fundamental and technical analysis: substitutes or ......our analysis commences with a consideration...

Fundamental and Technical Analysis:

Substitutes or Complements?

Jenni Lee Bettmars

May, 2007

A thesis submitted in partial fulfilment o f the requirements for the degree o f Doctor o f

Philosophy in Finance at the Australian National University

Declaration

I hereby certify that this thesis is entirely the work of the author and has not been

submitted to any other institution or University. Furthermore, all sources used in the

preparation of the thesis have been acknowledged in the usual manner.

Jenni Lee Bettman

May 31, 2007.

Acknowledgements

Many people have provided me with support and inspiration throughout the

duration of my PhD. One must begin with my supervisors, Dr Emma Welch and

Professor Tom Smith, whose advice, encouragement and enthusiasm were

indispensable. Their guidance and good humour were greatly appreciated

throughout the dissertation process. I have also been lucky enough to work with

great colleagues within the School of Finance and Applied Statistics, and I consider

these people to be, above all, good friends. Special thanks to Jennifer Hunt, Tracy

Skinner and Bronwen Whiting, for providing continual advice and support, as well

as, most importantly, laughter.

I also want to recognise and acknowledge the people who gave me the opportunity

to be in this position, namely, my family. My Mother and Father, Brothers and

Grandparents, are part of who I am, and I feel lucky to have them all in my life.

Lastly, the biggest thanks must go to my husband and daughter, Steve and Lana.

Without you both, I would have never completed this PhD.

This thesis is dedicated to my daughter, Lana Kennedy Sault, and all her future

siblings.

Abstract

While the fundamental and technical analysis literatures invest considerable effort

in assessing their respective ability to explain share prices, they invariably do so

without reference to each other. In this context, we propose an equity valuation

model integrating both fundamental and technical analysis and, in doing so,

recognise their potential as complements rather than as substitutes in such valuation

exercises. Specifically, we augment fundamental valuation models with a suite of

technical measures to investigate whether these resultant hybrid models have

superior explanatory power relative to models incorporating either measures of

fundamental or technical information in isolation.

Our analysis commences with a consideration of the strength of our hybrid models

in explaining the share price of listed Australian companies relative to purely

fundamental or technical models. Thereafter, we extend our investigation to assess

the complementary nature of fundamental and technical analysis in the valuation of

listed companies from the United States (“US”), as well as listed companies in

seven other countries.

Preliminary testing confirms the positive dependence of contemporaneous price on

the fundamental factors commonly employed in modelling, namely book value per

share, and earnings per share. Furthermore, the inclusion of forecast earnings per

share in the fundamental valuation model subsumes the earnings per share variable

in seven of the nine countries studied.

Supplementation of fundamental models with our three technical measures sees all

technical factors being significant in explaining contemporaneous share prices in

the majority of the countries examined in the dissertation. Overall, testing confirms

the complementary nature of fundamental and technical analysis by showing that,

while each performs well in isolation, models integrating both have superior

explanatory power.

Table of Contents

CHAPTER ONE: INTRODUCTION 1

CHAPTER TWO: LITERATURE REVIEW 5

2.1 Introduction 52.2 Fundamental Analysis 52.3 Technical Analysis 72.4 Conclusion 10

CHAPTER THREE: METHODOLOGY 11

3.1 Introduction 113.2 The Fundamental Models 123.3 The Technical Model 143.4 The Hybrid Models 163.5 Evaluating the Relative Strength of the Models 173.6 Does the United States Dominate the Pooled Sample? 18

CHAPTER FOUR: DATA 19

4.1 Introduction 194.2 Dataset Construction 194.3 Filtering Procedure 234.4 Descriptive Statistics 24

CHAPTER FIVE: AUSTRALIAN RESULTS 27

5.1 Introduction 275.2 Fundamental Models 275.3 Technical Model 305.4 Hybrid Models 325.5 Evaluating the Relative Strength of the Models 3 35.6 Conclusion 36

CHAPTER SIX: UNITED STATES RESULTS 38

6.1 Introduction 386.2 Fundamental Models 386.3 Technical Model 416.4 Hybrid Models 436.5 Evaluating the Relative Strength of the Models 446.6 Conclusion 47

