observe any significant relationship between changes in

R&D expenditure in the subsequent year following a current year change in

idiosyncratic volatility.

Figures 5.4 and 5.5 depict the relationships between changes in idiosyncratic volatility

in a current year and changes in R&D expenditure in the subsequent year when Petersen

clustering procedure is carried out.

260

Figure 5.4 Association between Changes in Current Year‟s Idiosyncratic



Figure 5.4 illustrates how managers respond when firm-level idiosyncratic volatility

moves upwards. A significant positive relationship is observed between changes in a

current year‟s idiosyncratic volatility and changes in the subsequent year‟s R&D

expenditure. This shows that managers derive the feedback from the stock markets

when stock price informativeness is improving and intensify their R&D expenditure in

the subsequent year to benefit more from the investment.

∆R&D t+1

∆Idiosyncratic Volatility t

Increasing ψ

261



when 1-R2 Drops 20%

Figure 5.5 presents the changes in subsequent year‟s R&D expenditure following

changes in idiosyncratic volatility for a sample of 794 firms that experience a significant

drop of relative idiosyncratic volatility (1-R2), that is, by 20 per cent. Changes in a

current year‟s idiosyncratic volatility are negatively associated with changes in the

subsequent year‟s R&D expenditure, indicating that managers are responding to

decreasing stock price informativeness by increasing their R&D activities in the

following year.

∆R&D t+1


Decreasing ψ

262

The significant result exhibited in Table 5.23 show that the main model is robust to

reverse causality. Variable ∆CAPEXt+1 (Changes in CAPEX in the subsequent year) is

also added in as a control variable in all the models displayed in Table 5.23 and the

results (untabulated) are qualitatively similar.

Next, Table 5.24 shows the results of changes in CAPEX following changes in stock

price informativeness for the full sample (Models 23a and 23b) as well as when

idiosyncratic volatility either increases (Models 24a and 24b) or deteriorates (see

Models 25a and 25b). This table also highlights changes in CAPEX when there is a 20

per cent34

reduction in the relative idiosyncratic volatility, 1-R2, in Models 26a and 26b.

34 This follows a trial and error procedure undertaken in analysing change model for R&D expenditure.

263

Table 5.24 Changes in CAPEX Following Changes in Stock Price Informativeness

∑

∑

∑

Full Sample

(Model 23a)

(Model 23b)

White-adjusted

Petersen Clustering


Intercept

0.140 1.72 *

0.049 0.50

∆ψ

0.004 0.59

0.028 2.24 *

∆SIZE

0.191 1.48

0.151 1.73

∆SIZE2

0.001 0.10

0.006 0.83

∆ANALYST

0.000 -0.25

0.001 0.31

∆AGE

-0.291 -1.90 *

-0.259 -1.29

∆StdROE

0.000 0.60

0.001 1.00

∆StdCF

0.000 -1.23

0.000 .

∆StdRET

-1.902 -3.12 ***

-6.287 -1.84

∆MB

0.000 1.29

0.000 1.31

∆ROA

0.435 5.48 ***

0.554 6.53 ***

∆WC

0.000 2.99 ***

0.000 .

∆LEV

-0.065 -0.71

-0.103 -1.61

∆EM

-0.209 -3.63 ***

-0.248 -4.48 ***

∆FCF

-0.110 -1.01

-0.230 -1.35

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.093

0.065

F Statistics

43.58

20.65

p-value

<0.0001

<0.0001

N 11,697 11,697


than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using


264


(Continued)

∑ ∑

∑

Increasing ψ


White-adjusted

Petersen Clustering


Intercept 0.366 2.66 ***

0.254 1.33

∆ψ 0.015 1.06

0.021 1.74

∆SIZE 0.447 1.98 **

0.456 1.76

∆SIZE2 -0.013 -0.82

-0.013 -0.74

∆ANALYST 0.000 -0.12

0.001 0.17

∆AGE -0.358 -1.58

-0.355 -2.22 * ∆StdROE 0.002 2.38 **

0.003 2.85 ** ∆StdCF 0.000 -1.54

0.000 .

∆StdRET 0.225 0.21

-2.749 -0.91

∆MB 0.000 2.66 ***

0.000 .

∆ROA 0.436 4.23 ***

0.544 6.58 *** ∆WC 0.000 4.84 ***

0.000 .

∆LEV 0.022 0.20

-0.045 -0.42

∆EM -0.239 -3.49 ***

-0.269 -4.05 *** ∆FCF -0.118 -0.89

-0.214 -1.29


Included


Included

Adjusted R2 0.065

0.047

F Statistics 13.29

9.05

p-value <0.0001

<0.0001

N 4,978 4,978 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value



265


(Continued)

∑ ∑

∑

Decreasing ψ


White-adjusted

Petersen Clustering



-0.071 -0.68

∆ψ -0.010 -0.90

0.008 0.61

∆SIZE -0.032 -0.21

-0.110 -0.67

∆SIZE2 0.014 1.31

0.022 1.70

∆ANALYST 0.000 0.04

0.002 0.45

∆AGE -0.299 -1.43

-0.259 -0.74

∆StdROE -0.001 -1.36

-0.002 -3.59 **

∆StdCF 0.000 -0.70

0.000 .

∆StdRET -3.440 -3.67 ***

-8.362 -2.35 *

∆MB 0.000 0.89

0.000 2.00 *

∆ROA 0.451 3.86 **

0.579 3.73 ***

∆WC 0.000 0.47

0.000 .

∆LEV -0.107 -0.87

-0.135 -0.81

∆EM -0.200 -2.31 **

-0.249 -3.89 ***

∆FCF -0.135 -0.80

-0.278 -1.43


Included


Included

Adjusted R

2 0.110

0.079

F Statistics 30.61

13.82

p-value <0.0001

<0.0001

N 6,719 6,719



heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the

Petersen (2009) procedure.

266


(Continued)

∑ ∑

∑

Decreasing ψ


20% reduction in 1-R

2 20% reduction in 1-R

2

White-adjusted Petersen Clustering


Intercept -0.067 -0.52

-0.102 -1.42

∆ψ -0.016 -0.49

-0.019 -0.87

∆SIZE -0.618 -1.58

-0.736 -1.29

∆SIZE2 0.040 1.65 *

0.050 1.20

∆ANALYST 0.000 0.10

0.002 0.22

∆AGE -0.609 -1.41

-0.821 -1.84

∆StdROE -0.011 -2.14 **

-0.013 -3.00 **

∆StdCF 0.000 -0.89

0.000 .

∆StdRET -7.085 -4.81 *** -12.682 -14.35 ***

∆MB -0.001 -1.32

0.000 -0.82

∆ROA 0.654 3.56 *** 0.691 2.68 **

∆WC 0.000 2.22 **

0.000 2.83 **

∆LEV -0.025 -0.13

-0.170 -0.97

∆EM -0.160 -1.40

-0.235 -1.85

∆FCF 0.014 0.05

-0.129 -0.59


Included


Included

Adjusted R

2 0.169

0.136

F Statistics 11.43

12.72

p-value <0.0001

<0.0001

N 1,442 1,442




267

It is observed in Table 5.24 that almost all models (Models 23a to 26b) examined using

White (1980) and Petersen (2009) methods show insignificant results for the coefficient

∆ψ (change in idiosyncratic volatility). The only exception is observed in Model 23b

(full sample) where a positive change in CAPEX of the subsequent year at p < 0.10 is

observed following changes in idiosyncratic volatility using the Petersen (2009)

procedure on the full sample. The overall insignificant results could be driven by reverse

causality or possibly because current year‟s idiosyncratic volatility is not a key

determinant for subsequent year‟s CAPEX, as reflected by non-robust results observed

in the main model (Refer Table 5.18 of item 5.3.1).

Table 5.25 shows the results of movements in SGA costs following changes in stock

price informativeness for the full sample (see Models 27a and 27b), increasing

idiosyncratic volatility (Models 28a and 28b), decreasing idiosyncratic volatility

(Models 29a and 29b) as well as when there is a 20 per cent reduction in relative

idiosyncratic volatility, 1-R2

(Models 30a and 30b).

268

Table 5.25 Changes in SGA Costs Following Changes in Stock Price Informativeness

∑

∑

∑

Full Sample

(Model 27a)

(Model 27b)

White-adjusted

Petersen Clustering


Intercept

-0.003 -0.11

0.011 0.35

∆ψ

0.006 2.85 ***

0.008 3.43 **

∆SIZE

0.134 3.05 ***

0.128 2.75 **

∆SIZE2

0.001 0.21

0.002 0.75

∆ANALYST

-0.003 -3.71 ***

-0.003 -3.02 **

∆AGE

0.134 2.69 ***

0.128 2.97 **

∆StdROE

0.000 0.23

0.000 -0.27

∆StdCF

0.000 -2.53 **

0.000 .

∆StdRET

-0.353 -1.37

-0.928 -1.89

∆MB

0.000 -1.22

0.000 .

∆ROA

-0.021 -0.86

-0.016 -0.53

∆WC

0.000 0.56

0.000 0.74

∆LEV

0.150 4.65 ***

0.161 5.53 ***

∆EM

0.015 0.69

0.014 0.81

∆EMP

1.703 1.67 *

1.792 2.80 **

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.069

0.045

F Statistics

29.60

17.61

p-value

<0.0001

<0.0001

N 10,814 10,814




269

Table 5.25 Changes in SGA Costs Following Changes in Stock Price


∑ ∑

∑

Increasing ψ


White-adjusted

Petersen Clustering


Intercept

-0.086 -1.66 *

-0.070 -2.05 * ∆ψ

0.013 2.94 ***

0.014 2.72 ** ∆SIZE

0.121 1.80 *

0.145 3.26 ** ∆SIZE

2

0.002 0.37

0.001 0.33

∆ANALYST

-0.002 -1.57

-0.002 -3.91 *** ∆AGE

0.158 2.03 **

0.177 1.74

∆StdROE

0.001 1.37

0.001 1.06

∆StdCF

0.000 -2.35 **

0.000 .

∆StdRET

-0.066 -0.22

-0.219 -0.31

∆MB

0.000 0.66

0.000 .

∆ROA

0.000 -0.01

-0.003 -0.11

∆WC

0.001 3.05 ***

0.001 4.08 *** ∆LEV

0.133 2.94 ***

0.149 4.81 *** ∆EM

-0.012 -0.36

-0.010 -0.82

∆EMP

1.856 1.06

1.813 1.01

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.068

0.042

F Statistics

12.96

8.31

p-value

<0.0001

<0.0001

N 4,605 4,605 Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less



270



∑

∑

∑

Decreasing ψ


White-adjusted

Petersen Clustering


Intercept

0.042 1.22

0.050 1.79

∆ψ

-0.004 -0.99

-0.002 -0.59

∆SIZE

0.119 2.08 **

0.093 1.29

∆SIZE2

0.002 0.48

0.005 1.08

∆ANALYST

-0.003 -3.48 ***

-0.004 -2.30 *

∆AGE

0.096 1.49

0.066 1.97 *

∆StdROE

0.000 -0.18

0.000 .

∆StdCF

0.000 -1.75 *

0.000 .

∆StdRET

-0.671 -1.91 *

-1.390 -5.50 ***

∆MB

0.000 -2.10 **

0.000 -6.37 ***

∆ROA

-0.043 -1.23

-0.035 -0.97

∆WC

0.000 -0.06

0.000 -0.16

∆LEV

0.167 3.92 ***

0.176 4.79 ***

∆EM

0.037 1.38

0.033 1.26

∆EMP

1.721 1.74 *

1.812 1.69

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.072

0.049

F Statistics

18.10

11.62

p-value

<0.0001

<0.0001

N 6,209 6,209




271



∑ ∑

∑

Decreasing ψ

(Model 30a)

(Model 30b)


2


2

White-adjusted

Petersen Clustering


Intercept

-0.046 -1.44

-0.059 -7.87 ***

∆ψ

-0.019 -1.75 *

-0.020 -2.70 **

∆SIZE

-0.030 -0.20

-0.116 -1.52

∆SIZE2

0.008 0.87

0.015 2.61 **

∆ANALYST

0.001 0.66

0.001 1.02

∆AGE

0.249 2.01 **

0.185 1.29

∆StdROE

-0.006 -2.67 ***

-0.008 -4.66 ***

∆StdCF

0.000 -0.27

0.000 .

∆StdRET

-0.487 -0.76

-2.161 -3.99 ***

∆MB

-0.001 -2.14 **

-0.001 -1.06

∆ROA

-0.013 -0.20

-0.010 -0.20

∆WC

0.000 -1.17

0.000 .

∆LEV

0.387 5.66 ***

0.353 7.41 ***

∆EM

0.029 0.56

0.002 0.10

∆EMP

2.045 0.72

1.583 1.33

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.113

0.072

F Statistics

7.16

19.06

p-value

<0.0001

<0.0001

N 1,357 1,357




272

Table 5.25 shows a significant positive change in subsequent year‟s SGA cost following

a change in idiosyncratic volatility when both White (1980) and Petersen (2009)

procedures are applied on the full sample in Models 27a and 27b. This is in contrast to

the prediction made in Hypothesis 1, that is, a negative relationship is expected between

changes in a current year‟s idiosyncratic volatility and changes in the subsequent year‟s

SGA costs.

When the subsamples are differentiated between increasing (Models 28a and 28b) and

decreasing (Models 29a and 29b) idiosyncratic volatilities, different results of these two

sub-samples are derived. When firms‟ idiosyncratic volatility is improving, both Models

28a and 28b show significant positive association between change in the subsequent

year‟s SGA costs following a change in idiosyncratic volatility by applying White (1980)

and Petersen (2009) procedures respectively. This implies that when stock price

informativeness improves, firm managers react to the information obtained from the

stock markets by increasing SGA costs in the subsequent year as idiosyncratic volatility

rises. Consistent with results observed in R&D expenditure in Table 5.23, these findings

signify that when stock price informativeness improves, firm managers extract positive

feedback from stock markets and increase SGA costs in the subsequent year in view of

its ability to generate long-term firm value.

When firms‟ idiosyncratic volatility is declining, an insignificant negative association is

found between changes in a current year‟s idiosyncratic volatility and changes in SGA

costs of the subsequent year (Models 29a and 29b of Table 5.25). Further analyses are

273

explored to detect the impact on changes in the subsequent year‟s SGA costs following a

change in idiosyncratic volatility when the relative idiosyncratic volatility, 1-R2

falls, for

example, by 5%, 10%, 15%, 20% and so forth. The percentage used for the examination

is determined arbitrarily using a trial and error procedure. It is observed that when the

relative idiosyncratic volatility of the current year drops by 20 per cent, the coefficient

of ∆ψ (changes in idiosyncratic volatility) becomes significantly negative as shown in

Models 30a and 30b of Table 5.25 with the application of White (1980) and Petersen

(2009) procedures respectively. This asymmetric cost response is partly due to the cost

“stickiness” behaviour of firm managers in altering SGA as stock price informativeness

changes. When stock price informativeness deteriorates, it seems that managers do not

immediately initiate any changes to the subsequent year‟s SGA costs if there are

immaterial movements in idiosyncratic volatility. They will only respond when the

relative idiosyncratic volatility, 1-R2 decreases by at least 20 per cent, probably because

managers need to assess the nature of the decline in the informativeness of stock prices.

If the decrease is assumed to be temporary, managers will not take any action to change

SGA costs. They will only respond when stock price informativeness continues to

deteriorate until a certain threshold value is reached, which is a 20 per cent drop in

relative idiosyncratic volatility (1-R2) in this sample. The “sticky” cost behaviour

demonstrated is in line with those presented by Anderson et al. (2003) where firm

managers are found to make asymmetric cost adjustments when sales revenue move in

different directions due to the managers‟ expectation on future sales demand.

274

Additional analyses are done to explore the changes in subsequent year‟s SGA costs

following a further decrease in 1-R2

of, for example, 25 per cent. No significant results,

however, are observed due to the small sample size.

Figures 5.6 and 5.7 illustrate the relationships between changes in idiosyncratic

volatility in a current year and changes in SGA costs in the subsequent year when

Petersen clustering procedure is carried out.


Volatility and Changes in SGA Costs of Subsequent Year


∆SGA t+1


Increasing ψ

275

Figure 5.6 shows changes in a current year idiosyncratic volatility is positively related to

changes in SGA costs of the subsequent year when idiosyncratic volatility is increasing.

This indicates that when stock price informativeness improves, firm managers respond

to the market feedback by boosting SGA costs in the subsequent year as idiosyncratic

volatility rises.


Volatility and Changes in SGA Costs of Subsequent Year

when 1-R2 Drops 20%

Figure 5.7 exhibits how firm managers react to decreasing relative idiosyncratic

volatility (1-R2) of 20 per cent. A negative association is found between changes in a

current year‟s idiosyncratic volatility and changes in the subsequent year‟s SGA

∆SGA t+1


Decreasing ψ

276

expenditure. This shows that firm managers are reacting to the decreasing stock price

informativeness by escalating their SGA expenditure in the following year.

In summary, the significant results exhibited in Table 5.25 show that the main model is

robust to reverse causality. Variable ∆CAPEXt+1 (changes in CAPEX in the subsequent

year) is also added in as a control variable for all the models in Table 5.25 and the

results (untabulated) are qualitatively similar.

5.3.2.2 Two-Stage Least Squares Regression

A common issue in empirical research in accounting and finance is the potential for

endogeneity problems. It is possible that corporate expenditure and stock price

informativeness are endogenously determined because firms with high level of corporate

expenditure, for example, may have better informed stock prices. In order to address this

issue, a 2SLS regression is conducted to tackle the potential reverse causality issue. In

performing the 2SLS, a suitable instrument variable is required to replace the original

independent variable in this study, that is, idiosyncratic volatility.

Ferreira and Laux (2007) establish that idiosyncratic volatility is significantly correlated

with a governance index developed by Gompers et al. (2003) while control variables

such as returns on equity, variance of returns on equity, leverage, market-to-book ratio,

market capitalization, dividend dummy, firm age and a diversification dummy, are

included in the model. Therefore, this study follows the model of Ferreira and Laux

(2007) by selecting all variables used as instrument variables.

277

In the first stage of 2SLS regression, a predicted value of idiosyncratic volatility

is obtained by using Ferreira and Laux (2007) model as follows:

∑ ∑ (5.3)

where:

represents idiosyncratic volatility of the current year for firm i in the year t and

GINDEX is the Governance index developed by Gompers et al. (2003) in the previous

year, t-1. The control variables included are ROE (return on equity), VROE (variance of

return on equity), LEV (leverage), MB (market-to-book ratio), MCAP (market

capitalization), DIV (dividend dummy), AGE (firm age) and DIVER (diversification

dummy). Year and Industry dummies are included in the model while is the error term.

In the second stage of 2SLS regression, the fitted value of idiosyncratic volatility,

substitutes the actual value of in the main model, as follows:

∑ ∑ ∑

(5.4)

278

where:

is the corporate expenditure of the subsequent year and is represented by

R&D expenditure, CAPEX and SGA costs. Coefficient is the intercept and is the

coefficient of interest. The original independent variable, idiosyncratic volatility is now

represented by the predicted value of idiosyncratic volatility,

denotes a set of control variables. Year and Industry

dummies are included in the model and represents unspecified random factors.

Table 5.26 presents the results of the first stage estimates following Ferreira and Laux

(2007) in Model 31. This table also reports the second stage results with the predicted

value of idiosyncratic volatility, substituting the actual value of idiosyncratic

volatility, as the independent variable in relation to:

a) R&D expenditure (Models 32a and 32b);

b) CAPEX (Models 33a and 33b); and

c) SGA costs (Models 34a and 34b).

279

Table 5.26 Results of Two-Stage Least Squares Regressions

∑

∑

First Stage Regression

Dependent variable= ψt

(Model 31)

Expected White- adjusted

Direction Coefficient t-statistic

Intercept 3.581 17.03 ***

GINDEXt-1 -ve -0.017 -3.07 ***

ROEt-1 +ve -0.002 -0.97

VROEt-1 -ve 0.000 -6.03 ***

LEVt-1 +ve 0.348 4.53 ***

MBt-1 -ve 0.000 -1.48

MCAPt-1 -ve -0.214 -15.71 ***

DIVt-1 +ve -0.199 -6.89 ***

AGEt-1 +ve -0.008 -0.30

DIVERt-1 -ve -0.138 -5.04 ***

Year dummies

Included

Industry dummies

Included

Adjusted R2

0.292

F-Statistics

88.71

p-value

<0.0001

N 4,886

Note: All variables are defined in Table 4.1. ***, ** and * indicate statistical

significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are

adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b)

clustering by firm and year using the Petersen (2009) procedure.