CHAPTER SEVEN: INTERNATIONAL RESULTS 48

7.1 Introduction 487.2 Fundamental Models 487.3 Technical Model 527.4 Hybrid Models 557.5 Evaluating the Relative Strength of the Models 597.6 Does the United States Dominate the Pooled Sample? 637.7 Conclusion 68

CHAPTER EIGHT: CONCLUSION 69

REFERENCES 71

APPENDIX A: COUNTRY DESCRIPTIVE STATISTICS 77

APPENDIX B: COUNTRY CORRELATION MATRICES 86

VI

List of Figures and Tables

Table 4.1: Variable Definition and Measurement 20

Figure 4.1: Timeline of Variable Construction 21

Table 4.2: Pooled Sample Descriptive Statistics 25

Table 4.3: Pooled Sample Correlation Matrix 26

Table 5.1: Australian Results of Fitting Fundamental Models 28

Table 5.2: Australian Results of F itting Models Including Technical Factors 31

Table 5.3: Australian Results of Likelihood Ratio Testing 35

Table 6.1: United States Results of F itting Fundamental Models 39

Table 6.2: United States Results of F itting Models Including Technical Factors 42

Table 6.3: United States Results of Likelihood Ratio Testing 46

Table 7.1: International Results of F itting Fundamental Model (1) 50

Table 7.2: International Results of F itting Fundamental Model (2) 51

Table 7.3: International Results of Fitting Technical Model (3) 53

Table 7.4: International Results of Fitting Hybrid Model (4) 57

Table 7.5: International Results of Fitting Hybrid Model (5) 58

Table 7.7: Pooled Sample Results of Fitting Fundamental Models Controlling

for the United States 64

Table 7.8: Pooled Sample Results of Fitting Models Including Technical Factors

Controlling for the United States 67

Table A. 1: Australia Descriptive Statistics 77

Table A.2: Canada Descriptive Statistics 78

Table A.3: France Descriptive Statistics 79

Table A.4: Germany Descriptive Statistics 80

Table A.5: Hong Kong Descriptive Statistics 81

Table A.6: Japan Descriptive Statistics 82

Table A.7: Singapore Descriptive Statistics 83

Table A.8: United K ingdom Descriptive Statistics 84

Table A.9: United States Descriptive Statistics 85

Table B.l: Australia Correlation Matrices 86

Table B.2: Canada Correlation Matrices 87

Table B.3: France Correlation Matrices 88

Table B.4: Germany Correlation Matrices 89

Table B.5: Hong Kong Correlation Matrices 90

Table B.6: Japan Correlation Matrices 91

Table B.7: Singapore Correlation Matrices 92

Table B.8: United Kingdom Correlation Matrices 93

Table B.9: United States Correlation Matrices 94

vii

Chapter One: Introduction

Identifying the factors important in explaining contemporaneous equity prices has

long been a focus of the valuation literature, with research divisible into the two

rich but largely distinct and often competing arms of fundamental (see, for example,

Holthausen and Watts, 2001) and technical analysis (see, for example, Lo and

MacKinlay, 1988 and 1999). While proponents of each type of analysis have

invariably agreed upon the general nature of factors important in explaining share

prices, identifying specific value relevant variables is a point of ongoing debate.

Despite voluminous evidence regarding the strength of fundamental and technical

factors in explaining equity prices, valuation models simultaneously incorporating

both types of measure are all but non-existent. In this context, we propose valuation

models that integrate aspects of both fundamental and technical analysis and, in

doing so, recognise their potential as complements rather than substitutes. More

specifically, we utilise an unconstrained version of Ohlson’s (1995) valuation

model, a model which investigates the value relevance of the book value of equity,

contemporaneous earnings and consensus earnings forecasts. We augment this

model with a suite of three technical factors, namely lagged price and two

momentum dummy variables to capture extreme past performance.

Specifically, these technical factors account for past price levels and changes in

these levels and, therefore, form a suite of technical measures. Our decision to

examine the importance of these factors in explaining contemporaneous price is

made with reference to other arms of the empirical literature. With respect to the

former, some research argues that the best predictor of current price is past price, or

that the share market exhibits weak form efficiency (see, for example, Fama, 1970).

1

If this is indeed the case, the best explanator of contemporaneous price is past price,

a fact leading to its inclusion in modelling undertaken in the current study.

However, more recent work, including the seminal paper of Jegadeesh and Titman

(1993), challenges market efficiency, showing predictability of returns or

momentum in the case of stocks exhibiting extreme performance. Specifically,

numerous studies have documented the profitability of buying an equally weighted

portfolio of stocks performing in the top decile over the prior three to twelve

months, simultaneously short-selling an equally weighted portfolio of stocks

performing in the lowest decile over the same period and holding the resultant

position for a further three to twelve months (see, for example, Jegadeesh and

Titman, 1993).

Whilst the profitability of such so-called momentum trading strategies represent a

deviation from the market efficiency, the existence of these opportunities are well

documented both through time (see, for example, Jegadeesh and Titman, 2001) and

in the context of different equity markets (see, for example, Liu et al, 1999;

Rouwenhorst, 1998 and 1999; and, Griffin et al, 2003). The momentum literature,

together with the work of researchers such as Cahart (1997) and Grundy and Martin

(2001), would suggest that past performance persistence is important in explaining

the cross-sectional variation in contemporaneous share prices and, in light of this,

we include it in modelling.

Empirically, to allow for the possibility that fundamental and technical analyses act

as substitutes rather than as complements, we commence our analysis by modelling

equity prices solely as a function of fundamental factors and, thereafter, consider

2

the ability of technical factors in isolation to explain price. Next, we fit our hybrid

models and, lastly consider the performance of our models relative to those

modelling price solely as a function of either fundamental or technical factors.

Our analysis begins with an examination of Australian listed companies, followed

by considering a dataset comprising companies from the US. Furthermore, to

provide international evidence, we investigate a comparative international study,

comprising Australian and US listed companies, and an additional seven, namely:

Canada; France; Germany; Hong Kong; Japan; Singapore; and the United Kingdom

(“UK”). The analysis for all countries is performed over the period January 1990 to

December 2004, inclusive.

Results of fitting fundamental models confirm that contemporaneous share price is

positively dependent on book value per share and earnings per share. Furthermore,

consistent with Dechow et al (1999) the inclusion of forecast earnings per share in

model fitting generally subsumes the information content in contemporaneous

earnings per share. Empirical findings pertaining to the technical model generally

confirm the importance of technical factors in equity valuation exercises.

Specifically, contemporaneous price is highly dependent on lagged price, and

companies exhibiting returns in the six month formation period that place them in

the top (bottom) performance decile continue to enjoy similar positive (negative)

performance in the subsequent six months.

Moreover, the results of testing our hybrid models not only confirm the importance

of both fundamental and technical analyses in explaining price, but also reveal the

superior explanatory power of these models relative to those considering either

3

fundamental or technical variables in isolation. This strength of our hybrid models

is best evidenced by their markedly higher (lower) adjusted/?2values (Akaike

Information Criterion, “AIC”, values) relative to models solely incorporating either

fundamental or technical measures, together with the highly significant results of

our likelihood ratio tests. Overall, we conclude there is strong evidence regarding

the complementary nature of fundamental and technical factors in equity valuation

exercises.

The remainder of this dissertation is structured as follows: Chapter Two provides a

review of the extant literature pertaining to fundamental and technical analysis;

Chapter Three outlines the methodology employed in assessing the ability of

fundamental and technical analysis to explain share prices both in isolation and in

combination; Chapter Four describes the characteristics of the three datasets, also

discussing the process employed in collecting them; Chapter Five presents and

discusses key results of testing in Australia; Chapter Six details the findings

pertaining to the US dataset; Chapter Seven provides the results of the international

comparative study; and, Chapter Eight concludes.

4

Chapter Two: Literature Review

2.1 Introduction

The valuation literature has invested considerable effort in identifying value

relevant variables. More specifically, extant research highlights the importance of

two broad types of variables, namely, fundamental and technical factors. However,

examination of this research reveals two interesting features: While proponents of

each type of analysis invariably agree upon the general nature of factors important

in explaining share prices, there lacks a general consensus on specific value relevant

factors; and, the research focuses on each factor in isolation, rather than in

combination.