280

Table 5.26 Result of Two-Stage Least Squares Regressions (Continued)

∑

∑

∑

Second Stage Regression

Dependent variable= R&Dt+1


Expected White- adjusted Petersen Clustering


Intercept 3.458 6.06 ***

3.476 3.75 ***

PREDψt -ve -0.264 -2.15 **

-0.143 -1.98 *

SIZEt -ve -1.172 -8.89 ***

-1.179 -5.80 ***

SIZEt2 +ve 0.045 5.17 ***

0.047 3.62 **

ANALYSTt +ve 0.075 18.39 ***

0.075 10.29 ***

StdCFt +ve 0.000 1.45

0.000 1.33

StdRETt -ve -2.636 -0.75

-5.726 -1.03

EPt +ve -0.480 -1.44

-0.489 -1.01

ROAt -ve -0.018 -0.07

0.000 0.00

WCt +ve 0.021 2.22 **

0.021 2.35 *

MERGERt +ve 0.214 3.37 ***

0.225 5.12 ***

RESTRUCTt +ve 0.117 2.53 **

0.116 1.77

LOSSt +ve 0.382 4.67 ***

0.374 2.59 **

AUDITt +ve 0.084 1.77 *

0.082 1.01

EMt -ve -0.197 -0.98

-0.229 -1.30

INDDIRPCTt -ve -0.165 -3.65 ***

-0.167 -2.29 *

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.366

0.363

F-Statistics

44.55

63.07

p-value

<0.0001

<0.0001

N 2,114

2,114




281


∑

∑

∑


Dependent variable= CAPEXt+1




Intercept

-3.061 -7.04 ***

-3.081 -4.28 ***

PREDψt -ve -0.130 -1.89 *

0.134 1.58

SIZEt -ve -0.109 -1.15

-0.133 -0.88

SIZEt2 +ve 0.004 0.71

0.008 0.87

ANALYSTt +ve 0.007 2.64 ***

0.009 2.61 **

StdCFt +ve 0.000 0.38

0.000 .

StdRETt -ve 2.999 1.46

-2.410 -1.16

EPt +ve 0.612 3.25 ***

0.544 2.66 **

ROAt -ve -0.506 -1.79 *

-0.426 -1.80

WCt +ve 0.001 0.09

0.001 0.09

MERGERt -ve -0.244 -5.83 ***

-0.251 -6.06 ***

RESTRUCTt -ve -0.116 -4.20 ***

-0.124 -3.32 **

LOSSt -ve -0.053 -0.92

-0.068 -1.23

AUDITt +ve 0.063 2.45 **

0.069 1.72

EMt -ve -0.284 -1.96 ***

-0.341 -2.09 *

FCFt +ve 2.417 8.48 ***

2.398 5.92 ***

INDDIRPCTt ? -0.014 -0.53

-0.006 -0.14

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.263

0.255

F-Statistics

45.52

47.05

p-value

<0.0001

<0.0001

N 3,628

3,628




282


∑

∑

∑


Dependent variable= SGAt+1




Intercept

3.320 10.31 ***

3.147 6.43 ***

PREDψt -ve -0.579 -8.91 ***

-0.259 -4.21 ***

SIZEt -ve -0.908 -14.17 ***

-0.928 -8.38 ***

SIZEt2 +ve 0.036 8.68 ***

0.040 5.52 ***

ANALYSTt +ve 0.012 5.56 ***

0.015 3.66 ***

StdCFt -ve 0.000 -0.58

0.000 .

StdRETt -ve -6.793 -4.19 ***

-11.988 -6.31 ***

EPt +ve -0.362 -3.11 ***

-0.440 -3.25 **

ROAt -ve 0.110 0.80

0.218 1.11

WCt -ve -0.017 -0.82

-0.003 -0.09

MERGERt +ve 0.020 0.51

0.011 0.26

RESTRUCTt +ve 0.179 7.17 ***

0.167 3.48 **

LOSSt +ve 0.079 1.80 *

0.062 1.05

AUDITt +ve -0.014 -0.55

-0.007 -0.18

EMt -ve -0.239 -2.24 **

-0.281 -2.09 *

EMPt +ve 2.225 1.53

2.896 1.09

INDDIRPCTt +ve 0.022 0.91

0.032 0.92

Year dummies

Included

Included

Industry dummies

Included

Included

Adjusted R

2

0.384

0.376

F-Statistics

74.95

72.05

p-value

<0.0001

<0.0001

N

3,436

3,436




283

Table 5.26 further shows the first stage regression that uses idiosyncratic volatility as the

dependent variable. The variable GINDEX has a significant negative relationship with

idiosyncratic volatility in Model 31 and this is consistent with the findings in Ferreira

and Laux (2007). Other variables have the same sign compared to the results in Ferreira

and Laux (2007) with the exception of the dividend dummy.

In the second stage regressions, the coefficient of is negatively correlated with

the subsequent year‟s R&D expenditure at varying levels of statistical significance in

Models 32a and 32b when the White (1980) and Petersen (2009) procedures are

conducted respectively. Nevertheless, non-robust stage-two regressions results are

exhibited when corporate expenditure is proxied by CAPEX. A negative relationship at

p < 0.10 is noted between and CAPEX of subsequent year in Model 33a when

using White (1980) method but an insignificant positive association is observed between

these two variables in Model 33b when Petersen (2009) procedure is applied. Further,

Table 5.26 highlights significant negative relationships between the predicted value of

idiosyncratic volatility, and subsequent year‟s SGA costs in Models 34a and

34b when the White (1980) and Petersen (2009) procedures are conducted respectively.

These findings in Table 5.26 indicate that the inverse relationship between a current

year‟s stock price informativeness and the subsequent year‟s corporate expenditure is

robust to potential endogeneity when it is represented by R&D expenditure and SGA

costs but not by CAPEX.

284

A comparison of results of control variables in Table 5.26 are made with those of the

main model presented in Table 5.17 to 5.19 in item 5.3.1. All control variables generally

show the same sign in the second stage regression results when they correlates with

corporate expenditure of the subsequent year. Several control variables such as firm age

(AGE), leverage (LEV), and dividend dummy (DIV) are not repeated in the second

stage regression as they are already used in the first stage regression to impose exclusion

restrictions on the model (Wooldridge, 2009, p. 521). Variable return volatility (StdROE)

is not included in the second stage regression as it is closely related to the variable

variance of return on equity (VROE) used in the stage one regression. Variables such as

earnings-to-price ratio (EP) and percentage of independent directors (INDDIRPCT) are

employed in the second stage regression to substitute market-to-book ratio (MB) and

GINDEX already employed in the first stage regression.

5.3.3 Summary of Findings – Hypothesis 1

Hypothesis 1 conjectures that there is a negative association between current year‟s

idiosyncratic volatility and subsequent year‟s corporate expenditure represented by

R&D expenditure, CAPEX and SGA costs. A summary of the results of the multivariate

and robustness tests conducted for each of the proxy of corporate expenditure are

highlighted in items (a) to (c).

285

a) R&D expenditure

A significant inverse relationship between current year idiosyncratic volatility and R&D

expenditure of the subsequent year is documented after controlling for relevant control

variables, capital expenditure of the subsequent year and diversification strategy. This

provides support to Hypothesis 1 of the study.

The robustness tests of change model and two-stage least squares regression confirm the

robustness of the lead-lag model to endogeneity. The test on change model provides

additional insights as change in R&D expenditure of the subsequent year following the

change in a current year‟s idiosyncratic volatility reveal different results when

idiosyncratic volatility moves upwards or downwards. When firm-level stock price

informativeness is improving, a change in current year‟s idiosyncratic volatility is

positively related to changes in R&D expenditure of the following year. However, when

stock price informativeness is deteriorating, managers do not react immediately to

modify the level of R&D expenditure until the relative idiosyncratic volatility (1-R2)

drops by at least 20 per cent. The two-stage least squares regression further substantiates

the significant negative association found between a current year‟s stock price

informativeness and R&D expenditure of subsequent year, after considering

endogeneity.

b) Capital expenditure

It is noted that current year‟s idiosyncratic volatility level is not significantly associated

with CAPEX level of the subsequent year. The test on change model also present non-

286

robust results on the association between changes in idiosyncratic volatility and changes

in subsequent year‟s CAPEX for both scenarios of improving and weakening

idiosyncratic volatilities. Weak statistical results are also revealed by the two-stage least

squares regression on CAPEX as seen in Table 5.26. As such, it is concluded that a

current year‟s stock price informativeness is not significantly related to CAPEX of

subsequent year, thus not supporting Hypothesis 1 of the study.

c) SGA costs

It is found that a current year‟s idiosyncratic volatility level has a significant negative

relationship with SGA costs level in the subsequent year after controlling for relevant

control variables, CAPEX of the subsequent year and diversification strategy adopted by

firms. These findings show that Hypothesis 1 is well supported.

The main model of SGA costs is robust to reverse causality and is not potentially

endogenous. A positive change in SGA costs in the subsequent year is observed as a

result of a change in idiosyncratic volatility when stock price informativeness improves.

On the other hand, when stock price informativeness diminishes, managers will only

initiate changes in the subsequent year‟s SGA costs when the relative idiosyncratic

volatility (1-R2) drops at least by 20 per cent. The two-stage least squares regression

further suggests a significant negative association between idiosyncratic volatility and

SGA costs of the subsequent year, after considering endogeneity. As such, it is

concluded that a current year‟s stock price informativeness is significantly related to

287

SGA costs of the subsequent year, providing support to Hypothesis 1 when corporate

expenditure is represented by SGA costs.

The findings on R&D and SGA provide evidence of learning hypothesis where

managers learn new private information of their own stock prices via input from the

stock markets. Additional insights on cost „stickiness‟ behaviour of firm managers are

revealed by using a change model that examines the relationship between changes in

current year‟s stock price informativeness and changes in the subsequent year‟s R&D

expenditure and SGA costs.

The results also indicate that stock price informativeness in a current year is not

associated with capital expenditure in the subsequent year. This indicates that firm

managers do not depend on capital markets to provide information for their capital

investment decisions. This is possibly due to needs of proper strategic planning and

consideration of funding before firms venture into capital projects. In addition, capital

expenditure are long-term oriented and thus any changes made in capital investment can

only be done after appropriate board approval. Therefore, stock prince informativeness

is not a significant determinant for capital expenditure in this study.

288

5.4 Hypotheses 2a to 2c – The Role of Information Asymmetry

Hypotheses 2a to 2c examine whether information asymmetry plays a role in the

relationship between current year‟s stock price informativeness and corporate

expenditure of the following year. Three proxies of information asymmetry are used in

this study, namely, firm size, analyst following and bid-ask spreads. Two sub-samples

are formed for each type of the corporate expenditure (R&D expenditure, CAPEX and

SGA costs) based on the median value of the full sample on firm size (natural logarithm

of total assets), analyst following (number of analysts that follow a firm) and bid-ask

spreads.

5.4.1 Firm Size

Table 5.27 presents the results of the effect of current year‟s stock price informativeness

on subsequent year‟s R&D level for the following types of firms:

a) Large firms – Models 35a and 35b.

b) Small firms – Models 36a and 36b.

c) Small firms with increasing idiosyncratic volatility – Models 37a and 37b.

d) Small firms with decreasing idiosyncratic volatility – Models 38a and 38b.

289


– by Firm Size (H2a)

∑

∑

∑

Large Firms

Large Firms

(Model 35a)

(Model 35b)

White-adjusted

Petersen clustering


Intercept 2.663 4.31 *** 2.617 2.35 *

ψ 0.034 1.35 0.018 0.61

SIZE -1.052 -6.94 *** -1.039 -3.57 **

SIZE2 0.047 5.08 *** 0.047 2.59 **

ANALYST 0.071 23.18 *** 0.069 12.96 ***

AGE 0.111 3.81 *** 0.111 1.81

StdROE 0.002 6.05 *** 0.002 5.46 ***

StdCF 0.000 -2.28 ** 0.000 .

StdRET -7.152 -3.55 *** -6.487 -3.49 **

MB 0.000 1.30 0.000 1.02

ROA -0.603 -2.59 *** -0.543 -1.75

WC 0.000 7.22 *** 0.000 .

DIV -0.505 -12.68 *** -0.513 -6.21 ***

MERGER 0.090 1.83 * 0.104 1.90

RESTRUCT 0.166 4.79 *** 0.167 2.79 **

LOSS 0.335 5.60 *** 0.329 5.04 ***

LEV -0.546 -6.80 *** -0.554 -3.45 **

AUDIT 0.022 0.66 0.020 0.36

EM -0.272 -1.67 * -0.360 -2.38 *


Included


Included

Adjusted R2 0.368

0.366

F-Statistics 78.54

107.23 p-value <0.0001

<0.0001

N 4,257 4,257 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets

of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value

less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the


290


– by Firm Size (H2a) (Continued)

∑

∑

∑

Small Firms

Small Firms

(Model 36a)

( Model 36b)

White-adjusted

Petersen clustering


Intercept -3.262 -8.17 *** -3.083 -5.06 ***

Ψ -0.077 -6.84 *** -0.060 -3.70 ***

SIZE 0.341 2.37 ** 0.338 1.46

SIZE2 -0.078 -4.87 *** -0.077 -2.99 **

ANALYST 0.063 11.35 *** 0.065 9.37 ***

AGE -0.243 -6.92 *** -0.240 -4.58 ***

StdROE 0.010 2.16 ** 0.010 1.50

StdCF 0.008 4.63 *** 0.008 4.31 ***

StdRET 0.902 0.96 -1.151 -0.93

MB 0.001 1.19 0.001 0.99

ROA -0.571 -6.40 *** -0.578 -3.96 ***

WC 0.000 1.12 0.000 .

DIV -0.425 -8.08 *** -0.440 -4.85 ***

MERGER -0.011 -0.17 -0.042 -0.54

RESTRUCT 0.153 4.28 *** 0.141 3.74 ***

LOSS 0.474 10.74 *** 0.481 8.53 ***

LEV -0.192 -2.97 *** -0.184 -2.43 *

AUDIT 0.108 2.87 *** 0.099 1.38

EM 0.088 1.34 0.093 1.26


Included


Included

Adjusted R2 0.375

0.368

F-Statistics 80.63

90.82 p-value <0.0001

<0.0001


of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-

value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the


291



∑ ∑ ∑

Small Firms

Small Firms

Increasing ψ

Increasing ψ

(Model 37a)

(Model 37b)

White-adjusted

Petersen clustering


Intercept -3.407 -6.30 *** -3.153 -7.40 ***

ψ -0.069 -4.67 *** -0.058 -3.26 **

SIZE 0.319 1.50 0.328 1.81

SIZE2 -0.081 -3.45 *** -0.081 -3.81 ***

ANALYST 0.076 9.26 *** 0.080 12.38 ***

AGE -0.275 -5.31 *** -0.272 -4.79 ***

StdROE 0.012 1.50 0.011 1.53

StdCF 0.007 3.04 *** 0.007 3.49 **

StdRET 1.477 1.22 0.202 0.20

MB 0.002 2.58 *** 0.001 2.04 *

ROA -0.492 -3.84 *** -0.496 -2.66 **

WC 0.000 -0.14 0.000 .

DIV -0.359 -4.37 *** -0.375 -3.64 **

MERGER -0.020 -0.18 -0.054 -0.45

RESTRUCT 0.106 1.95 * 0.096 1.69

LOSS 0.476 7.51 *** 0.486 9.08 ***

LEV -0.246 -2.56 ** -0.236 -2.21 *

AUDIT 0.105 1.77 * 0.090 1.18

EM 0.333 2.91 *** 0.334 4.07 ***


Included


Included

Adjusted R2 0.358

0.350

F-Statistics 33.22

206.00 p-value <0.0001

<0.0001

N 1,851 1,851 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets of the

full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-value less than 1%,

5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the procedure of White (1980)

and (b) clustering by firm and year using the Petersen (2009) procedure.

292



∑

∑

∑

Small Firms

Small Firms

Decreasing ψ

Decreasing ψ

(Model 38a)

(Model 38b)

White-adjusted

Petersen clustering


Intercept -3.018 -6.28 *** -2.757 -3.52 **

ψ -0.145 -6.94 *** -0.122 -4.59 ***

SIZE 0.376 2.06 ** 0.377 1.31

SIZE2 -0.082 -3.93 *** -0.082 -2.54 **

ANALYST 0.047 6.37 *** 0.049 6.88 ***

AGE -0.216 -4.71 *** -0.219 -3.60 **

StdROE 0.008 1.34 0.008 1.15

StdCF 0.009 3.41 *** 0.009 4.36 ***

StdRET -0.260 -0.16 -3.823 -1.78

MB 0.001 1.06 0.001 0.88

ROA -0.703 -8.22 *** -0.724 -6.86 ***

WC 0.000 1.73 * 0.001 1.55

DIV -0.496 -7.40 *** -0.513 -5.64 ***

MERGER -0.023 -0.26 -0.049 -0.65

RESTRUCT 0.192 4.18 *** 0.180 4.89 ***

LOSS 0.430 8.19 *** 0.441 6.11 ***

LEV -0.099 -1.53 -0.095 -1.29

AUDIT 0.108 2.25 ** 0.100 1.44

EM -0.063 -0.82 -0.049 -0.60


Included


Included

Adjusted R2 0.399

0.393

F-Statistics 50.88

59.86

p-value <0.0001

<0.0001


of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a

p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using


293

Table 5.27 shows that the coefficient idiosyncratic volatility, ψ is significantly negative

for small firms (see Models 36a and 36b) but insignificant for large firms (see Models

35a and 35b). These findings indicate that the inverse relationship between a current

year‟s stock price informativeness and R&D expenditure of the subsequent year is

stronger when firm size is small. This scenario is justified by the greater extent of

private information available in small firms as reported by Bakke and Whited (2010).

Managers managing small firms can learn more from the market feedback on firms‟

growth prospects, sales demand and competition and react quicker in modifying firms‟

R&D expenditure in the subsequent year.

The sub-sample of small firms is further divided into two groups, one with increasing

idiosyncratic volatility and the other one with decreasing idiosyncratic volatility. It is

observed that the coefficient of idiosyncratic volatility, ψ is greater in Models 38a and

38b when idiosyncratic volatility is decreasing compared to Models 37a and 37b when

the idiosyncratic volatility is improving. Further, by applying Petersen (2009) procedure,

the significance level achieved by the coefficient idiosyncratic volatility in Model 38b

(decreasing stock price informativeness) is higher than that of Model 37b (strengthening

stock price informativeness). Consequently, managers of small firms are more

responsive to new private information embedded in the stock prices, thereby making

R&D investment in response to stock price informativeness, especially when

idiosyncratic volatility is worsening.

294

Table 5.28 reports the effect of current year‟s stock price informativeness on subsequent

year‟s CAPEX level of large and firms.

Table 5.28 further highlight that the coefficient idiosyncratic volatility, ψ is significantly

negative for large firms in Model 39a using White (1980) procedure but becomes

insignificantly positive in Model 39b when the Petersen (2009) procedure are applied to

control for cross-sectional and time series correlation. Insignificant association is also

noted between idiosyncratic volatility and the subsequent year‟s CAPEX for small firms

(Models 40a and 40b). These findings suggest non-robust results on the association

between a current year‟s stock price informativeness and CAPEX of the subsequent year

when the sample data is analysed in terms of their firm size.

295

Table 5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size (H2a)

∑

∑

∑

Large Firms

Large Firms

(Model 39a)

(Model 39b)

White-adjusted

Petersen clustering


Intercept -3.307 -8.48 *** -3.016 -4.69 ***

ψ -0.027 -2.03 ** 0.025 1.16

SIZE -0.132 -1.46 -0.147 -0.91

SIZE2 0.006 1.19 0.007 0.70

ANALYST 0.007 3.89 *** 0.007 2.02 *

AGE 0.010 0.62 0.014 0.45

StdROE 0.000 6.06 *** 0.000 .

StdCF 0.000 3.02 *** 0.000 .

StdRET 2.044 1.99 ** -3.899 -4.46 ***

MB -0.001 -1.05 -0.001 -1.28

ROA 0.103 0.51 0.205 0.50

WC 0.001 4.01 *** 0.001 3.33 **

DIV 0.111 4.83 *** 0.092 2.03 *

MERGER -0.186 -6.19 *** -0.210 -6.54 ***

RESTRUCT -0.179 -8.76 *** -0.183 -5.35 ***

LOSS 0.045 1.20 0.065 1.79

LEV 0.304 5.16 *** 0.327 3.54 **

AUDIT 0.043 2.27 ** 0.049 1.62

EM -0.296 -2.23 ** -0.371 -3.10 **

FCF 2.822 10.82 *** 2.674 3.30 **


Included


Included

Adjusted R2 0.270

0.256

F-Statistics 84.78

111.03

p-value <0.0001

<0.0001

N 7,483 7,483

Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total assets




296

Table 5.28 Effect of Stock Price Informativeness on CAPEX – by Firm Size (H2a)

(Continued)

∑

∑

∑

Small Firms

Small Firms

(Model 40a)

(Model 40b)

White-adjusted

Petersen clustering


Intercept -2.515 -5.57 *** -2.269 -4.01 ***

ψ -0.007 -0.76 0.007 0.51

SIZE -0.295 -2.44 ** -0.333 -1.80

SIZE2 0.034 2.67 *** 0.038 1.78

ANALYST 0.021 5.50 *** 0.022 4.30 ***

AGE -0.041 -1.77 -0.057 -1.76

StdROE 0.000 0.11 0.000 0.58

StdCF -0.003 -2.82 *** -0.003 -1.33

StdRET -5.054 -5.41 *** -7.630 -5.68 ***

MB 0.000 1.58 0.000 .

ROA -0.613 -4.17 *** -0.508 -1.46

WC 0.000 -0.01 0.000 .

DIV 0.116 3.74 *** 0.106 2.15 *

MERGER -0.243 -5.26 *** -0.251 -4.30 ***

RESTRUCT -0.138 -5.10 *** -0.129 -3.01 **

LOSS -0.217 -6.06 *** -0.209 -5.92 ***

LEV 0.339 5.11 *** 0.339 3.23 **

AUDIT 0.015 0.57 0.018 0.50

EM -0.238 -1.92 * -0.249 -3.03 **

FCF 1.199 7.63 *** 1.099 3.41 **


Included


Included

Adjusted R2 0.227 0.221

F-Statistics 67.73 62.17

p-value <0.0001 <0.0001


of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a

p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using


297

Table 5.29 demonstrates the effect of current year‟s stock price informativeness on SGA

costs of the subsequent year for the following types of firms:

a) Large firms – Models 41a and 41b.

b) Small firms – Models 42a and 42b.

c) Small firms with increasing idiosyncratic volatility – Models 43a and 43b.

d) Small firms with decreasing idiosyncratic volatility – Models 44a and 44b.

298

Table 5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size (H2a)

∑

∑

∑

Large Firms

Large Firms

(Model 41a)

(Model 41b)

White-adjusted

Petersen clustering


Intercept 1.489 3.76 *** 1.418 1.82

ψ 0.000 -0.02 -0.020 -0.93

SIZE -0.875 -9.71 *** -0.868 -4.85 ***

SIZE2 0.040 7.60 *** 0.040 3.88 ***

ANALYST 0.020 11.91 *** 0.019 5.16 ***

AGE 0.201 12.61 *** 0.199 5.79 ***

StdROE 0.004 3.79 *** 0.003 4.98 ***

StdCF 0.000 -2.73 *** 0.000 .

StdRET -6.043 -6.50 *** -4.251 -4.99 ***

MB 0.002 2.09 ** 0.002 2.30 *

ROA 0.499 3.43 *** 0.517 1.53

WC 0.000 0.22 0.000 0.10

DIV -0.163 -7.48 *** -0.161 -4.24 ***

MERGER -0.006 -0.22 0.006 0.16

RESTRUCT 0.245 12.49 *** 0.247 5.67 ***

LOSS 0.006 0.16 0.000 0.00

LEV -0.152 -2.68 *** -0.159 -1.41

AUDIT 0.036 1.93 * 0.032 0.98

EM -0.766 -6.05 *** -0.787 -3.13 **

EMP 3.765 7.94 *** 3.708 3.55 **


Included


Included

Adjusted R2 0.450

0.448

F-Statistics 171.82

184.47

p-value <0.0001

<0.0001

N 6,890 6,890


of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at a p-

value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity using the


299

Table 5.29 Effect of stock price informativeness on SGA Costs – by Firm Size (H2a)

(Continued)

∑

∑

∑

Small Firms

Small Firms

(Model 42a)

(Model 42b)

White-adjusted

Petersen clustering


Intercept -0.932 -3.48 *** -0.815 -2.21 *

ψ -0.030 -5.00 *** -0.025 -2.01 *

SIZE -0.279 -4.44 *** -0.286 -3.42 **

SIZE2 -0.009 -1.32 -0.008 -0.91

ANALYST 0.030 11.43 *** 0.030 4.68 ***

AGE 0.018 1.12 0.017 0.67

StdROE 0.001 2.99 *** 0.001 3.00 **

StdCF 0.004 5.56 *** 0.004 5.47 ***

StdRET -0.905 -1.55 -2.106 -3.30 **

MB 0.000 -1.90 0.000 .

ROA -0.388 -8.11 *** -0.407 -6.05 ***

WC -0.001 -1.24 -0.001 -0.90

DIV -0.069 -3.05 *** -0.074 -1.54

MERGER 0.045 1.44 0.028 0.85

RESTRUCT 0.148 8.65 *** 0.145 4.87 ***

LOSS 0.103 5.05 *** 0.102 3.20 **

LEV 0.190 4.52 *** 0.206 2.80 **

AUDIT 0.028 1.46 0.023 0.75

EM 0.031 0.55 0.037 0.69 EMP 0.191 0.69 0.119 0.21


Included


Included

Adjusted R2 0.386 0.381


p-value <0.0001 <0.0001





300

Table 5.29 Effect of Stock Price Informativeness on SGA Costs – by Firm Size (H2a)

(Continued)

∑

∑

∑

Small Firms

Small Firms

Increasing ψ

Increasing ψ

(Model 43a)

(Model 43b)

White-adjusted

Petersen clustering


Intercept -1.297 -1.89 * -1.111 -1.21

ψ -0.023 -2.69 *** -0.015 -0.79

SIZE -0.401 -4.04 *** -0.410 -3.08 **

SIZE2 0.004 0.39 0.006 0.39

ANALYST 0.030 7.64 *** 0.031 3.74 ***

AGE 0.004 0.16 0.004 0.17

StdROE 0.001 3.05 *** 0.001 14.23 ***

StdCF 0.004 4.19 *** 0.004 6.39 ***

StdRET -0.070 -0.09 -1.108 -1.49

MB 0.006 2.46 ** 0.007 1.70

ROA -0.312 -5.05 *** -0.338 -4.49 ***

WC 0.000 -1.27 0.000 -0.91

DIV -0.046 -1.33 -0.050 -0.95

MERGER 0.000 0.00 -0.024 -0.73

RESTRUCT 0.128 5.00 *** 0.125 3.98 ***

LOSS 0.112 3.77 *** 0.114 5.30 ***

LEV 0.113 1.64 0.111 1.40

AUDIT 0.062 2.16 ** 0.053 1.78

EM 0.089 1.11 0.089 0.89 EMP 0.274 0.67 0.165 0.30


Included


Included

Adjusted R2 0.381 0.374


p-value <0.0001 <0.0001

N 2,899 2,899





301

Table 5.29 Effect of Stock Price Informativeness on SGA Costs– by Firm Size (H2a)

(Continued)

∑

∑

∑

Small Firms

Small Firms

Decreasing ψ

Decreasing ψ

(Model 44a)

(Model 44b)

White-adjusted

Petersen clustering


Intercept -0.949 -3.28 *** -0.834 -2.53 **

ψ -0.053 -5.22 *** -0.049 -3.42 **

SIZE -0.174 -2.19 ** -0.179 -1.67

SIZE2 -0.021 -2.40 ** -0.020 -1.81

ANALYST 0.027 7.53 *** 0.027 4.59 ***

AGE 0.031 1.43 0.029 1.04

StdROE 0.001 1.03 0.001 1.39

StdCF 0.003 3.76 *** 0.003 5.39 ***

StdRET -1.934 -2.21 ** -3.296 -3.73 ***

MB 0.000 -1.77 * 0.000 .

ROA -0.436 -6.01 *** -0.455 -4.11 ***

WC -0.003 -0.79 -0.003 -0.69

DIV -0.097 -3.22 *** -0.101 -1.85

MERGER 0.076 1.78 * 0.063 1.21

RESTRUCT 0.167 7.27 *** 0.164 4.30 ***

LOSS 0.099 3.53 *** 0.098 1.98 *

LEV 0.222 3.96 *** 0.244 2.84 **

AUDIT 0.003 0.11 -0.001 -0.04

EM -0.020 -0.24 -0.006 -0.11 EMP 0.178 0.49 0.126 0.22


Included


Included

Adjusted R2 0.391 0.389


p-value <0.0001 <0.0001

N 3,991 3,991 Note: Large (small) firms are defined as firms above (below) the median value of natural logarithm of total

assets of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical significance at

a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a) heteroskedasticity

using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009) procedure.

302

Table 5.29 shows a significant negative association between idiosyncratic volatility and

subsequent year‟s SGA costs in small firms (Models 42a and 42b) but no material

relationship is found between a current year‟s idiosyncratic volatility and the subsequent

year‟s SGA costs level in large firms (Models 41a and 41b). Further analyses on two

sub-samples of small firms based on increasing and decreasing idiosyncratic volatility

reveal asymmetric cost behaviour of firm managers. Only small firms with diminishing

idiosyncratic volatility are found to be responsive to feedback from the capital markets

and make changes to SGA costs (Model 44a and 44b). Small firms with intensifying

idiosyncratic volatility are shown to be making changes in SGA expenditure as shown in

Model 43a when White (1980) procedure is applied but no significant reaction is noted

in Model 43b when Petersen (2009) method is used to control for time series and cross-

sectional correlation.

These findings provide evidence that a larger amount of private information is available

in small firms, as opposed to large firms, thereby encouraging managerial learning. Thus,

their managers are motivated to be more responsive in changing firms‟ SGA costs level

in the subsequent year. The impact of stock price informativeness on SGA costs level of

the subsequent year is more apparent in small firms that are experiencing declining

stock price informativeness as it is imperative to take appropriate action compared to

those firms that are facing a situation of escalating stock price informativeness.

303

5.4.2 Analyst Following

Table 5.30 examines whether the relationship between a current year‟s stock price

informativeness and the subsequent year‟s R&D level is dependent on analyst following.

Eight models are presented in Table 5.30. These models are Models 45a and 45b for

firms with high analyst following; Models 46a and 46b for firms with low analyst

following; Models 47a and 47b for low analyst following firms with increasing

idiosyncratic volatility; as well as Models 48a and 48b for high analyst following firms

with decreasing idiosyncratic volatility.

304


– by Analyst Following (H2b)

∑

∑

∑

High Analyst Following


(Model 45a)

(Model 45b)

White-adjusted

Petersen clustering


Intercept 1.480 4.30 *** 1.616 2.24 *

Ψ 0.028 1.19 0.044 1.23

SIZE -0.667 -7.79 *** -0.673 -3.85 ***

SIZE2 0.034 5.94 *** 0.034 3.02 **

AGE 0.019 0.57 0.019 0.30

StdROE 0.003 2.23 ** 0.003 2.63 **

StdCF 0.000 -0.89 0.000 .

StdRET -3.680 -1.93 * -5.641 -1.56

MB 0.000 0.96 0.000 0.94

ROA -1.082 -8.53 *** -1.084 -8.16 ***

WC 0.000 4.86 *** 0.000 .

DIV -0.610 -13.10 *** -0.616 -6.87 ***

MERGER -0.056 -1.07 -0.060 -0.98

RESTRUCT 0.008 0.22 0.005 0.09

LOSS 0.353 6.67 *** 0.357 5.55 ***

LEV -0.603 -7.15 *** -0.591 -4.06 ***

AUDIT 0.006 0.16 0.005 0.09

EM -0.160 -1.95 * -0.176 -2.11 *

Industries dummies Included Included

Year dummies Included Included

Adjusted R2 0.392 0.390


p-value <0.0001 <0.0001

N 4,025 4,025

Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full

sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate

statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)

heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen (2009)

procedure.

305


– by Analyst Following (H2b) (Continued)

∑

∑

∑

Low Analyst Following


(Model 46a)

(Model 46b)

White-adjusted

Petersen clustering


Intercept -1.044 -1.79 * -0.861 -1.08

ψ -0.086 -7.39 *** -0.072 -4.68 ***

SIZE -0.075 -0.80 -0.070 -0.41

SIZE2 -0.029 -3.31 *** -0.029 -1.85

AGE -0.164 -4.90 *** -0.167 -3.37 ***

StdROE 0.003 4.22 ** 0.003 4.41 ***

StdCF 0.001 3.07 *** 0.001 2.54 **

StdRET -0.293 -0.29 -2.222 -1.61

MB 0.002 1.56 0.002 1.56

ROA -0.616 -6.46 *** -0.627 -3.96 ***

WC 0.000 1.57 0.000 .

DIV -0.490 -10.97 *** -0.497 -5.44 ***

MERGER 0.109 1.61 0.082 1.27

RESTRUCT 0.150 4.14 *** 0.140 2.80 **

LOSS 0.483 10.76 *** 0.486 8.18 ***

LEV -0.334 -4.84 *** -0.332 -3.21 **

AUDIT 0.108 2.73 *** 0.105 1.70

EM 0.058 0.66 0.061 0.64


Industry dummies Included Included

Adjusted R2 0.453 0.450


p-value <0.0001 <0.0001

N 4,488 4,488

Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full sample

on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate

statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)


procedure.

306



∑

∑

∑



Increasing ψ

Increasing ψ

(Model 47a)

(Model 47b)

White-adjusted

Petersen clustering


Intercept -2.590 -5.55 *** -2.272 -4.37 ***

ψ -0.076 -4.94 *** -0.063 -2.93 **

SIZE -0.030 -0.22 -0.022 -0.13

SIZE2 -0.036 -2.74 *** -0.036 -2.65 **

AGE -0.233 -4.58 *** -0.245 -4.56 ***

StdROE 0.003 4.19 *** 0.003 4.85 ***

StdCF 0.002 3.28 *** 0.002 4.81 ***

StdRET 0.791 0.63 -0.605 -0.47

MB 0.003 3.32 *** 0.003 4.03 ***

ROA -0.524 -4.01 *** -0.530 -2.30 *

WC 0.000 1.61 0.000 .

DIV -0.476 -6.68 *** -0.480 -5.03 ***

MERGER 0.230 2.12 ** 0.191 2.39 *

RESTRUCT 0.101 1.83 * 0.090 1.71

LOSS 0.432 6.82 *** 0.434 9.05 ***

LEV -0.352 -3.62 *** -0.357 -2.59 **

AUDIT 0.071 1.14 0.076 1.26

EM 0.260 2.19 ** 0.265 2.70 **



Adjusted R2 0.444 0.440


p-value <0.0001 <0.0001

N 1,945 1,945

Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full sample

on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and * indicate statistical

significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for (a)


procedure.

307



∑

∑

∑



Decreasing ψ

Decreasing ψ

(Model 48a)

(Model 48b)

White-adjusted

Petersen clustering


Intercept -0.403 -0.66 -0.177 -0.23

Ψ -0.162 -8.04 *** -0.147 -6.60 ***

SIZE -0.136 -1.16 -0.134 -0.66

SIZE2 -0.026 -2.37 ** -0.026 -1.43

AGE -0.111 -2.57 ** -0.117 -2.24 *

StdROE 0.010 1.86 * 0.010 2.05 *

StdCF 0.001 2.46 ** 0.001 2.16 *

StdRET -2.450 -1.46 -5.535 -2.61 **

MB 0.001 0.80 0.001 0.91

ROA -0.736 -7.23 *** -0.761 -5.86 ***

WC 0.000 1.42 0.000 1.33

DIV -0.524 -9.19 *** -0.535 -5.77 ***

MERGER 0.016 0.19 -0.002 -0.02

RESTRUCT 0.185 3.92 *** 0.177 2.90 **

LOSS 0.473 8.35 *** 0.486 5.85 ***

LEV -0.289 -3.73 *** -0.281 -3.11 **

AUDIT 0.132 2.55 ** 0.122 1.60

EM -0.091 -0.74 -0.072 -0.58



Adjusted R2 0.470 0.466


p-value <0.0001 <0.0001

N 2,543 2,543 Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full

sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, ** and *

indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted

for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the


308

The results in Table 5.30 show that the coefficient idiosyncratic volatility, ψ is

significantly negative for firms with low analyst following (Models 46a and 46b) but

insignificant positive for firms with high analyst following (Model 45a and 45b). This

suggests that the relationship between stock price informativeness and R&D expenditure

level of the subsequent year is stronger when analyst following is low. Easley et al.

(1998) suggest that analysts introduce more uninformed or noise trading to the stocks.

High analyst following does not seem to generate new private information but in turn

has worsened the private information content in stock prices. Therefore, firms with low

analyst coverage are able to gather higher volume of new firm-specific information from

stock prices and are more responsive in adjusting their R&D expenditure in the

subsequent year, compared to firms with high analyst following.

The sub-sample of low analyst following is further segregated into two groups of firms,

being firms with intensifying idiosyncratic volatility (Models 47a and 47b) and firms

with weakening idiosyncratic volatility (Models 48a and 48b). A stronger relationship

between a current year‟s stock price informativeness and the subsequent year‟s R&D

expenditure level is found in the sub-group of low analyst following firms with

weakening idiosyncratic volatility (Models 48a and 48b), reflected by the greater value

of coefficient idiosyncratic volatility, ψ. This suggests that firms with low analyst

coverage and decreasing stock price informativeness are more responsive to firm-

specific private information by modifying their R&D expenditure.

309

Table 5.31 demonstrates the relationship between a current year‟s stock price

informativeness and the subsequent year‟s CAPEX for firms with high and low analyst

following.

It is observed from the findings presented in Table 5.31 that the association between a

current year‟s idiosyncratic volatility and the subsequent year‟s CAPEX are

insignificant for firms with either high or low analyst following, after controlling for

heteroskedasticity (Models 49a and 50a) as well as for cross-sectional and time series

correlation (Models 49b and 50b). These findings are probably due to non-robust results

revealed in the main model for CAPEX presented in Table 5.18 in item 5.3.1.

310



∑

∑

∑



(Model 49a)

(Model 49b)

White-adjusted

Petersen clustering


Intercept -3.300 -12.56 *** -2.881 -6.39 ***

Ψ -0.004 -0.33 0.040 1.28

SIZE -0.102 -1.69 * -0.160 -1.56

SIZE2 0.005 1.45 0.010 1.51

AGE 0.006 0.30 0.002 0.06

StdROE -0.002 -1.77 * -0.002 -2.90 **

StdCF 0.000 3.22 0.000 .

StdRET 2.507 2.04 ** -3.408 -2.78 *

MB 0.000 0.64 0.000 .

ROA -0.576 -3.00 *** -0.485 -1.03

WC 0.000 5.52 *** 0.000 .

DIV 0.091 3.58 *** 0.071 1.49

MERGER -0.228 -7.10 *** -0.252 -7.26 ***

RESTRUCT -0.162 -7.55 *** -0.163 -5.05 ***

LOSS -0.014 -0.35 0.013 0.27

LEV 0.253 4.20 *** 0.259 2.77 *

AUDIT 0.059 2.92 *** 0.063 1.96 *

EM 0.030 0.21 -0.013 -0.17 FCF 2.535 13.20 *** 2.431 4.09 ***



Adjusted R2 0.299 0.288


p-value <0.0001 <0.0001

N 7,259 7,259






311



∑

∑

∑



(Model 50a)

(Model 50b)

White-adjusted

Petersen clustering


Intercept -3.404 -13.45 *** -3.219 -11.87 ***

ψ -0.014 -1.62 0.002 0.14

SIZE 0.025 0.42 0.019 0.28

SIZE2 -0.002 -0.41 -0.002 -0.29

AGE -0.028 -1.41 -0.038 -1.43

StdROE 0.000 4.43 *** 0.000 .