With these characteristics in mind, Chapter Two commences by reviewing evidence

on the importance of fundamental factors in equity valuation exercises (Section

2.2). Thereafter, the chapter overviews research into the power of technical factors

in explaining contemporaneous share prices (Section 2.3).

2.2 Fundamental Analysis

Graham and Dodd (1934) are among the first to formally argue the importance of

fundamental factors in share valuation exercises. Subsequent studies further detail

the relationship between share price and fundamental factors, with Gordon and

Shapiro’s (1956) Dividend Discount Model not only becoming one of the most

widely cited models in modem finance theory, but also providing the foundation for

voluminous subsequent research.

5

In the context of this dissertation, the most notable extension of Gordon and

Shapiro’s (1956) work is provided by Ohlson (1995), who formulates a model

expressing price as a linear function of book value per share, earnings per share and

a vector of other value-relevant information. Subsequent research invests

considerable effort in empirically testing numerous variations of Ohlson’s (1995)

Residual Income Valuation Model, with early studies invariably lending support to

the (positive) dependence of equity values on both book value per share and

earnings per share (see, for example, Collins et al, 1997; and Ely and Waymire,

1999). These findings are consistent with the liquidation or adaptation value of the

firm’s assets (Berger et al, 1996) and their value in use (Barth et al, 1996),

respectively.

More recently, researchers have turned their focus to identifying variables forming

part of Ohlson’s (1995) vector of other value relevant information, with one stream

of the literature supplementing the aforementioned two-factor model to include

forecast earnings per share (see, for example, Dechow et al, 1999; and Morel,

2003). Results of this testing reveals that, while forecast earnings is significant and

positive in explaining price, its inclusion sees contemporaneous earnings ceasing to

be value relevant. Dechow et al (1999, p 26) suggest this result is not unexpected

as “analysts’ forecasts of next year’s earnings subsume value relevant information

in current earnings”.

In addition to exploring the importance of book values and current and forecast

earnings in explaining price, the literature also considers the value relevance of a

suite of other accounting variables (see, for example, Amir and Lev, 1996; and,

Amir et al, 1997, among others), with a comprehensive summary of these findings

6

provided by Holthausen and Watts (2001). While recent empirical research is yet to

reach a consensus regarding the identity of these “other” value relevant variables,

there seems little disagreement regarding the appropriateness of Ohlson’s (1995)

model as a foundation for these fundamental valuation exercises.

2.3 Technical Analysis

The ability of a variety of technical factors to explain share prices has long

fascinated practitioners and academics. Indeed, recognition of the potential for past

prices, and movements therein, to predict future equity values dates back to a series

of editorials published by Charles Dow in the Wall Street Journal between 1900 and

1902. The publication of these editorials prompted further research into the ability

of technical analysis to explain current and future share prices as well as equity

returns. One arm of this literature dismisses the random walk hypothesis, agreeing

upon the ability of past prices to forecast future returns (see, for example, Lo and

MacKinlay, 1988 and 1999).

Another arm of technical research tests the ability of various trading rules to

generate superior profits, with these studies providing support for the role of

technical analysis in predicting future share performance (see, for example, Brock

et al, 1992; and, Allen and Karjalianen, 1999). However, the reliability of these

results are called into question by research as early as that of Jensen and Bennington

(1970), who argue their potential to be explained by data-snooping biases.

Despite such criticisms, a technique that comprehensively accounts for data-

snooping biases is not employed in testing prior to the work of Sullivan et al (1999),

who apply White’s Reality Check bootstrap methodology to Brock et aVs (1992)

7

trading rules and dataset. Interestingly, the application of this technique on the same

dataset as Brock et ö/’s (1992) sees findings remain unchanged. However, when re

performing testing with a more recent dataset, Sullivan et al (1999) report that all

profits associated with Brock et al's (1992) trading rules disappear. Sullivan et al

(1999, p. 1684) argue that, whilst data-snooping biases may not explain the

historical profitability of trading based on technical analysis, such trading strategies

are no longer profitable given the increased efficiency of equity markets afforded by

“cheaper computing power, the lower transaction costs and increased liquidity”.