StdCF 0.000 2.50 ** 0.000 2.93 **

StdRET -4.013 -4.76 *** -7.189 -5.71 ***

MB 0.000 -0.57 0.000 -0.30

ROA -0.501 -3.43 *** -0.387 -1.04

WC 0.000 -3.54 *** 0.000 .

DIV 0.128 4.80 *** 0.113 2.63 **

MERGER -0.196 -4.73 *** -0.213 -4.10 ***

RESTRUCT -0.169 -6.84 *** -0.165 -4.33 ***

LOSS -0.220 -6.57 *** -0.213 -7.73 ***

LEV 0.329 5.08 *** 0.337 3.47 **

AUDIT 0.023 0.91 0.027 0.95

EM -0.432 -3.81 *** -0.449 -5.09 ***

FCF 1.079 6.81 *** 0.957 2.82 **



Adjusted R2 0.203 0.195


p-value <0.0001 <0.0001

N 7,707 7,707



indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are

adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using


312

Next, Table 5.32 presents the effect of current year‟s stock price informativeness on

subsequent year‟s SGA expenditure with the following models:

a) Firms with high analyst following – Models 51a and 51b.

b) Firms with low analyst following – Models 52a and 52b.

c) Firms with low analyst following and increasing idiosyncratic volatility – Models

53a and 53b.

d) Firms with low analyst following and decreasing idiosyncratic volatility – Models

54a and 54b.

313



∑

∑

∑



(Model 51a)

(Model 51b)

White-adjusted

Petersen clustering


Intercept 1.173 4.68 *** 1.171 2.42 *

ψ 0.014 1.10 0.001 0.04

SIZE -0.779 -16.52 *** -0.787 -9.99 ***

SIZE2 0.037 12.23 *** 0.038 7.50 ***

AGE 0.136 7.96 *** 0.137 4.39 ***

StdROE 0.001 1.29 0.001 2.21 *

StdCF 0.000 -1.88 * 0.000 .

StdRET -5.204 -4.80 *** -4.291 -2.65 **

MB 0.000 0.75 0.000 .

ROA 0.193 1.87 * 0.205 1.21

WC 0.005 3.88 *** 0.005 2.85 **

DIV -0.101 -4.30 *** -0.100 -2.32 *

MERGER -0.043 -1.54 -0.033 -0.87

RESTRUCT 0.165 8.74 *** 0.165 4.23 ***

LOSS 0.065 2.10 ** 0.063 1.51

LEV -0.156 -3.10 *** -0.148 -1.83

AUDIT 0.081 4.40 *** 0.077 2.09 *

EM -0.583 -5.93 *** -0.595 -3.36 **

EMP 2.378 5.07 *** 2.319 2.46 **



Adjusted R2 0.491 0.490


p-value <0.0001 <0.0001

N 6,708 6,708






314



∑

∑

∑



(Model 52a)

(Model 52b)

White-adjusted

Petersen clustering


Intercept -0.124 -0.64 0.019 0.06

ψ -0.039 -6.24 *** -0.033 -2.81 **

SIZE -0.453 -10.61 *** -0.455 -5.47 ***

SIZE2 0.011 2.76 *** 0.011 1.38

AGE 0.104 6.49 *** 0.101 3.23 **

StdROE 0.001 3.93 *** 0.001 5.48 ***

StdCF 0.000 1.20 0.000 0.78

StdRET -1.754 -3.12 *** -2.898 -4.08 ***

MB 0.003 2.79 *** 0.003 2.23 *

ROA -0.450 -8.65 *** -0.468 -6.43 ***

WC 0.000 -3.39 *** 0.000 -3.41 **

DIV -0.143 -6.62 *** -0.146 -3.56 **

MERGER 0.072 2.19 ** 0.058 1.62

RESTRUCT 0.242 12.89 *** 0.239 6.69 ***

LOSS 0.053 2.42 ** 0.051 1.61

LEV -0.010 -0.22 0.002 0.03

AUDIT -0.019 -0.89 -0.019 -0.56

EM -0.032 -0.55 -0.033 -0.47 EMP 1.633 4.28 *** 1.608 2.88 **



Adjusted R2 0.445 0.442


p-value <0.0001 <0.0001

N 7,072 7,072



indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are

adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using


315



∑

∑

∑



Increasing ψ

Increasing ψ

(Model 53a)

(Model 53b)

White-adjusted

Petersen clustering


Intercept 0.197 0.54 0.334 1.27

ψ -0.035 -4.00 *** -0.023 -1.28

SIZE -0.532 -7.91 *** -0.529 -6.51 ***

SIZE2 0.018 2.91 *** 0.018 2.38 *

AGE 0.073 2.81 *** 0.069 2.15 *

StdROE 0.001 3.41 *** 0.001 6.27 ***

StdCF 0.000 -0.45 0.000 -0.56

StdRET -0.866 -1.16 -2.315 -3.09 **

MB 0.003 3.15 *** 0.003 2.67 **

ROA -0.393 -5.88 *** -0.416 -4.07 ***

WC 0.000 0.48 0.000 0.47

DIV -0.128 -3.66 *** -0.131 -2.74 **

MERGER 0.087 1.76 0.054 1.26

RESTRUCT 0.221 7.86 *** 0.223 5.02 ***

LOSS 0.059 1.82 * 0.059 2.26 *

LEV 0.028 0.39 0.027 0.32

AUDIT 0.024 0.74 0.027 0.81

EM 0.057 0.72 0.050 0.45 EMP 1.539 3.38 *** 1.568 2.48 **



Adjusted R2 0.437 0.431


p-value <0.0001 <0.0001

N 2,936 2,936

Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the

full sample on the number of analysts that follow the firms. All variables are defined in Table 4.1. ***, **

and * indicate statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics

are adjusted for (a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and

year using the Petersen (2009) procedure.

316



∑

∑

∑



Decreasing ψ

Decreasing ψ

(Model 54a)

(Model 54b)

White-adjusted

Petersen clustering


Intercept -0.128 -0.54 -0.007 -0.02

ψ -0.064 -6.01 *** -0.061 -4.37 ***

SIZE -0.432 -7.56 *** -0.432 -4.88 ***

SIZE2 0.008 1.47 0.008 0.88

AGE 0.124 6.09 *** 0.121 3.61 **

StdROE 0.005 1.10 0.004 1.04

StdCF 0.000 1.63 0.000 1.18

StdRET -2.751 -3.10 *** -3.656 -2.85 **

MB 0.004 1.48 0.004 1.45

ROA -0.460 -5.82 *** -0.477 -4.90 ***

WC 0.000 -2.43 * -0.001 -3.31 **

DIV -0.160 -5.84 *** -0.162 -4.31 ***

MERGER 0.060 1.36 0.057 1.44

RESTRUCT 0.256 10.18 *** 0.252 5.69 ***

LOSS 0.046 1.53 0.045 1.01

LEV -0.014 -0.23 0.001 0.01

AUDIT -0.051 -1.86 * -0.054 -1.45

EM -0.118 -1.43 -0.110 -1.88 EMP 1.766 2.84 *** 1.694 2.14 **



Adjusted R2 0.450 0.449


p-value <0.0001 <0.0001

N 4,136 4,136 Note: Firms with high (low) analyst following are defined as firms above (below) the median value of the full





317

The findings presented in Table 5.32 highlight that the association between a current

year‟s stock price informativeness and SGA expenditure of the subsequent year is

significantly negative for firms with low analyst following (Models 52a and 52b) but

insignificantly positive for firms with high analyst following (Model 51a and 51b). The


year‟s SGA is stronger when analyst following is low. This is consistent with Chen et al.

(2007) who conclude firms with low analyst following have a higher sensitivity of

investment-to-price. As such, managerial learning from stock prices is higher for firms

with low analyst following and their firm managers are more enthusiastic in modifying

their SGA expenditure level of the subsequent year in response to stock prices, as

opposed to firms with high analyst following.

Further analyses were performed on the sub-sample of low analyst following firms in

terms of the direction of movement in firm-level idiosyncratic volatility. The coefficient

idiosyncratic volatility, ψ is larger and at higher significance level for firms with low

analyst following and weakening idiosyncratic volatility (Models 54a and 54b),

compared to low analyst following firms experiencing a strengthening idiosyncratic

volatility (Models 53a and 53b). These findings suggest that firms with low analyst

following and decreasing idiosyncratic volatility learn more and react faster to new firm-

specific information by changing their SGA expenditure level in the subsequent year to

enjoy long-term benefits from this corporate expenditure.

318

5.4.3 Bid-ask Spreads

This item demonstrates how an association between current year‟s stock price

informativeness and subsequent year‟s corporate expenditure is dependent on bid-ask

spreads. The findings displayed in Table 5.33 highlight the effect of current year‟s stock

price informativeness on R&D level for the following types of firms:

a) Firms with small bid-ask spreads (Models 55a and 55b).

b) Firms with large bid-ask spreads (Models 56a and 56b).

c) Firms with large bid-ask spreads and increasing idiosyncratic volatility (Models

57a and 57b).

d) Firms with large bid-ask spreads and decreasing idiosyncratic volatility (Models

58a and 58b).

319


– by Bid-Ask Spreads (H2c)

∑

∑

∑

Small Bid-Ask Spreads


(Model 55a)

(Model 55b)

White-adjusted

Petersen clustering


Intercept 2.433 6.67 *** 2.434 3.49 *

Ψ 0.019 0.79 0.001 0.02

SIZE -0.924 -10.41 *** -0.923 -4.84 ***

SIZE2 0.039 6.65 *** 0.039 3.20 **

ANALYST 0.069 22.87 *** 0.068 11.78 ***

AGE 0.024 0.82 0.025 0.42

StdROE 0.002 3.18 *** 0.002 3.43 **

StdCF 0.000 -1.77 * 0.000 .

StdRET -10.262 -5.09 *** -8.533 -3.19 **

MB 0.001 1.32 0.001 1.23

ROA -1.034 -7.32 *** -1.020 -4.71 ***

WC 0.000 8.23 *** 0.000 .

DIV -0.469 -11.33 *** -0.471 -6.77 ***

MERGER 0.016 0.32 0.019 0.37

RESTRUCT 0.143 4.22 *** 0.143 2.57 **

LOSS 0.381 7.45 *** 0.373 8.42 ***

LEV -0.492 -6.16 *** -0.493 -3.13 **

AUDIT 0.010 0.32 0.008 0.17

EM -0.066 -0.45 -0.106 -0.86


Included


Included

Adjusted R2 0.430 0.427


p-value <0.0001 <0.0001

N 4,255 4,255 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average

bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate statistical


heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the Petersen

(2009) procedure.

320


– by Bid-Ask Spreads (H2c) (Continued)

∑

∑

∑

Large Bid-Ask Spreads


(Model 56a)

(Model 56b)

White-adjusted

Petersen clustering


Intercept -1.134 -1.81 * -0.992 -1.35

ψ -0.070 -5.97 *** -0.053 -3.28 **

SIZE -0.193 -2.45 ** -0.188 -1.74

SIZE2 -0.020 -2.87 *** -0.021 -2.22 *

ANALYST 0.074 11.94 *** 0.075 12.42 ***

AGE -0.125 -3.65 *** -0.126 -2.35 *

StdROE 0.004 4.14 *** 0.004 3.84 ***

StdCF 0.000 1.72 * 0.000 4.48 ***

StdRET 0.591 0.60 -1.427 -1.02

MB 0.000 0.79 0.000 0.76

ROA -0.540 -5.85 *** -0.542 -3.72 ***

WC 0.000 1.10 0.000 .

DIV -0.467 -9.47 *** -0.478 -4.48 ***

MERGER 0.105 1.49 0.087 1.32

RESTRUCT 0.146 3.92 *** 0.142 3.13 **

LOSS 0.515 11.28 *** 0.532 7.76 ***

LEV -0.230 -3.52 *** -0.222 -2.49 **

AUDIT 0.115 2.86 *** 0.113 1.79

EM 0.045 0.66 0.052 0.81


Included


Included

Adjusted R2 0.438

0.434

F-Statistics 104.66

108.77

p-value <0.0001

<0.0001

N 4,255 4,255

Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the average




(2009) procedure.

321



∑

∑

∑



Increasing ψ

Increasing ψ

(Model 57a)

(Model 57b)

White-adjusted

Petersen clustering


Intercept -2.376 -5.57 *** -2.053 -4.18 ***

ψ -0.068 -4.37 *** -0.055 -2.74 **

SIZE -0.220 -1.80 * -0.206 -1.36

SIZE2 -0.021 -1.94 * -0.022 -1.73

ANALYST 0.086 9.53 *** 0.088 9.43 ***

AGE -0.163 -3.13 *** -0.170 -3.50 **

StdROE 0.003 4.23 *** 0.003 3.22 **

StdCF 0.000 1.39 0.000 2.00 *

StdRET 1.495 1.18 0.310 0.28

MB 0.002 2.85 *** 0.002 1.90

ROA -0.440 -3.52 *** -0.443 -2.08 *

WC 0.000 0.66 0.000 .

DIV -0.492 -6.25 *** -0.503 -5.10 ***

MERGER 0.086 0.80 0.060 0.65

RESTRUCT 0.147 2.59 *** 0.138 2.20 *

LOSS 0.487 7.62 *** 0.507 10.21 ***

LEV -0.262 -2.87 *** -0.257 -1.97 *

AUDIT 0.097 1.56 0.093 1.25

EM 0.260 2.28 ** 0.270 3.55 **


Included


Included

Adjusted R2 0.439 0.435


p-value <0.0001 <0.0001

N 1,847 1,847





(2009) procedure.

322



∑

∑

∑



Decreasing ψ

Decreasing ψ

(Model 58a)

(Model 58b)

White-adjusted

Petersen clustering


Intercept -0.693 -1.04 -0.508 -1.03

ψ -0.122 -5.71 *** -0.099 -4.53 ***

SIZE -0.188 -1.84 * -0.184 -1.32

SIZE2 -0.020 -2.13 ** -0.021 -1.70

ANALYST 0.062 7.42 *** 0.062 10.05 ***

AGE -0.092 -2.03 ** -0.094 -1.68

StdROE 0.008 2.03 ** 0.009 2.67 **

StdCF 0.000 1.18 0.000 1.76

StdRET -1.132 -0.68 -4.612 -1.60

MB 0.000 0.69 0.000 0.67

ROA -0.694 -7.23 *** -0.707 -6.66 ***

WC 0.001 1.68 * 0.001 1.57

DIV -0.472 -7.52 *** -0.481 -4.16 ***

MERGER 0.123 1.29 0.110 1.85

RESTRUCT 0.124 2.54 ** 0.124 1.80

LOSS 0.494 8.59 *** 0.518 5.20 ***

LEV -0.171 -2.50 ** -0.164 -2.33 *

AUDIT 0.124 2.32 ** 0.119 1.73

EM -0.076 -0.94 -0.059 -0.86


Included


Included

Adjusted R2 0.444 0.440


p-value <0.0001 <0.0001





(2009) procedure.

323

The findings presented in Table 5.33 highlight a significantly negative relationship

between a current year‟s idiosyncratic volatility and the subsequent year‟s R&D

expenditure level for firms with high bid-ask spreads (Models 56a and 56b). However,

an insignificant association between these variables is observed for firms with low bid-

ask spreads (Models 55a and 55b). Managers of firms with high bid-ask spreads seem to

learn more from the capital markets about firm‟s new private information that managers

have yet to possess, hence they are more induced to initiate changes in R&D

expenditure.

Further analyses on firms with high bid-ask spreads disclose a stronger association

between a current year‟s stock price informativeness and the subsequent year‟s R&D

expenditure in firms with diminishing idiosyncratic volatility (Models 58a and 58b) as

compared to firms with improving informativeness in their stock prices (Models 57a and

57b). Firms with high bid-ask spreads and diminishing idiosyncratic volatility respond

swiftly in modifying their spending in R&D projects in the following year.

Table 5.34 exhibits the association between a current year‟s stock price informativeness

and CAPEX in the subsequent year of firms with small (Models 59a and 59b) and large

bid-ask spreads (Models 60a and 60b).

324

Table 5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-Ask Spreads

(H2c)

∑

∑

∑



(Model 59a)

(Model 59b)

White-adjusted

Petersen clustering


Intercept -3.900 -14.43 *** -3.454 -8.87 ***

ψ -0.001 -0.10 0.059 1.72

SIZE -0.022 -0.36 -0.058 -0.63

SIZE2 0.000 0.00 0.001 0.22

ANALYST 0.006 3.25 *** 0.006 1.77

AGE 0.015 0.88 0.010 0.31

StdROE -0.003 -8.08 *** -0.003 -5.16 ***

StdCF 0.000 3.44 *** 0.000 .

StdRET 3.649 2.78 *** -4.185 -2.74 **

MB 0.001 1.90 * 0.001 2.02 *

ROA -0.456 -2.03 ** -0.354 -0.87

WC 0.000 6.32 *** 0.000 .

DIV 0.135 5.69 *** 0.123 3.20 **

MERGER -0.204 -6.82 *** -0.230 -8.87 ***

RESTRUCT -0.165 -7.98 *** -0.172 -6.69 ***

LOSS 0.037 0.91 0.059 1.04

LEV 0.253 4.23 *** 0.291 3.27 **

AUDIT 0.067 3.44 *** 0.073 2.48 **

EM -0.399 -2.73 *** -0.456 -2.88 **

FCF 2.829 12.69 *** 2.682 6.85 ***


Included


Included

Adjusted R2 0.285 0.271


p-value <0.0001 <0.0001





(2009) procedure.

325

Table 5.34 Effect of Stock Price Informativeness on CAPEX – by Bid-Ask Spreads

(H2c) (Continued)

∑

∑

∑



(Model 60a)

(Model 60b)

White-adjusted

Petersen clustering


Intercept -3.297 -10.27 *** -3.163 -8.94 ***

ψ -0.010 -1.11 0.003 0.30

SIZE 0.089 1.67 * 0.088 1.37

SIZE2 -0.009 -2.20 ** -0.010 -2.14 *

ANALYST 0.021 5.88 *** 0.021 6.79 ***

AGE -0.022 -1.05 -0.028 -0.93

StdROE 0.000 4.74 *** 0.000 .

StdCF 0.000 -0.32 0.000 .

StdRET -3.747 -4.58 *** -6.207 -4.93 ***

MB 0.000 -0.46 0.000 .

ROA -0.521 -3.69 *** -0.426 -1.09

WC 0.000 -3.50 *** 0.000 .

DIV 0.092 3.21 *** 0.075 1.54

MERGER -0.211 -4.56 *** -0.221 -4.29 ***

RESTRUCT -0.148 -5.70 *** -0.137 -4.40 ***

LOSS -0.227 -6.80 *** -0.219 -8.85 ***

LEV 0.395 5.96 *** 0.396 4.06 ***

AUDIT 0.004 0.15 0.007 0.23

EM -0.205 -1.64 -0.216 -3.14 **

FCF 1.069 6.98 *** 0.968 2.49 **


Included


Included

Adjusted R2 0.220

0.214

F-Statistics 64.75

67.62

p-value <0.0001

<0.0001

N 7,481 7,481





(2009) procedure.

326

The findings reported in Table 5.34 highlight that the association between a current

year‟s stock price informativeness and the subsequent year‟s CAPEX are insignificant

for both firms with small (Models 59a and 59b) and large bid-ask spreads (Models 60a

and 60b) by applying White (1980) and Petersen (2009) procedures respectively . This is

consistent with the non-robust regression results reported in firms with varying firm

sizes (Table 5.28) and analyst followings (Table 5.31).

Table 5.35 presents the relationship between a current year‟s stock price informativeness

and SGA expenditure in the subsequent year in firms with small and large bid-ask

spreads. The following models are reported in this table:

a) Firms with small bid-ask spreads (Models 61a and 61b).

b) Firms with large bid-ask spreads (Models 62a and 62b).

c) Firms with large bid-ask spreads and increasing idiosyncratic volatility (Models

63a and 63b).

d) Firms with large bid-ask spreads and decreasing idiosyncratic volatility (Models

64a and 64b).

327


– by Bid-Ask Spreads (H2c)

∑ ∑ ∑



(Model 61a)

(Model 61b)

White-adjusted

Petersen clustering


Intercept 1.324 5.57 *** 1.185 2.70 **

ψ 0.061 4.46 *** 0.028 1.32

SIZE -0.830 -16.59 *** -0.823 -9.13 ***

SIZE2 0.037 11.68 *** 0.037 6.39 ***

ANALYST 0.020 12.77 *** 0.019 5.73 ***

AGE 0.150 8.98 *** 0.152 5.14 ***

StdROE 0.001 1.35 0.001 1.47

StdCF 0.000 -2.96 *** 0.000 .

StdRET -8.448 -7.25 *** -4.681 -4.35 ***

MB 0.000 0.49 0.000 0.47

ROA 0.356 2.68 *** 0.371 1.98 *

WC 0.000 -0.56 0.000 -0.56

DIV -0.104 -4.65 *** -0.104 -2.81 **

MERGER -0.007 -0.24 0.008 0.24

RESTRUCT 0.194 10.45 *** 0.200 5.38 ***

LOSS 0.077 2.33 ** 0.067 1.28

LEV 0.082 1.57 0.076 0.81

AUDIT 0.053 2.91 *** 0.047 1.56

EM -0.477 -4.82 *** -0.488 -3.93 ***

EMP 2.515 8.22 ***

2.483 3.77 ***


Included


Included

Adjusted R2 0.450 0.446


p-value <0.0001 <0.0001





(2009) procedure.

328



∑ ∑ ∑



(Model 62a)

(Model 62b)

White-adjusted

Petersen clustering


Intercept -0.429 -1.57 -0.293 -0.82

ψ -0.039 -6.43 *** -0.032 -2.81 **

SIZE -0.443 -12.30 *** -0.449 -9.18 ***

SIZE2 0.006 1.99 ** 0.007 1.53

ANALYST 0.026 8.91 *** 0.027 3.85 ***

AGE 0.111 6.90 *** 0.107 3.81 ***

StdROE 0.001 2.93 *** 0.001 3.21 **

StdCF 0.000 3.86 *** 0.000 .

StdRET -0.816 -1.46 -2.038 -3.09 **

MB 0.000 -0.56 0.000 .

ROA -0.450 -8.99 *** -0.461 -5.61 ***

WC -0.001 -0.95 -0.001 -0.78

DIV -0.126 -5.56 *** -0.130 -2.83 **

MERGER 0.048 1.41 0.031 1.05

RESTRUCT 0.222 11.73 *** 0.221 6.60 ***

LOSS 0.075 3.47 *** 0.076 1.71

LEV 0.038 0.83 0.046 0.62

AUDIT 0.013 0.64 0.014 0.50

EM -0.026 -0.45 -0.023 -0.48 EMP 1.255 3.69 ***

1.233 2.59 **


Included


Included

Adjusted R2 0.505

0.503

F-Statistics 213.98

218.37

p-value <0.0001

<0.0001

N 6,889 6,889





(2009) procedure.

329



∑ ∑ ∑



Increasing ψ

Increasing ψ

(Model 63a)

(Model 63b)

White-adjusted

Petersen clustering


Intercept -0.806 -1.38 -0.654 -1.04

ψ -0.031 -3.63 *** -0.024 -1.50

SIZE -0.430 -7.62 *** -0.432 -6.54 ***

SIZE2 0.005 0.91 0.005 0.72

ANALYST 0.024 5.86 *** 0.025 2.96 **

AGE 0.086 3.40 *** 0.082 2.63 **

StdROE 0.001 2.72 *** 0.001 3.66 **

StdCF 0.000 3.45 *** 0.000 .

StdRET 0.027 0.04 -1.056 -1.48

MB 0.006 3.18 *** 0.006 2.11 *

ROA -0.441 -6.82 *** -0.452 -5.12 ***

WC 0.000 0.49 0.001 0.55

DIV -0.106 -2.97 *** -0.111 -2.11 *

MERGER -0.010 -0.20 -0.033 -0.75

RESTRUCT 0.215 7.65 *** 0.217 6.84 ***

LOSS 0.069 2.18 ** 0.073 1.72

LEV 0.045 0.63 0.046 0.65

AUDIT 0.081 2.59 ** 0.080 2.60 **

EM 0.101 1.27 0.105 1.76 EMP 1.222 2.88 *** 1.181 1.45


Included


Included

Adjusted R2 0.529 0.527


p-value <0.0001 <0.0001

N 2,883 2,883





(2009) procedure.

330



∑ ∑ ∑



Decreasing ψ

Decreasing ψ

(Model 64a)

(Model 64b)

White-adjusted

Petersen clustering


Intercept -0.207 -0.67 -0.075 -0.25

ψ -0.063 -6.06 *** -0.054 -3.86 ***

SIZE -0.452 -9.36 *** -0.456 -7.35 ***

SIZE2 0.007 1.65 0.008 1.32

ANALYST 0.026 6.37 *** 0.027 3.99 ***

AGE 0.127 6.19 *** 0.123 4.17 ***

StdROE 0.003 0.88 0.003 0.77

StdCF 0.000 2.46 ** 0.000 2.46 **

StdRET -1.905 -2.17 ** -3.279 -3.44 **

MB 0.000 -1.92 * 0.000 .

ROA -0.462 -5.98 *** -0.476 -4.23 ***

WC -0.014 -2.90 *** -0.014 -2.10 *

DIV -0.149 -5.13 *** -0.152 -3.24 **

MERGER 0.091 2.00 ** 0.079 2.44 *

RESTRUCT 0.228 8.96 *** 0.226 5.77 ***

LOSS 0.080 2.71 *** 0.081 1.53

LEV -0.016 -0.25 -0.006 -0.06

AUDIT -0.036 -1.30 -0.036 -0.95

EM -0.114 -1.42 -0.107 -2.21 *

EMP 1.280 2.63 *** 1.256 2.55 **


Included


Included

Adjusted R2 0.491 0.490


p-value <0.0001 <0.0001

N 4,006 4,006 Note: Firms with high (low) bid ask spreads are defined as firms above (below) the median value of the

average bid-ask spreads of the full sample. All variables are defined in Table 4.1. ***, ** and * indicate

statistical significance at a p-value less than 1%, 5% and 10% levels respectively. T-statistics are adjusted for

(a) heteroskedasticity using the procedure of White (1980) and (b) clustering by firm and year using the


331

The findings reported in Table 5.35 highlight a significant negative relationship between

a current year‟s stock price informativeness and subsequent year‟s SGA costs level for

firms with large bid ask spreads (Models 62a and 62b). The coefficient idiosyncratic

volatility, ψ is significantly positive for firms with small bid-ask spreads in Model 61a

(White-adjusted) but become insignificantly positive in Model 61b after controlling for

time series and cross-sectional correlations (referred to as Petersen clustering procedure).

This shows the relationship between a current year‟s stock price informativeness and the

subsequent year‟s SGA expenditure level is generally stronger for firms possessing large

bid-ask spreads. This finding also suggests managers of these firms are guided by new

firm-specific private information they have learned from the capital markets in making

decisions in SGA expenditure. Moreover, a stronger association between a current

year‟s idiosyncratic volatility and the subsequent year‟s SGA expenditure is observed in

firms possessing high bid-ask spreads when they experience declining informativeness

in their stock prices (Models 64a and 64b). It appears that these firms react more

“aggressively” in response to firm-specific private information obtained from stock

prices by changing their spending in SGA of the subsequent year.

5.4.4 Summary of Findings – Hypotheses 2a to 2c

Hypotheses 2a to 2c of this study examine whether the association between a current

year‟s stock price informativeness and the subsequent year‟s corporate expenditure is

332

more likely to be stronger for firms with small firm size, low analyst following and with

large bid-ask spreads respectively.

The above results show a significant inverse relationship between a current year‟s stock

price informativeness and the subsequent year‟s R&D expenditure is stronger in small

firms and in firms with low analyst following as well as in firms possessing large bid-

ask spreads. It is also demonstrated in these firms that the effect of stock price

informativeness on R&D expenditure is greater when the stock price informativeness is

weakening compared to firms with strengthening informativeness of their stock prices.

The results are similar when the corporate expenditure is proxied by SGA expenditure.

As such, it is concluded that Hypotheses 2a to 2c are well supported when corporate

expenditure is represented by R&D and SGA.

The relationship between a current year‟s stock price informativeness and the

subsequent year‟s CAPEX is, however, insignificant when firms are analysed by their

firm size, analyst following and bid-ask spreads. Thus, Hypotheses 2a to 2c are not

supported when corporate expenditure is represented by CAPEX.

5.5 Additional Tests

The following additional tests are conducted to verify the robustness of the model

applied in this study.

333

5.5.1 Different Measures of Idiosyncratic Volatility

The current measure of idiosyncratic volatility is generated using the value-weighted

market return. Alternative measures of idiosyncratic volatility outlined in item 4.5.2 are

used to examine the robustness of the current measure used:

a) Use different market index

The equally-weighted market return is used to replace value-weighted market return to

generate different measures of idiosyncratic volatility (Gul et al., 2011a).

b) Use Fama & French three-factor model (Fama & French, 1993, 1995, 1996)

Instead of the one-factor model used in the current study, the Fama & French three-

factor model (Fama & French, 1993, 1995, 1996) is used to estimate idiosyncratic

volatility by obtaining the value of (1-R2) from the regression indicated in equation 5.5.

(5.5)

where:

is the daily excess return of stock i in day t, is the daily value-weighted excess

market return, is the small-minus-big size factor return, and is the high-

minus-low book-to-market factor return.

334

c) Use Brockman and Yan‟s (2009) model

In Brockman and Yan (2009) model, the idiosyncratic volatility is estimated by

regressing firms‟ daily return on contemporaneous and lagged daily market return, as

well as contemporaneous and lagged daily industry return for each firm-year

observation, as indicated in Equation 5.6.

(5.6)

where:

is the daily excess return of stock i in day t, is the contemporaneous daily value-

weighted market return, is the lagged daily value-weighted market return,

is the contemporaneous daily industry return and is the lagged daily industry

return. The industry return for a specific day is created using all firms with the same

two-digit SIC codes.

The results (untabulated) using these measures of idiosyncratic volatility are essentially

the same as those presented in Tables 5.17 to 5.35, indicating the empirical evidence

presented is robust and not methodology-specific.

5.5.2 Controlling for the Effect of Global Financial Crisis

The data of year 2008 is excluded from the sample to examine whether the results

presented are driven by the effect of global financial crisis. The results (untabulated) of

this smaller sample remain robust and significant as those presented in Tables 5.17 to

335

5.35. This indicates the empirical evidence presented is not affected by the impact of

global financial crisis in year 2008.

5.5.3 Controlling for Time-Series and Cross-Sectional Correlations

All tests are conducted using the Fama and MacBeth (1973) procedure to control for

possible correlation in the cross-sectional error structure, as well as the Newey-West

corrected Fama-Macbeth (FM-NW) procedure to mitigate time series autocorrelation.

The results (untabulated) are qualitatively similar to those reported in Tables 5.17 and

5.35.

5.6 Chapter Summary

This chapter presents the research findings and deliberates the results that were obtained.

The univariate and multivariate results reported provide insights with regards to the

association between a current year‟s stock price informativeness and the subsequent

year‟s corporate expenditure proxied by R&D expenditure, CAPEX and SGA costs.

This chapter also exhibits how this association between a current year‟s stock price


information asymmetry, proxied by firm size, analyst following and bid-ask spreads.

336

The subsequent chapter (Chapter 6) presents the conclusions of this study by

summarising the major findings. It also outlines the limitations of this study and

provides the future research directions.

337

CHAPTER 6

CONCLUSION

6.1 Introduction

This study examines the relationship between stock price informativeness and corporate

expenditure and evaluates whether this relationship is dependent on information

asymmetry. The thesis consists of six chapters. Chapter 1 provides the background

information representing the foundation of this research while Chapter 2 evaluates the

extant literature on stock price informativeness and corporate expenditure. Chapter 3

provides an overview of empirical literature on the theories underpinning the current

study and also outlines the theoretical framework of the current study. Four hypotheses

are developed to predict the association between stock price informativeness and

corporate expenditure as well as to determine whether the relationship is dependent on

information asymmetry. Chapter 4 presents the research methodology adopted and

further outlines the procedures employed to examine the hypotheses developed in this

study. Chapter 5 exhibits the research findings followed by a discussion of the results of

the current study.

After providing an outline of each of the chapters (Chapters 1 to 5) in Section 6.1,

Section 6.2 presents a summary of major findings by reference to the research objectives

338

and hypotheses of this study, together with the research implication. The contributions

of this study are highlighted in Section 6.3, followed by an outline of the limitations of

the study in Section 6.4 and recommendations for future research directions in Section

6.5. Section 6.6 presents the conclusion of this thesis.

6.2 Summary of Key Research Findings

In this section, the key research findings are elaborated in relation to the two research

objectives and four hypotheses developed for this study.35

Table 6.1 provides a summary

of research objectives, corresponding hypotheses and research findings. These research

findings are based on the research model outlined in item 3.4. The implications from the

research findings are also presented accordingly.

35 Refer to item 1.3 for the two research objectives of this study as well as items 3.2 and 3.3 for the four

corresponding hypotheses.

339

Table 6.1 Summary of Research Objectives, Corresponding Hypotheses and Research Findings

Research Objectives Hypotheses Findings

R&D

Expenditure

Capital

Expenditure

SGA

Costs

1. To investigate how firm-level

corporate expenditure responds

to stock price informativeness.

H1: The stock price informativeness of

a current year is negatively

associated with corporate

expenditure in the subsequent year,

ceteris paribus.

Supported Not

supported

Supported

2. To assess whether information

asymmetry plays a role in the

relationship between stock price

informativeness and corporate

expenditure.

H2a: The negative relationship between a

current year‟s stock price

informativeness and the subsequent

year‟s corporate expenditure is

likely to be stronger for small

firms, ceteris paribus.

Supported Not

supported

Supported

H2b: The negative relationship between a




likely to be stronger for firms with

low analyst following, ceteris

paribus.

Supported Not

supported

Supported

H2c: The negative relationship between a




more likely to be stronger for firms

with high bid-ask spreads, ceteris

paribus.

Supported Not

supported

Supported

340

6.2.1 Stock Price Informativeness and Corporate Expenditure

The first research objective of this study concerns the association between stock price

informativeness and corporate expenditure. Specifically, this study‟s first research

objective is stated as follows:

“To investigate how firm-level corporate expenditure responds to stock price

informativeness.”

Learning theory suggests managers apply new private information gathered from their

own stock prices to make their decisions in respect of corporate resource allocation (Luo,

2005; Chen et al., 2007; Frésard, 2012). It is posited in this study that a low level of

stock price informativeness is more likely to motivate firm managers to increase their

corporate expenditure in three specific areas, namely, R&D expenditure, capital

expenditure and SGA costs. In view of the significance of these corporate expenditures

to firm performance as suggested by prior studies, firm managers intend to convey

positive signals to the capital markets about firms‟ potential cash flows and earnings,

thereby increasing investors‟ confidence in respect of firms‟ future performance. On the

other hand, it is argued that firm managers are more likely to maintain a relatively low

level of corporate expenditure when the stock price informativeness is at a high level.

This is because investors are already well informed of firms‟ future cash flows and

prospects through firms‟ current prices when their stock prices are more informative.

Moreover, high stock price informativeness indicates high efficiency of resource

341

allocation in these firms (Durnev et al., 2003). Hence, Hypothesis 1 postulates that stock

price informativeness of the current year is negatively associated with corporate

expenditure in the subsequent year, ceteris paribus.

This study finds a significant negative link between a current year‟s stock price

informativeness and the subsequent year‟s R&D expenditure and SGA costs after

considering endogeneity, providing support to Hypothesis 1 of the study. The impact of

stock price informativeness on the level of subsequent year‟s R&D expenditure and

SGA costs is greater (lower) when firms‟ idiosyncratic volatility is weakening

(strengthening) from the previous year. However, it is observed that there is no

relationship between a current year‟s stock price informativeness and CAPEX in the

subsequent year, indicating that Hypothesis 1 is not supported when corporate

expenditure is proxied by CAPEX.

To overcome endogeneity concern, a change model is used to analyse the relationship

between changes in a current year‟s stock price informativeness and changes in the

subsequent year‟s corporate expenditure. The results show that as firm-level stock price

informativeness strengthens from the previous year, a change in the current year‟s

idiosyncratic volatility is positively associated to the changes in R&D expenditure and

SGA costs in the subsequent year. This suggests that when stock price informativeness

improves, firm managers obtain positive feedback provided by the stock markets and

342

thereby increasing R&D expenditure and SGA costs in the subsequent year to better

enjoy the benefit expected from these corporate expenditures.

When stock price informativeness is weakening, there is no immediate managerial

reaction in modifying R&D expenditure and SGA costs. This asymmetric cost response

is partly due to the cost “stickiness” behaviour of firm managers. These managers may

need to evaluate whether the declining stock price informativeness is temporary or long-

term in nature, and they are thus reluctant to increase corporate expenditure when

idiosyncratic volatility worsens. It is observed that firm managers merely react when it

is crucial for them to increase R&D expenditure and SGA costs, that is, when there is a

drop in relative idiosyncratic volatility (1-R2) by 20 per cent. However, the change

model reveals an insignificant relationship between changes in idiosyncratic volatility

and changes in CAPEX in the subsequent year when stock price informativeness is

either strengthening or weakening. The finding of asymmetric cost response in R&D

and SGA expenditures is consistent with the cost “stickiness” behaviour demonstrated in

past studies such as Anderson et al. (2003), Balakrishnan, Petersen and Soderstrom

(2004), Balakrishnan and Gruca (2008) and Dalla Via and Perego (Forthcoming).

These findings on R&D and SGA are consistent with the learning theory that firm

managers learn important and new firm-specific information from their own stock prices

and they integrate this information to make appropriate corporate expenditure decisions.

These findings are robust and significant when examining using varying measures of

343

idiosyncratic volatility as well as after controlling for time series and cross-sectional

error structure correlations. The insignificant relationship between idiosyncratic

volatility and CAPEX shows that firm managers do not rely on feedback derived from

the capital markets to determine their capital investments. In this study, it is conceived

that firm managers are more likely to thoroughly plan their long-term needs by

considering other factors such as internal cash flow, return on investment and board

approval before investing in capital projects.

Thus, Hypothesis 1 is well supported when corporate expenditure is represented by

R&D expenditure and SGA costs, but not by capital expenditure.

6.2.2 The Role of Information Asymmetry

The second research objective of this study is to determine whether the relationship

between a current year‟s stock price informativeness and corporate expenditure of the

subsequent year is dependent on information asymmetry. Specifically, this study‟s

research objective is stated as follows:

“To assess whether information asymmetry plays a role in the relationship

between stock price informativeness and corporate expenditure.”

Three proxies of information asymmetry are used in this study, namely, firm size,

analyst following and bid-ask spreads. Large firms and firms with high analyst

344

following as well as firms with low bid-ask spreads are generally associated with less

severe information asymmetry problem. However, large firms produce a large amount

of public information through public announcements or financial disclosures (Atiase,

1985; Bhushan, 1989a) while analysts are more likely to rely on firm managers in

generating information (Agrawal et al., 2006). Applying ideas of the learning theory,

this information is not new as it has already been used by firm managers‟ in their past

investment decisions and is therefore unlikely to have any effect on firms‟ corporate

expenditure decisions. Further, analysts generate more industry and market-level

information (Piotroski & Roulstone, 2004) and introduce uninformed trading to the

stock markets (Easley et al., 1998). This reduces private information contents in stock

prices and discourages managerial learning as well as prompt responses of firm

managers in making their corporate decisions.

On the other hand, recent empirical research exhibits that a greater amount of private

information is produced in small firms (Chen et al., 2007; Bakke & Whited, 2010) and

in firms with low analyst following (Chen et al., 2007) as well as in firms with high bid-

ask spreads (Chan et al., 2013). This enables more intensive learning of firm managers

from the stock markets leading to more aggressive responses in adjusting firms‟

corporate expenditure.

Three separate hypotheses are formulated to examine whether the association between a

current year‟s stock price informativeness and the subsequent year‟s corporate

345

expenditure is dependent on firm size, analyst following and bid-ask spreads

respectively.

Hypothesis 2a (H2a) posits that the relationship between a current year‟s stock price


for small firms, ceteris paribus. The findings of this study indicate that the inverse


year‟s R&D expenditure and SGA costs are stronger in small firms. The effect of stock

price informativeness on the level of R&D expenditure and SGA costs is greater

(smaller) in small firms when their idiosyncratic volatility is weakening (strengthening).

However, no significant relationship is noted between a current year‟s stock price

informativeness and the subsequent year‟s CAPEX for both small and big firms.

Therefore, H2a hypothesizes that the association between a current year‟s stock price


information asymmetry proxied by firm size, and it is well supported when corporate

expenditure is represented by R&D expenditure and SGA costs. The findings of the

results show that H2a is not supported when corporate expenditure is represented by

CAPEX.

Hypothesis 2b (H2b) posits that the relationship between a current year‟s stock price


346

for firms with low analyst following, ceteris paribus. The findings of this study reveal

that the inverse relationship between a current year‟s stock price informativeness and the

subsequent year‟s R&D expenditure and SGA costs are stronger in firms with low

analyst following. The sub-samples of low analyst following are further segregated into

two sub-groups, namely, increasing and decreasing idiosyncratic volatilities. It was then

found that the impact of stock price informativeness on R&D expenditure and SGA

costs is greater (lower) in firms with low analyst following when their idiosyncratic

volatility is weakening (strengthening). Nevertheless, the association between a current

year‟s idiosyncratic volatility and CAPEX of the subsequent year are insignificant for

both firms with high and low analyst following.

Consequently, H2b which posits that information asymmetry represented by analyst

following plays a role in the connection between a current year‟s stock price

informativeness and the subsequent year‟s corporate expenditure is well supported when

corporate expenditure is represented by R&D expenditure and SGA costs, but not when

it is represented by CAPEX.

Hypothesis 2c (H2c) postulates that the relationship between a current year‟s stock price


for firms with high bid-ask spreads, ceteris paribus. It is observed that the inverse


year‟s R&D expenditure and SGA costs are stronger in firms with high bid-ask spreads.

347

In addition, the impact of stock price informativeness on R&D expenditure and SGA

costs is greater (smaller) in these firms when their idiosyncratic volatility is weakening

(strengthening). On the other hand, the relationship between a current year‟s stock price

informativeness and the subsequent year‟s CAPEX is insignificant for both firms with

low and high bid-ask spreads.

Therefore, H2c which hypothesizes that the relationship between a current year‟s stock

price informativeness and the subsequent year‟s corporate expenditure is dependent on

information asymmetry proxied by bid-ask spreads, is well supported when corporate

expenditure is represented by either R&D expenditure or SGA costs. H2c is not

supported when corporate expenditure is proxied by CAPEX.

Small firms and firms with low analyst following as well as firms with high bid-ask

spread are expected to be linked to higher information asymmetry. However, a greater

volume of private information that is available in these firms facilitates better

managerial learning of information that managers have yet to possess from firms‟ stock

prices. Consistent with the learning theory and information asymmetry theory, managers

from these firms learn new firm-specific information from the capital markets and are

induced to respond more quickly, thereby making changes to firms‟ R&D expenditure

and SGA costs in the subsequent year when stock price informativeness of a current

year changes. Nevertheless, insignificant changes are observed in firms‟ CAPEX in the

subsequent year when the stock price informativeness of a current year changes

348

indicating non-applicability of the learning theory and information asymmetry theory

when corporate expenditure is represented by CAPEX.

6.3 Contributions of the Study

This study contributes to the body of knowledge on stock price informativeness,

corporate expenditure and information asymmetry in terms of its findings. There are

four significant contributions in this study.

First, the empirical findings of this study contribute to the literature of learning theory. It

provides evidence that a current year‟s stock price informativeness is associated with the

subsequent year‟s corporate expenditure in R&D expenditure and SGA costs. This

implies that firm managers learn from stock prices of their own firms and react by

changing R&D and SGA costs. This study also finds that a current year‟s idiosyncratic

volatility is not associated with CAPEX of the subsequent year, signifying that

managerial learning is not associated with capital decisions.

Second, unlike previous studies that examine the impact of stock price informativeness

on the sensitivity of investment-to-price (Chen et al., 2007; Foucault & Frésard, 2012)

or cash savings-to-price (Frésard, 2012), this study provides direct evidence on how a

current year‟s stock price informativeness affects the level of three types of corporate

expenditures (R&D expenditure, CAPEX and SGA costs) as well as their changes in

349

the subsequent year. It fills the gap of the extant literature by linking stock price

informativeness to corporate expenditure. Further, this research seeks to find out how

firm managers react by changing their corporate expenditure in the subsequent year

when stock price informativeness strengthens as well as when it weakens during the

current year. Hence, this study provides meaningful insights and understanding on how

firms modify their corporate expenditure in the subsequent year in response to changes

in stock price informativeness during the current year. This study also finds that

idiosyncratic volatility of a current year is not associated with CAPEX in the subsequent

year, indicating that stock price informativeness is not a key determinant of CAPEX.

Third, the current study adds to the management accounting literature by presenting

useful insights on the cost “stickiness” behaviour of firm managers in changing R&D

expenditure and SGA costs. The findings of the study reveal that when firm-level stock

price informativeness is strengthening, a change in the current year‟s idiosyncratic

volatility is positively associated to the changes in R&D expenditure and SGA costs of

the following year. However, when stock price informativeness is weakening, there is

no immediate managerial reaction in modifying R&D and SGA expenditure. This

disproportionate change in corporate expenditure arising from changes in idiosyncratic

volatility is partly caused by the cost “stickiness” behaviour of firm managers. These

managers may need to evaluate the nature of declining stock price informativeness,

hence their reluctance to increase corporate expenditure when idiosyncratic volatility

deteriorates. Firm managers are only motivated to respond when conditions become

350

crucial, that is, when the relative idiosyncratic volatility (1-R2) drops significantly by 20

per cent.

Fourth, this research contributes to the information asymmetry literature by examining

whether the relationship between a current year‟s stock price informativeness and

corporate expenditure in the subsequent year is dependent on information asymmetry.

Three proxies of information asymmetry, namely, firm size, analyst following and bid-

ask spreads are deployed in this study. Small firms, firms with low analyst following

and firms with high bid-ask spreads are expected to be associated with higher

information asymmetry. Extant empirical studies, however, show that greater amount of

private information is available in these firms (Chen et al., 2007; Bakke & Whited,

2010). This study observes that the relationship between stock price informativeness and

corporate expenditure represented by R&D expenditure and SGA costs is stronger in

small firms and firms with low analyst following as well as firms with high bid-ask

spreads. This implies that greater extent of private information facilitates better

managerial learning by observing firms‟ stock prices. Managers of these firms are

motivated to respond more “aggressively” by changing corporate expenditure when

stock price informativeness changes. This study further finds that the relationship

between stock price informativeness and corporate expenditure represented by R&D

expenditure and SGA costs of these firms are stronger (weaker) when idiosyncratic

volatility is declining (strengthening). These research findings shed some light on the

roles of both information asymmetry and the direction of idiosyncratic volatility

351

(weakening or strengthening) in determining the relationship between stock price

informativeness and corporate expenditure.

6.4 Limitations of the Study

This study has empirically assessed the relationship between stock price informativeness

and corporate expenditure as well as how this relationship is dependent on information

asymmetry. The findings of this study have contributed to the body of knowledge on

stock price informativeness, corporate expenditure and information asymmetry.

However, it is important to acknowledge some of the limitations of this study and many

of them represent opportunities for future research.

a) Generalization of result findings

The sample obtained is from US public listed companies (PLCs) for the years 2003 to

2009. Consequently, the results of this study should not be extrapolated to other time

periods due to different capital market conditions arising from market booming or

contraction, as well as varying business cycles brought about by economic expansion or

recession. Similarly, generalization of these results to other countries should be

interpreted with caution due to different institutional settings. In addition, the findings

on change model in item 5.3.2.1 should be interpreted with care as the corresponding

sample size is much smaller when sub-samples of increasing and decreasing

idiosyncratic volatilities are formed.

352

b) Measure of stock price informativeness

In this study, stock price informativeness is measured by idiosyncratic volatility, as

guided by previous theoretical and empirical literature (Morck et al., 2000; Durnev et al.,

2004; Jin & Myers, 2006; Ferreira & Laux, 2007). The strength of interpreting the

research findings by relating stock price informativeness to corporate expenditure

decisions is mainly dependent on whether idiosyncratic volatility adequately captures

private information in the stock prices. To mitigate this concern, robustness tests are

carried out by using alternative measures of idiosyncratic volatility generated using

different models.36

Nevertheless, the measure of stock price informativeness is

acknowledged as one of the major limitations of this study.

c) Probability of Informed Trading measure not used

Previous empirical research use the measure of Probability of Informed Trading (PIN)

as a proxy for stock price informativeness (Chen et al., 2007; Ferreira & Laux, 2007;

Ferreira et al., 2011). The PIN measure developed by Easley, Kiefer and O‟Hara (1996)

is found to be associated with corporate decisions (Chen et al., 2007) and corporate

governance in the form of openness to the market for corporate control (Ferreira & Laux,

2007). However, PIN measure is not applied in this study to examine the hypotheses

developed due to non-availability of data.

36 Refer item 5.5.1 for additional tests carried out using different measures of idiosyncratic volatility.

353

d) Mechanism of the learning theory

In line with the learning theory, this study attributes the association between stock price

informativeness and corporate expenditure decisions to managers learning new firm-

specific information from stock prices. Prior studies suggest firm managers may not

possess information such as growth opportunities, future demand of firms‟ products,

strategic competition, relationships with firms‟ stakeholders, as well as financing

opportunities (Dow & Gorton, 1997; Subrahmanyam & Titman, 1999). However, the

kind of information managers have exactly learned from their stock prices is

unobservable. It is also difficult to perceive from secondary data on how this

information learned from firms‟ stock prices is translated into corporate expenditure

decisions. This constitutes one of the limitations of utilising empirical methodology for

this study.

e) Endogeneity issue

A change model, lead-lag approach and two-stage least squares regressions are

conducted in this study to address endogeneity issue. Nevertheless, the inferences made

from this study are still subject to standard caveats of endogeneity due to possibilities of

omitted variables. As such, it is acknowledged that this challenge is one of the

limitations of this study.

354

6.5 Recommendations for Future Research

Given the limitations of this study, there are many avenues available for future research

directions.

a) Extension of sample data

The study covers US PLCs for the years 2003 to 2009. Future research may update the

investigation until the latest year, that is, year 2012 as the lead-lag approach adopted in

this study requires data of corporate expenditure in year 2013. In addition, a

comparative study can be carried out to examine the impact of Sarbanes-Oxley Act on

managerial learning from stock prices and corporate expenditure decisions. This can be

done by extending the period of study backward for seven years, that is from 1996 to

2002 and comparing them with the current study covering the post Sarbanes-Oxley

period. Furthermore, future studies can consider replicating the current study to other

countries with a different information environment. Emerging countries such as China

and India can be considered to provide insights on the learning theory and corporate

expenditure decisions as well as the role of information asymmetry.

b) Qualitative research

Qualitative research incorporating questionnaires and interviews could be considered for

future investigations on managerial learning from observing stock prices as well as

examining the motivation of managers when they make corporate expenditure decisions.

355

6.6 Concluding Remarks

This study provides insights into two broad areas: the connection between stock price

informativeness and corporate expenditure as well as the moderating effect of

information asymmetry on this association. A negative link between a current year‟s

stock price informativeness and the subsequent year‟s R&D expenditure and SGA costs

is observed while no relationship is found between a current year‟s stock price

informativeness and CAPEX in the subsequent year. This study also sheds some light by

using a change model to examine changes in a current year‟s stock price

informativeness and changes in the subsequent year‟s R&D expenditure and SGA costs.

Asymmetric cost behaviour of firm managers is portrayed when it comes to modifying

corporate expenditure as idiosyncratic volatility changes. These findings are consistent

with the learning theory and they enrich our understanding of how firm-level corporate

expenditure responds to stock price informativeness.

This study also finds the relationship between stock price informativeness and corporate

expenditure is dependent on information asymmetry through three proxies: firm size,

analyst following and bid-ask spreads. Consistent with the learning theory and

information asymmetry theory, managers in small firms, in firms with low analyst

following and in firms with high bid-ask spreads are induced by the greater amount of

new private information available in their firms. They are more responsive and alter

their spending in R&D expenditure and SGA costs in the subsequent year when stock

price informativeness of a current year changes.

356

Stock prices informativeness portrays efficient signals for resource allocation and thus

to the functional efficiency of the stock markets (Durnev et al., 2003). Given the

significance of stock price informativeness in the allocation of scarce resources, the

findings of this study have important implications for firms and investors. This study

further provides meaningful insights on the appropriate corporate expenditure invested

by firm managers in the following year in response to the informativeness of the stock

price as well as how the association between stock price informativeness and corporate

expenditure is dependent on information asymmetry. It is believed that this study has

made a valuable contribution to the relevant body of knowledge.

357

REFERENCES

Abarbanell, J. S. & Bushee, B. J. (1997). Fundamental analysis, future earnings, and

stock prices. Journal of Accounting Research, 35(1), 1-24.

Aboody, D. & Lev, B. (2000). Information asymmetry, R&D, and insider gains. Journal

of Finance, 55(6), 2747-2766.

Agrawal, A., Chadha, S. & Chen, Mark A. (2006). Who is afraid of Reg FD? The

behaviour and performance of sell-side analysts following the SEC‟s fair

disclosure rules. The Journal of Business, 79(6), 2811-2834.

Akerlof, G. A. (1970). The market for "lemons": Quality uncertainty and the market

mechanism. Quarterly Journal of Economics, 84(3), 488-500.

Alves, P., Peasnell, K. & Taylor, P. (2010). The use of the R2 as a measure of firm-

specific information: A cross-country critique. Journal of Business Finance &

Accounting, 37(1-2), 1-26.

Amihud, Y. & Mendelson, H. (1980). Dealership market: Market-making with

inventory. Journal of Financial Economics, 8(1), 31-53.

Amihud, Y. & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of

Financial Economics, 17(2), 223-249.

Amihud, Y. & Mendelson, H. (1989). The effects of beta, bid-ask spread, residual risk,

and size on stock returns. Journal of Finance, 44(2), 479-486.

Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects.

Journal of Financial Markets, 5(1), 31-56.

358

An, H. & Zhang, T. (2013). Stock price synchronicity, crash risk, and institutional

investors. Journal of Corporate Finance, 21(0), 1-15.

Anderson, M. C., Banker, R. D. & Janakiraman, S. N. (2003). Are selling, general, and

administrative costs “sticky”? Journal of Accounting Research, 41(1), 47-63.

Anderson, M. C., Banker, R. D., Huang, R. & Janakiraman, S. N. (2007). Cost

behaviour and fundamental analysis of SG&A costs. Journal of Accounting,

Auditing & Finance, 22(1), 1-28.

Anderson, S. W. & Lanen, W. N. (2009). Understanding cost management: What can

we learn from the empirical evidence on “sticky costs?” Rice University,

University of Melbourne and University of Michigan. Available at

https://research.mbs.ac.uk/accounting-

finance/Portals/0/docs/2008/UnderstandingCostManagementWhatCanWeLearnfr

omtheEmpiricalEvidenceonStickyCosts.pdf.

Armstrong, C. S., Guay, W. R. & Weber, J. P. (2010). The role of information and

financial reporting in corporate governance and debt contracting. Journal of

Accounting and Economics, 50(2-3), 179-234.

Armstrong, C. S., Balakrishnan, K. & Cohen, D. (2012). Corporate governance and the

information environment: Evidence from state anti-takeover laws. Journal of


Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. The

American Economic Review, 53(5), 941-973.

359

Arrow, K. J. (1985). The economics of agency. In Pratt, J. & Zeckhauser, R. (Eds.),

Principals and agents: The structure of business. Boston: Harvard Business

School Press.

Asquith, P., Mikhail, M. B. & Au, A. S. (2005). Information content of equity analyst

reports. Journal of Financial Economics, 75(2), 245-282.

Atiase, R. K. (1985). Pre-disclosure information, firm capitalization, and security price

behaviour around earnings announcements. Journal of Accounting Research,

23(1), 21-36.

Audretsch, D. B. & Feldman, M. P. (1996). R&D spillovers and the geography of

innovation and production. The American Economic Review, 86(3), 630-640.

Ayers, B. & Freeman, R. (2003). Evidence that analyst following and institutional

ownership accelerate the pricing of future earnings. Review of Accounting

Studies, 8(1), 47-67.

Bagehot, W. (1971). The only game in town. Financial Analysts Journal, 27(2), 12-14

& 22.

Bakke, T.-E. & Whited, T. M. (2010). Which firms follow the market? An analysis of

corporate investment decisions. Review of Financial Studies, 23(5), 1941-1980.

Balakrishnan, R. & Soderstrom, N. S. (2000). The cost of system congestion: Evidence

from the healthcare sector. Journal of Management Accounting Research, 12,

97-114.

360

Balakrishnan, R., Petersen, M. J. & Soderstrom, N. S. (2004). Does capacity utilization

affect the "stickiness" of cost? Journal of Accounting, Auditing & Finance, 19(3),

283-299.

Balakrishnan, R. & Gruca, T. S. (2008). Cost stickiness and core competency: A note.

Contemporary Accounting Research, 25(4), 993-1006.

Balakrishnan, R., Labro, E. & Soderstrom, N. S. (2011). Cost structure and sticky costs.

The University of Iowa, University of North Carolina and University of

Colorado. Available at SSRN: http://ssrn.com/abstract=1562726

Banker, R. D. & Chen, L. (2006a). Predicting earnings using a model based on cost

variability and cost stickiness. Accounting Review, 81(2), 285-307.

Banker, R. D. & Chen, L. (2006b). Labour market characteristics and cross-country

differences in cost stickiness. Temple University and Georgia State University.

Available at

http://64.156.29.76/MAS/MASPAPERS2007/cost_behavior/Banker%20and%20

Chen.pdf.

Banker, R. D., Huang, R. & Natarajan, R. (2006). Does SG&A expenditure create a

long-lived asset? Temple University, City University of New York, The

University of Texas at Dallas

Banker, R. D., Byzalov, D. & Plehn-Dujowich, J. M. (2011a). Sticky cost behavior:

Theory and evidence. Temple University. Available at

http://astro.temple.edu/~dbyzalov/sticky.pdf.

http://ssrn.com/abstract=1562726

http://64.156.29.76/MAS/MASPAPERS2007/cost_behavior/Banker%20and%20Chen.pdf

http://64.156.29.76/MAS/MASPAPERS2007/cost_behavior/Banker%20and%20Chen.pdf

http://astro.temple.edu/~dbyzalov/sticky.pdf

361

Banker, R. D., Huang, R. & Natarajan, R. (2011b). Equity incentives and long-term

value created by SG&A expenditure. Contemporary Accounting Research, 28(3),

794-830.

Banker, R. D., Basu, S., Byzalov, D. & Chen, J. Y. S. (2012). Asymmetric timeliness of

earnings: Conservatism or sticky costs? Paper presented at the AAA 2013

Management Accounting Section (MAS) Meeting paper. Available at SSRN:

http://ssrn.com/abstract=213089.

Banker, R. D., Byzalov, D., Ciftci, M. & Mashruwala, R. (2012). The moderating effect

of prior sales changes on asymmetric cost behaviour. Temple University, SUNY

at Binghamton, University of Illinois at Chicago. Available at SSRN:


Banker, R. D., Byzalov, D. & Chen, L. (2013). Employment protection legislation,

adjustment costs and cross-country differences in cost behaviour. Journal of

Accounting and Economics, 55(1), 111-127.

Baptista, R. & Swann, P. (1998). Do firms in clusters innovate more? Research Policy,

27(5), 525-540.

Barker, V. L., III & Mueller, G. C. (2002). CEO characteristics and firm R&D spending.

Management Science, 48(6), 782-801.

Barth, M. E. (1994). Fair value accounting: Evidence from investment securities and the

market valuation of banks. The Accounting Review, 69(1), 1-25.

Basu, S. (1997). The conservatism principle and the asymmetric timeliness of earnings.

Journal of Accounting and Economics, 24(1), 3-37.



362

Baumgarten, D., Bonenkamp, U. & Homburg, C. (2010). The information content of the

SG&A ratio. Journal of Management Accounting Research, 22(1), 1-22.

Baysinger, B. & Hoskisson, R. E. (1989). Diversification strategy and R&D intensity in

multiproduct firms. The Academy of Management Journal, 32(2), 310-332.

Baysinger, B. & Hoskisson, R. E. (1990). The composition of boards of directors and

strategic control: Effects on corporate strategy. The Academy of Management

Review, 15(1), 72-87.

Baysinger, B., Kosnik, R. D. & Turk, T. A. (1991). Effects of board and ownership

structure on corporate R&D strategy. The Academy of Management Journal,

34(1), 205-214.

Beaver, W. H. (1968). The information content of annual earnings announcements.

Journal of Accounting Research, 6(3), 67-92.

Benartzi, S. & Thaler, R. H. (1995). Myopic loss aversion and the equity premium

puzzle. The Quarterly Journal of Economics, 110(1), 73-92.

Berk, J. B., Green, R. C. & Naik, V. (2004). Valuation and return dynamics of new

ventures. Review of Financial Studies, 17(1), 1-35.

Beuselinck, C., Joos, P., Khurana, I. K. & Van der Meulen, S. (2009). Mandatory IFRS

reporting and stock price informativeness. Tilburg University and University of

Missouri at Columbia. Available at SSRN: http://ssrn.com/abstract=1381242

Bhagat, S. & Bolton, B. (2008). Corporate governance and firm performance. Journal of

Corporate Finance, 14(3), 257-273.


363

Bhattacharya, U. & Spiegel, M. (1991). Insiders, outsiders, and market breakdowns.

Review of Financial Studies, 4(2), 255-282.

Bhushan, R. (1989a). Collection of information about publicly traded firms: Theory and

evidence. Journal of Accounting and Economics, 11(2–3), 183-206.

Bhushan, R. (1989b). Firm characteristics and analyst following. Journal of Accounting

and Economics, 11(2–3), 255-274.

Bourgeois, L. J. (1981). On the measurement of organizational slack. Academy of

Management Review, 6(1), 29-39.

Brennan, M. J., Jegadeesh, N. & Swaminathan, B. (1993). Investment analysis and the

adjustment of stock prices to common information. Review of Financial Studies,

6(4), 799-824.

Brennan, M. J. & Subrahmanyam, A. (1995). Investment analysis and price formation in

securities markets. Journal of Financial Economics, 38(3), 361-381.

Brennan, M. J. & Tamarowski, C. (2000). Investor relations, liquidity, and stock prices.

Journal of Applied Corporate Finance, 12(4), 26-37.

Brockman, P. & Yan, X. (2009). Block ownership and firm-specific information.

Journal of Banking & Finance, 33(2), 308-316.

Brockman, P., Liebenberg, I. & Schutte, M. (2010). Co-movement, information

production, and the business cycle. Journal of Financial Economics, 97(1), 107-

129.

Brown, S. & Hillegeist, S. (2007). How disclosure quality affects the level of

information asymmetry. Review of Accounting Studies, 12(2-3), 443-477.

364

Bublitz, B. & Ettredge, M. (1989). The information in discretionary outlays: Advertising,

research, and development. The Accounting Review, 64(1), 108-124.

Bushman, R. M. & Smith, A. J. (2001). Financial accounting information and corporate

governance. Journal of Accounting and Economics, 32(1–3), 237-333.

Calleja, K., Steliaros, M. & Thomas, D. C. (2006). A note on cost stickiness: Some

international comparisons. Management Accounting Research, 17(2), 127-140.

Cameron, A. C., Gelbach, J. B. & Miller, D. L. (2011). Robust inference with multiway

clustering. Journal of Business & Economic Statistics, 29(2), 238-249.

Campbell, J. Y., Lettau, M., Malkiel, B. G. & Xu, Y. (2001). Have individual stocks

become more volatile? An empirical exploration of idiosyncratic risk. The

Journal of Finance, 56(1), 1-43.

Capon, N., Farley, J. U. & Hoenig, S. (1990). Determinants of financial performance: A

meta-analysis. Management Science, 36(10), 1143-1159.

Carpenter, M. A. (2000). The price of change: The role of CEO compensation in

strategic variation and deviation from industry strategy norms. Journal of

Management, 26(6), 1179.

Carpenter, R. E. & Guariglia, A. (2008). Cash flow, investment, and investment

opportunities: New tests using UK panel data. Journal of Banking & Finance,

32(9), 1894-1906.

Cazier, R. A. (2011). Measuring R&D curtailment among short-horizon CEOs. Journal

of Corporate Finance, 17(3), 584-594.

365

Chambers, D., Jennings, R. & Thompson, I. I. R. B. (2002). Excess returns to R&D-

intensive firms. Review of Accounting Studies, 7(2-3), 133-158.

Chan, K. & Hameed, A. (2006). Stock price synchronicity and analyst coverage in

emerging markets. Journal of Financial Economics, 80(1), 115-147.

Chan, K., Hameed, A. & Kang, W. (2013). Stock price synchronicity and liquidity.

Journal of Financial Markets, 16(3), 414-438.

Chan, L. K. C., Lakonishok, J. & Sougiannis, T. (2001). The stock market valuation of

research and development expenditures. The Journal of Finance, 56(6), 2431-

2456.

Chauvin, K. W. & Hirschey, M. (1993). Advertising, R&D expenditures and the market

value of the firm. Financial Management, 22(4), 128-140.

Chen, C., Huang, A. G. & Jha, R. (2012a). Idiosyncratic return volatility and the

information quality underlying managerial discretion. Journal of Financial &

Quantitative Analysis, 47(4), 873-899.

Chen, C. X., Lu, H. & Sougiannis, T. (2012b). The agency problem, corporate

governance, and the asymmetrical behaviour of selling, general, and

administrative costs. Contemporary Accounting Research, 29(1), 252-282.

Chen, Q., Goldstein, I. & Jiang, W. (2007). Price informativeness and investment

sensitivity to stock price. The Review of Financial Studies, 20(3), 619-650.

Chen, S.-S. (2006). The economic impact of corporate capital expenditures: Focused

firms versus diversified firms. The Journal of Financial and Quantitative

Analysis, 41(2), 341-355.

366

Chung, K. H. & Jo, H. (1996). The impact of security analysts' monitoring and

marketing functions on the market value of firms. The Journal of Financial and


Chung, K. H., Wright, P. & Charoenwong, C. (1998). Investment opportunities and

market reaction to capital expenditure decisions. Journal of Banking & Finance,

22(1), 41-60.

Chung, K. H., Elder, J. & Kim, J.-C. (2010). Corporate governance and liquidity.

Journal of Financial & Quantitative Analysis, 45(2), 265-291.

Ciftci, M. & Cready, W. M. (2011). Scale effects of R&D as reflected in earnings and

returns. Journal of Accounting and Economics, 52(1), 62-80.

Ciftci, M., Lev, B. & Radhakrishnan, S. (2011). Is research and development mispriced

or properly risk adjusted? Journal of Accounting, Auditing & Finance, 26(1), 81-

116.

Cody, R. (2011). SAS statistics by example. Cary, North Carolina: SAS Institute Inc.

Cohen, W. M. & Levinthal, D. A. (1989). Innovation and learning: The two faces of

R&D. The Economic Journal, 99(397), 569-596.

Cohen, W. M. & Kleppler, S. (1996). A reprise of size and R&D. Economic Journal,

106(437), 925-951.

Coles, J. L., Daniel, N. D. & Naveen, L. (2008). Boards: Does one size fit all. Journal of

Financial Economics, 87(2), 329-356.

367

Coles, J. L., Lemmon, M. L. & Meschke, J. F. (2012). Structural models and

endogeneity in corporate finance: The link between managerial ownership and

corporate performance. Journal of Financial Economics, 103(1), 149-168.

Collins, D. W., Kothari, S. P. & Rayburn, J. D. (1987). Firm size and the information

content of prices with respect to earnings. Journal of Accounting and Economics,

9(2), 111-138.

Connolly, R. A. & Hirschey, M. (2005). Firm size and the effect of R&D on Tobin's q.

R&D Management, 35(2), 217-223.

Cooper, R. & Kaplan, R. S. (1988). The design of cost management systems: Text, cases

and readings. Upper Saddle River, N.J: Prentice Hall.

Copeland, T. E. & Galai, D. (1983). Information effects on the bid-ask spread. The

Journal of Finance, 38(5), 1457-1469.

Crawford, S. S., Roulstone, D. T. & So, E. C. (2012). Analyst initiations of coverage

and stock return synchronicity. The Accounting Review, 87(5), 1527-1553.

Crotty, M. (1998). The foundations of social research. Sydney: Allen & Unwin.

Dalla Via, N. & Perego, P. (Forthcoming). Sticky cost behaviour: Evidence from small

and medium sized companies. Accounting & Finance, available at

doi:10.1111/acfi.12020.

Daouk, H., Lee, C. M. C. & Ng, D. (2006). Capital market governance: How do security

laws affect market performance? Journal of Corporate Finance, 12(3), 560-593.

368

Dasgupta, S., Gan, J. & Gao, N. (2010). Transparency, price informativeness, and stock

return synchronicity: Theory and evidence. Journal of Financial & Quantitative

Analysis, 45(5), 1189-1220.

Dechow, P. M. & Sloan, R. G. (1991). Executive incentives and the horizon problem:

An empirical investigation. Journal of Accounting and Economics, 14(1), 51-89.

Dempsey, S. J. (1989). Pre-disclosure information search incentives, analyst following,

and earnings announcement price response. The Accounting Review, 64(4), 748-

757.

Demsetz, H. (1968). The cost of transacting. The Quarterly Journal of Economics, 82(1),

33-53.

Demsetz, H. & Lehn, K. (1985). The structure of corporate ownership: Causes and

consequences. Journal of Political Economy, 93(6), 1155-1177.

Devereux, M. & Schiantarelli, F. (1990). Investment, financial factors, and cash flow:

Evidence from U.K. panel data. In Hubbard, R. G. (Ed.), Asymmetric

information, corporate finance, and investment (pp. 279-306). Chicago:

University of Chicago Press.

Diamond, D. & Verrecchia, R. (1991). Disclosure, liquidity, and the cost of capital.

Journal of Finance 46(4), 1325-1359.

Ding, R., Hou, W., Kuo, J.-M. & Lee, E. (2013). Fund ownership and stock price

informativeness of Chinese listed firms. Journal of Multinational Financial

Management, 23(3), 166-185.

369

Dopuch, N. & Simunic, D. (1982). Competition in auditing: An assessment. Paper

presented at the Fourth Symposium on Auditing Research, Univeristy of Illinois.

Dornbusch, R. & Fischer, S. (1992). Macroeconomics. New York: McGraw-Hill.

Douma, S. & Schreuder, H. (2008). Economic approaches to organizations (4th ed.).

England: Pearson Education Limited.

Dow, J. & Gorton, G. (1997). Stock market efficiency and economic efficiency: Is there

a connection? Journal of Finance, 52(3), 1087-1129.

Dow, J. & Rahi, R. (2003). Informed trading, investment, and welfare. The Journal of

Business, 76(3), 439-454.

Durnev, A., Morck, R., Yeung, B. & Zarowin, P. (2003). Does greater firm-specific

return variation mean more or less informed stock pricing? Journal of

Accounting Research, 41(5), 797-836.

Durnev, A., Morck, R. & Yeung, B. (2004). Value-enhancing capital budgeting and

firm-specific stock return variation. The Journal of Finance, 59(1), 65-105.

Dye, R. A. & Sridhar, S. (2002). Resource allocation effects of price reactions to

disclosures. Contemporary Accounting Research, 19(3), 385-410.

Easley, D., Kiefer, N. M. & O'Hara, M. (1996). Cream-skimming or profit-sharing? The

curious role of purchased order flow. The Journal of Finance, 51(3), 811-833.

Easley, D., O'Hara, M. & Paperman, J. (1998). Financial analysts and information-based

trade. Journal of Financial Markets, 1(2), 175-201.

370

Eberhart, A. C., Maxwell, W. F. & Siddique, A. R. (2004). An examination of long-term

abnormal stock returns and operating performance following R&D increases.

The Journal of Finance, 59(2), 623-650.

Eberhart, A. C., Maxwell, W. F. & Siddique, A. R. (2008). A re-examination of the

tradeoff between the future benefit and riskiness of R&D increases. Journal of


Erickson, G. & Jacobson, R. (1992). Gaining comparative advantage through

discretionary expenditures: The returns to R&D and advertising. Management

Science, 38(9), 1264-1279.

Eriksson, P. & Kovalainen, A. (2008). Research philosophy. London: SAGE

Publications Ltd.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work.


Fama, E. F. & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests.

Journal of Political Economy, 81(3), 607-636.

Fama, E. F. & Jensen, M. C. (1983). Agency problems and residual claims. Journal of

Law and Economics, 26(2), 327-349.

Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and

bonds. Journal of Financial Economics, 33(1), 3-56.

Fama, E. F. & French, K. R. (1995). Size and book-to-market factors in earnings and

returns. The Journal of Finance, 50(1), 131-155.

371

Fama, E. F. & French, K. R. (1996). Multifactor explanations of asset pricing anomalies.


Fazzari, S. M., Hubbard, R. G. & Petersen, B. C. (1988). Financing constraints and

corporate investment. Brookings Papers on Economic Activity, 1, 141-195.

Fernandes, N. & Ferreira, M. A. (2008). Does international cross-listing improve the

information environment? Journal of Financial Economics, 88(2), 216-244.

Fernandes, N. & Ferreira, M. A. (2009). Insider trading laws and stock price

informativeness. Review of Financial Studies, 22(5), 1845-1887.

Ferreira, D., Ferreira, M. A. & Raposo, C. C. (2011). Board structure and price

informativeness. Journal of Financial Economics, 99(3), 523-545.

Ferreira, M. A. & Laux, P. A. (2007). Corporate governance, idiosyncratic risk, and

information flow. The Journal of Finance, 62(2), 951-989.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: SAGE

Publication Ltd.

Finkelstein, S. & Hambrick, D. C. (1990). Top-management-team tenure and

organizational outcomes: The moderating role of managerial discretion.

Administrative Science Quarterly, 35(3), 484-503.

Finkelstein, S. & Hambrick, D. C. (1996). Strategic Leadership: Top executives and

their effects on organizations. St Paul, MN: West.

Fischer, S. & Merton, R. C. (1984). Macroeconomics and finance: The role of the stock

market. Carnegie-Rochester Conference Series on Public Policy, 21, 57-108.

372

Foucault, T. & Gehrig, T. (2008). Stock price informativeness, cross-listings, and

investment decisions. Journal of Financial Economics, 88(1), 146-168.

Foucault, T. & Frésard, L. (2012). Cross-listing, investment sensitivity to stock price,

and the learning hypothesis. Review of Financial Studies, 25(11), 3305-3350.

Francis, J. & Soffer, L. (1997). The relative informativeness of analysts' stock

recommendations and earnings forecast revisions. Journal of Accounting

Research, 35(2), 193-211.

Frankel, R. & Li, X. (2004). Characteristics of a firm's information environment and the

information asymmetry between insiders and outsiders. Journal of Accounting

and Economics, 37(2), 229-259.

Frankel, R., Kothari, S. P. & Weber, J. (2006). Determinants of the informativeness of

analyst research. Journal of Accounting and Economics, 41(1–2), 29-54.

Freeman, R. N. (1987). The association between accounting earnings and security

returns for large and small firms. Journal of Accounting and Economics, 9(2),

195-228.

French, K. R. & Roll, R. (1986). Stock return variances: The arrival of information and

the reaction of traders. Journal of Financial Economics, 17(1), 5-26.

Frésard, L. (2012). Cash savings and stock price informativeness. Review of Finance,

16(4), 985-1012.

Fried, D. & Givoly, D. (1982). Financial analysts' forecasts of earnings: A better

surrogate for market expectations. Journal of Accounting and Economics, 4(2),

85-107.

373

Gaspar, J. M. & Massa, M. (2006). Idiosyncratic volatility and product market

competition. The Journal of Business, 79(6), 3125-3152.

Giammarino, R., Heinkel, R., Hollifield, B. & Li, K. (2004). Corporate decisions,

information and prices: Do managers move prices or do prices move managers?

Economic Notes, 33(1), 83-110.

Gillan, S. L., Hartzell, J. C. & Starks, L. T. (2003). Explaining corporate governance:

Boards, bylaws, and charter provisions. Weinberg Center for Corporate

Governance Working Paper No. 2003-03. Available at SSRN:

http://ssrn.com/abstract=442740 or doi:10.2139.

Givoly, D. & Lakonishok, J. (1979). The information content of financial analysts'

forecasts of earnings: Some evidence on semi-strong inefficiency. Journal of

Accounting and Economics, 1(3), 165-185.

Glosten, L. R. & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist

market with heterogeneously informed traders. Journal of Financial Economics,

14(1), 71-100.

Goldstein, I. & Guembel, A. (2008). Manipulation and the allocational role of prices.

The Review of Economic Studies, 75(1), 133-164.

Gompers, P., Ishii, J. & Metrick, A. (2003). Corporate governance and equity prices.

Quarterly Journal of Economics, 118(1), 107-155.

Gordon, L. A. & Iyengar, R. J. (1996). Return on investment and corporate capital

expenditures: Empirical evidence. Journal of Accounting and Public Policy,

15(4), 305-325.


374

Gow, I. D., Ormazabal, G. & Taylor, D. J. (2010). Correcting for cross-sectional and

time-series dependence in accounting research. Accounting Review, 85(2), 483-

512.

Goyal, A. & Santa-Clara, P. (2003). Idiosyncratic risk matters! The Journal of Finance,

58(3), 975-1007.

Grabowski, H. G. (1968). The determinants of industrial research and development: A

study of the chemical, drug, and petroleum industries. Journal of Political

Economy, 76(2), 292-306.

Grant, E. B. (1980). Market implications of differential amounts of interim information.

Journal of Accounting Research, 18(1), 255-268.

Graves, S. B. (1988). Institutional ownership and corporate R&D in the computer

industry. The Academy of Management Journal, 31(2), 417-428.

Griliches, Z. (1981). Market value, R&D, and patents. Economics Letters, 7(2), 183-187.

Griliches, Z. (1998). The search for R&D spillovers. R&D and productivity: The

econometric evidence (pp. 251-268): University of Chicago Press.

Grimm, C. M. & Smith, K. G. (1991). Management and organizational change: A note

on the railroad industry. Strategic Management Journal, 12(7), 557-562.

Griner, E. H. & Gordon, L. A. (1995). Internal cash flow, insider ownership, and capital

expenditures: A test of pecking order and managerial hypotheses. Journal of

Business Finance & Accounting, 22(2), 179-199.

Grossman, G. & Helpman, E. (1991). Innovation and growth in the global economy.

Cambridge: MIT Press.

375

Grossman, S. (1976). On the efficiency of competitive stock markets where trades have

diverse information. The Journal of Finance, 31(2), 573-585.

Grossman, S. J. & Stiglitz, J. E. (1980). On the impossibility of informationally efficient

markets. The American Economic Review, 70(3), 393-408.

Guba, E. G. (1990). The alternative paradigm dialog In Guba, E. G. (Ed.), The paradigm

dialog (pp. 17-27): Sage Publications.

Gul, F. A. & Tsui, J. S. L. (1998). A test of the free cash flow and debt monitoring

hypotheses: Evidence from audit pricing. Journal of Accounting and Economics,

24(2), 219-237.

Gul, F. A. (2001). Free cash flow, debt-monitoring and managers' LIFO/FIFO policy

choice. Journal of Corporate Finance, 7(4), 475-492.

Gul, F. A., Fung, S. Y. K. & Jaggi, B. (2009). Earnings quality: Some evidence on the

role of auditor tenure and auditors' industry expertise. Journal of Accounting &

Economics, 47(3), 265-287.

Gul, F. A., Kim, J.-B. & Qiu, A. A. (2010). Ownership concentration, foreign

shareholding, audit quality, and stock price synchronicity: Evidence from China.

Journal of Financial Economics, 95(3), 425-442.

Gul, F. A., Cheng, L. T. W. & Leung, T. Y. (2011a). Perks and the informativeness of

stock prices in the Chinese market. Journal of Corporate Finance, 17(5), 1410-

1429.

376

Gul, F. A., Srinidhi, B. & Ng, A. C. (2011b). Does board gender diversity improve the

informativeness of stock prices? Journal of Accounting & Economics, 51(3),

314-338.

Gupta, A. K. (1987). SBU strategies, corporate-SBU relations, and SBU effectiveness in

strategy implementation. Academy of Management Journal, 30(3), 477-500.

Haggard, K. S., Martin, X. & Pereira, R. (2008). Does voluntary disclosure improve

stock price informativeness? Financial Management, 37(4), 747-768.

Hambrick, D. C. & Fukutomi, G. D. S. (1991). The seasons of a CEO's tenure. The

Academy of Management Review, 16(4), 719-742.

Hamermesh, D. S. (2000). The craft of labourmetrics. Industrial and Labor Relations

Review, 53(3), 363-380.

Hamilton, J. L. (1978). Marketplace organization and marketability: NASDAQ, the

stock exchange, and the national market system. The Journal of Finance, 33(2),

487-503.

Hansen, G. S. & Hill, C. W. L. (1991). Are institutional investors myopic? A time-series

study of four technology-driven industries. Strategic Management Journal, 12(1),

1-16.

Harford, J., Mansi, S. A. & Maxwell, W. F. (2008). Corporate governance and firm cash

holdings in the US. Journal of Financial Economics, 87(3), 535-555.

Hayek, F. A. (1945). The use of knowledge in society. The American Economic Review,

35(4), 519-530.

377

Haynes, K. T. & Hillman, A. (2010). The effect of board capital and CEO power on

strategic change. Strategic Management Journal, 31(11), 1145-1163.

He, W., Li, D., Shen, J. & Zhang, B. (2013). Large foreign ownership and stock price

informativeness around the world. Journal of International Money and Finance,

36, 211-230.

Healy, P. M., Hutton, A. P. & Palepu, K. G. (1999). Stock performance and

intermediation changes surrounding sustained increases in disclosure.

Contemporary Accounting Research, 16(3), 485-520.

Healy, P. M. & Palepu, K. G. (2001). Information asymmetry, corporate disclosure, and

the capital markets: A review of the empirical disclosure literature. Journal of


Henderson, R. & Cockburn, I. (1996). Scale, scope, and spillovers: The determinants of

research productivity in drug discovery. The RAND Journal of Economics, 27(1),

32-59.

Hill, C. W. L. & Snell, S. A. (1988). External control, corporate strategy, and firm

performance in research-intensive industries. Strategic Management Journal,

9(6), 577-590.

Himmelberg, C. P. & Petersen, B. C. (1994). R & D and internal finance: A panel study

of small firms in high-tech industries. The Review of Economics and Statistics,

76(1), 38-51.

Hirschey, M. (1985). Market structure and market value. The Journal of Business, 58(1),

89-98.

378

Ho, Y. K., Xu, Z. & Yap, C. M. (2004). R&D investment and systematic risk.

Accounting & Finance, 44(3), 393-418.

Ho, Y. K., Tjahjapranata, M. & Yap, C. M. (2006). Size, leverage, concentration, and

R&D investment in generating growth opportunities. Journal of Business, 79(2),

851-876.

Holmström, B. & Tirole, J. (1993). Market liquidity and performance monitoring.

Journal of Political Economy, 101(4), 678-709.

Hölmstrom, B. (1979). Moral hazard and observability. The Bell Journal of Economics,

10(1), 74-91.

Hong, H., Lim, T. & Stein, J. C. (2000). Bad news travels slowly: Size, analyst coverage,

and the profitability of momentum strategies. The Journal of Finance, 55(1),

265-295.

Hou, W., Kuo, J.-M. & Lee, E. (2012). The impact of state ownership on share price

informativeness: The case of the Split Share Structure Reform in China. The

British Accounting Review, 44(4), 248-261.

Hsin, C.-W. & Tseng, P.-W. (2012). Stock price synchronicities and speculative trading

in emerging markets. Journal of Multinational Financial Management, 22(3),

82-109.

Huang, R. D. & Stoll, H. R. (1996). Dealer versus auction markets: A paired comparison

of execution costs on NASDAQ and the NYSE. Journal of Financial Economics,

41(3), 313-357.

379

Hutton, A. P., Marcus, A. J. & Tehranian, H. (2009). Opaque financial reports, R2, and

crash risk. Journal of Financial Economics, 94(1), 67-86.

Inci, A. C., Lee, B. S. & Suh, J. (2009). Capital investment and earnings: International

evidence. Corporate Governance: An International Review, 17(5), 526-545.

Irvine, P. J. & Pontiff, J. (2009). Idiosyncratic return volatility, cash flows, and product

market competition. Review of Financial Studies, 22(3), 1149-1177.

Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: Evidence from

firms' patents, profits and market value. American Economic Review, 76(5), 984-

999.

Janakiraman, S. (2010). Discussion of "The information content of the SG&A ratio".

Journal of Management Accounting Research, 22(1), 23-30.

Jensen, M. C. & Meckling, W. H. (1976). Theory of the firm: Managerial behavior,

agency costs and ownership structure. Journal of Financial Economics, 3(4),

305-360.

Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers.


Jensen, M. C. (2005). Agency costs of overvalued equity. Financial Management, 34(1),

5-19.

Jiang, G. J., Xu, D. & Tong, Y. (2009). The information content of idiosyncratic

volatility. Journal of Financial & Quantitative Analysis, 44(1), 1-28.

Jin, L. & Myers, S. C. (2006). R2 around the world: New theory and new tests. Journal

of Financial Economics, 79(2), 257-292.

380

Johnson, S., La Porta, R., Lopez-de-Silanes, F. & Shleifer, A. (2000). Tunneling.


Jones, J. J. (1991). Earnings management during import relief investigations. Journal of


Kama, I. & Weiss, D. (2013). Do earnings targets and managerial incentives affect

sticky costs? Journal of Accounting Research, 51(1), 201-224.

Kamien, M. I. & Schwartz, N. L. (1978). Self-financing of an R&D Project. The


Kamien, M. I. & Schwartz, N. L. (1982). Market structure and innovation. New York:

Cambridge University Press.

Karpoff, J. M. (1986). A theory of trading volume. The Journal of Finance, 41(5), 1069-

1087.

Kelly, P. J. (2005). Information efficiency and firm-specific return variation. SSRN

eLibrary. University of South Florida,. EFA 2005 Moscow Meetings Paper.

Available at http://ssrn.com/paper=676636.

Kerstein, J. & Kim, S. (1995). The incremental information content of capital

expenditures. The Accounting Review, 70(3), 513-526.

Khanna, T. & Thomas, C. (2009). Synchronicity and firm interlocks in an emerging

market. Journal of Financial Economics, 92(2), 182-204.

Kim, J.-B. & Shi, H. (2010). Voluntary IFRS adoption and stock price synchronicity:

Do analyst following and institutional infrastructure matter? City University of

http://ssrn.com/paper=676636

381

Hong Kong & Fudan University. Available at SSRN:


Kor, Y. Y. (2006). Direct and interaction effects of top management team and board

compositions on R&D investment strategy. Strategic Management Journal,

27(11), 1081-1099.

Kothari, S. P. (2001). Capital markets research in accounting. Journal of Accounting

and Economics, 31(1-3), 105-231.

Kothari, S. P., Laguerre, T. E. & Leone, A. J. (2002). Capitalization versus expensing:

Evidence on the uncertainty of future earnings from capital expenditures versus

R&D outlays. Review of Accounting Studies, 7(4), 355-382.

Kuhn, T. S. (1962). The structure of scientific revolutions. Illinois: University of

Chicago Press.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315-

1335.

Lang, L. H. P., Stulz, R. & Walkling, R. A. (1991). A test of the free cash flow

hypothesis: The case of bidder returns. Journal of Financial Economics, 29(2),

315-335.

Larcker, D. F. & Rusticus, T. O. (2010). On the use of instrumental variables in

accounting research. Journal of Accounting and Economics, 49(3), 186-205.

Lee, D. W. & Liu, M. H. (2011). Does more information in stock price lead to greater or

smaller idiosyncratic return volatility? Journal of Banking & Finance, 35(6),

1563-1580.


382

Lehmann, B. N. (1990). Residual risk revisited. Journal of Econometrics, 45(1–2), 71-

97.

Leuz, C. & Verrecchia, R. E. (2000). The economic consequences of increased

disclosure. Journal of Accounting Research, 38(3), 91-124.

Lev, B. (1988). Toward a theory of equitable and efficient accounting policy. The

Accounting Review, 63(1), 1-22.

Lev, B. & Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of


Lev, B. & Sougiannis, T. (1996). The capitalization, amortization, and value-relevance

of R&D. Journal of Accounting and Economics, 21(1), 107-138.

Lev, B. (1999). R&D and capital markets. Journal of Applied Corporate Finance, 11(4),

21-35.

Levine, R. (1997). Financial development and economic growth: Views and agenda.

Journal of Economic Literature, 35(2), 688-726.

Li, K., Morck, R., Yang, F. & Yeung, B. (2004). Firm-specific variation and openness in

emerging markets. The Review of Economics and Statistics, 86(3), 658-669.

Linck, J. S., Netter, J. M. & Yang, T. (2008). The determinants of board structure.

Journal of Financial Economics, 87(2), 308-328.

Luo, X. & de Jong, P. (2012). Does advertising spending really work? The intermediate

role of analysts in the impact of advertising on firm value. Journal of the

Academy of Marketing Science, 40(4), 605-624.

383

Luo, Y. (2005). Do insiders learn from outsiders? Evidence from mergers and

acquisitions. The Journal of Finance, 60(4), 1951-1982.

Lys, T. & Sohn, S. (1990). The association between revisions of financial analysts'

earnings forecasts and security-price changes. Journal of Accounting and

Economics, 13(4), 341-363.

McAlister, L., Srinivasan, R. & Kim, M. (2007). Advertising, research and development,

and systematic risk of the firm. Journal of Marketing, 71(1), 35-48.

McConnell, J. J. & Muscarella, C. J. (1985). Corporate capital expenditure decisions and

the market value of the firm. Journal of Financial Economics, 14(3), 399-422.

McEachern, W. A. & Romeo, A. A. (1978). Stockholder control, uncertainty and the

allocation of resources to research and development. The Journal of Industrial

Economics, 26(4), 349-361.

Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete

information. Journal of Finance, 42(3), 483-510.

Mizik, N. & Jacobson, R. (2003). Trading-off between value creation and value

appropriation: The financial implications of shifts in strategic emphasis. Journal

of Marketing, 67(1), 63-76.

Morck, R., Shleifer, A., Vishny, R. W., Shapiro, M. & Poterba, J. M. (1990). The stock

market and investment: Is the market a sideshow? Brookings Papers on

Economic Activity, 1990(2), 157-215.

384

Morck, R., Yeung, B. & Yu, W. (2000). The information content of stock markets: Why

do emerging markets have synchronous stock price movements? Journal of

Financial Economics, 58(1-2), 215-260.

Moyer, R. C., Chatfield, R. E. & Sisneros, P. M. (1989). Security analyst monitoring

activity: Agency costs and information demands. The Journal of Financial and


Myers, S. C. (1984). The capital structure puzzle. Journal of Finance, 39(3), 574-592.

Myers, S. C. & Majluf, N. S. (1984). Corporate financing and investment decisions

when firms have information that investors do not have. Journal of Financial

Economics, 13(2), 187-221.

Nadiri, M. I. & Prucha, I. R. (1996). Estimation of the depreciation rate of physical and

R&D capital in the U.S. total manufacturing sector. Economic Inquiry, 34(1), 43.

Newey, W. K. & West, K. D. (1987). A simple, positive semi-definite,

heteroskedasticity and autocorrelation consistent covariance matrix.

Econometrica, 55(3), 703-708.

Noreen, E. (1991). Conditions under which activity-based cost systems provide relevant

costs. Journal of Management Accounting Research, 3, 159-168.

Noreen, E. & Soderstrom, N. (1994). Are overhead costs strictly proportional to

activity?: Evidence from hospital departments. Journal of Accounting and

Economics, 17(1–2), 255-278.

385

Noreen, E. & Soderstrom, N. (1997). The accuracy of proportional cost models:

Evidence from hospital service departments. Review of Accounting Studies, 2(1),

89-114.

O'Rourke, N., Hatcher, L. & Stepanski, E. J. (2009). A step-by-step approach to using

SAS for univariate and multivariate statistics (2nd ed.). Cary, North Carolina:

SAS Institute Inc.

Pakes, A. (1985). On patents, R&D, and the stock market rate of return. Journal of

Political Economy, 93(2), 390-409.

Palepu, K. (1985). Diversification strategy, profit performance and the entropy measure.

Strategic Management Journal, 6(3), 239-255.

Pauly, M. V. (1974). Over-insurance and public provision of insurance: The roles of

moral hazard and adverse selection. The Quarterly Journal of Economics, 88(1),

44-62.

Penman, S. H. & Zhang, X. J. (2002). Accounting conservatism, the quality of earnings,

and stock returns. The Accounting Review, 77(2), 237-264.

Penrose, E., Penrose, E. T. & Pitelis, C. (2009). The theory of the growth of the firm (4th

ed.). London: Oxford University Press.

Peress, J. (2010). Product market competition, insider trading, and stock market

efficiency. The Journal of Finance, 65(1), 1-43.

Petersen, C. & Plenborg, T. (2006). Voluntary disclosure and information asymmetry in

Denmark. Journal of International Accounting, Auditing and Taxation, 15(2),

127-149.

386

Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing

approaches. Review of Financial Studies, 22(1), 435-480.

Pierce, J. R. & Aguinis, H. (2013). The too-much-of-a-good-thing effect in management.

Journal of Management, 39(2), 313-338.

Piotroski, J. D. & Roulstone, B. T. (2004). The influence of analysts, institutional

investors, and insiders on the incorporation of market, industry, and firm-specific

information into stock prices. The Accounting Review, 79(4), 1119-1151.

Ponterotto, J. G. (2005). Qualitative reserach in counseling psychology: A primer on

reserach paradigms and philosophy of science. Journal of Counseling

Psychology, 52(2), 126-136.

Porter, M. E. (1990). The competitive advantage of nations: With a new introduction.

New York: Free Press.

Rajagopalan, N. & Spreitzer, G. M. (1997). Toward a theory of strategic change: A

multi-lens perspective and integrative framework. The Academy of Management

Review, 22(1), 48-79.

Rajgopal, S. & Venkatachalam, M. (2011). Financial reporting quality and idiosyncratic

return volatility. Journal of Accounting and Economics, 51(1-2), 1-20.

Richardson, V. J. (2000). Information asymmetry and earnings management: Some

evidence. Review of Quantitative Finance and Accounting, 15(4), 325-347.

Roberts, P. W. (2001). Innovation and firm-level persistent profitability: A

Schumpeterian framework. Managerial and Decision Economics, 22(4-5), 239-

250.

387

Roll, R. (1988). R2. The Journal of Finance, 43(3), 541-566.

Rothwell, R. (1984). The role of small firms in the emergence of new technologies.

Omega, 12(1), 19-29.

Scherer, F. M. (1984). Innovation and growth: Schumpeterian perspectives. Cambridge:

MIT Press.

Schumpeter, J. A. (1942). Capitalism, socialism and democracy. New York: Harper and

Row.

Shadab, H. B. (2008). Innovation and corporate governance: The impact of Sarbanes-

Oxley. University of Pennsylvania Journal of Business and Employment Law, 10,

955-1008.

Shi, C. (2003). On the trade-off between the future benefits and riskiness of R&D: A

bondholders‟ perspective. Journal of Accounting and Economics, 35(2), 227-254.

Sougiannis, T. (1994). The accounting based valuation of corporate R&D. The

Accounting Review, 69(1), 44-68.

Spiegel, M. I. & Wang, X. (2005). Cross-sectional variation in stock returns: Liquidity

and idiosyncratic risk. Available at SSRN: http://ssrn.com/abstract=709781.

Yale ICF Working Paper No. 05-13; EFA 2005 Moscow Meetings Paper.

Srinivasan, R., Lilien, G. L. & Sridhar, S. (2011). Should firms spend more on research

and development and advertising during recessions? Journal of Marketing, 75(3),

49-65.

Stoll, H. R. (2000). Presidential address: Friction. The Journal of Finance, 55(4), 1479-

1514.


388

Stowe, J. D. & Xing, X. (2011). R2: Does it matter for firm valuation? Financial Review,

46(2), 233-250.

Strong, J. S. & Meyer, J. R. (1990). Sustaining investment, discretionary investment,

and valuation: A residual funds study of the paper industry. In Hubbard, R. G.

(Ed.), Asymmetric information, corporate finance, and investment (pp. 127-148).

Chicago: University of Chicago Press.

Subrahmanyam, A. & Titman, S. (1999). The going-public decision and the

development of financial markets. The Journal of Finance, 54(3), 1045-1082.

Sundaram, R. K. & Yermack, D. L. (2007). Pay me later: Inside debt and its role in

managerial compensation. The Journal of Finance, 62(4), 1551-1588.

Thompson, S. B. (2011). Simple formulas for standard errors that cluster by both firm

and time. Journal of Financial Economics, 99(1), 1-10.

Titman, S., Wei, K. C. J. & Xie, F. (2004). Capital investments and stock returns. The

Journal of Financial and Quantitative Analysis, 39(4), 677-700.

Tobin, J. (1984). On the efficiency of the financial system. Lloyds Bank Review, 153, 1-

15.

Verrecchia, R. E. (2001). Essays on disclosure. Journal of Accounting and Economics,

32(1–3), 97-180.

Vogt, S. C. (1994). The cash flow/investment relationship: Evidence from U.S.

manufacturing firms. Financial Management, 23(2), 3-20.

Vogt, S. C. (1997). Cash flow and capital spending: Evidence from capital expenditure

announcements. Financial Management, 26(2), 44-57.

389

Weidenmier, M. L. & Subramaniam, C. (2003). Additional evidence on the sticky

behavior of costs. Texas Christian University. Available at SSRN:


Weiss, D. (2010). Cost behaviour and analysts‟ earnings forecasts. The Accounting

Review, 85(4), 1441-1471.

Welker, M. (1995). Disclosure policy, information asymmetry, and liquidity in equity

markets. Contemporary Accounting Research, 11(2), 801-827.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a

direct test for heteroskedasticity. Econometrica, 48(4), 817-838.

White, H. (1984). Asymptotic theory for econometricians. Orlando, FL: Academic Press,

Harcourt Brace Jovanovich.

Wiersema, M. F. & Bowen, H. P. (2008). Corporate diversification: The impact of

foreign competition, industry globalization, and product diversification. Strategic

Management Journal, 29(2), 115-132.

Wooldridge, J. M. (2009). Introductory econometrics: A modern approach (4th ed.).

Mason, Ohio: South Western Cengage Learning.

Wurgler, J. (2000). Financial markets and the allocation of capital. Journal of Financial

Economics, 58(1-2), 187-214.

Xu, N., Chan, K. C., Jiang, X. & Yi, Z. (2013). Do star analysts know more firm-

specific information? Evidence from China. Journal of Banking & Finance,

37(1), 89-102.


390

Xu, Y. & Malkiel, Burton G. (2003). Investigating the behaviour of idiosyncratic

volatility. The Journal of Business, 76(4), 613-645.

Zhang, Y. & Rajagopalan, N. (2010). Once an outsider, always an outsider? CEO origin,

strategic change, and firm performance. Strategic Management Journal, 31(3),

334-346.

stock price informativeness, corporate expenditure … · stock price informativeness, corporate...

Documents