This argument is supported by Ready (2002), who documents the inability of either

Brock et ö/’s (1992) or Allen and Karjalianen’s (1999) trading rules to consistently

outperform a buy and hold strategy in recent times.

Yet another subset of the technical literature considers the profitability of

momentum strategies, which involve taking positions in portfolios constructed on

the basis of historical performance, and holding them for a pre-defined period.

While momentum research supports the profitability of buying a portfolio of past

“winners” and simultaneously short selling a portfolio of past “losers”, then holding

the resultant position for three to twelve months (see, for example, Jegadeesh and

Titman, 1993 and 2001), it has met with considerable scepticism given the

challenge it poses for the Efficient Market Hypothesis.

However, proponents of momentum provide evidence dismissive of these concerns,

which include data snooping and questions regarding the economic significance of

results. More specifically, this research reports that momentum profits are robust to

the introduction of transaction costs (see, for example, Korajczyk and Sadka, 2004)

as well as through time (see, for example, Grundy and Martin, 2001; and, Jegadeesh

8

and Titman, 2001) and across multiple equity markets (Rouwenhorst, 1998; Liu et

al, 1999; and, Griffin et al, 2003).

Further, evidence regarding the profitability of momentum trading strategies is not

confined to US equity markets, with profits also observed in the UK (see, for

example, Liu et al, 1999), Europe (see, for example, Rouwenhorst, 1998; Nijman et

al, 2002; and, Fomer and Marhuenda, 2003), and in a number of developing

markets (see, for example, Rouwenhorst, 1999; and, Griffin et al, 2003). However,

testing does not reach a consensus regarding the profitability of momentum trading

strategies in the context of Asian markets. Specifically, Hameed and Kusnadi

(2002) fail to find evidence of momentum profits in Hong Kong, Malaysia,

Singapore, South Korea, Taiwan, or Thailand. Similarly, Griffin et al (2003)

document the absence of significant momentum profits in fourteen Asian markets1.

Further, Chui et al (2000) report insignificant momentum profits in the eight Asian

markets they study.2 In contrast to these findings, Hum and Pavlov (2003), Demir et

al (2004), and, Marshall and Cahan (2005) all report the existence of significant

momentum profits in the Australian equity market.

Taking the preceding discussion as a whole, two types of technical analysis are

consistently documented as important in predicting prices and returns: Lagged

price; and, momentum. Indeed, their importance has already been recognised

outside the technical analysis literature. By way of example, the ability of

momentum to explain the cross-sectional variation in returns has already been

recognised by Carhart (1997), who reports its significance in explaining mutual

1 Griffin et al (2003) study 14 Asian countries: Australia; China; Hong Kong; India; Indonesia; Japan; Malaysia; New Zealand; Pakistan; Philippines; Singapore; South Korea; Taiwan; and, Thailand.2 Chui et al (2000) include eight Asian markets: Hong Kong, Indonesia, Japan, Korea, Malaysia, Singapore, Taiwan, and Thailand.

9

fund performance persistence when supplementing Fama and French’s (1993) three

factors to form a four-factor asset pricing model. Further, the complementary nature

of technical and fundamental analysis is identified by Taylor and Allen (1992), who

note that some 90% of foreign exchange market dealers rely upon both technical

and fundamental analysis.

2.4 Conclusion

Notwithstanding the preceding discussion of the literature which highlights the

importance of fundamental and technical analyses in explaining contemporaneous

share prices, models incorporating both classes of factors are all but non-existent.

As such, there is little evidence regarding whether these two sets of factors are

complementary.

To facilitate comparison with the extant literature, we commence our analysis by

modelling price as a function of either fundamental or technical factors in isolation.

Thereafter, we assess the complementary nature of fundamental and technical

analysis by augmenting an unconstrained version of Ohlson’s (1995) valuation

model with three technical factors, namely, lagged price and two momentum

dummy variables. Having fit these models, we then assess their strength in

explaining contemporaneous share prices relative to those employing either

fundamental or technical factors in isolation. The methodology we employ in

providing this evidence is overviewed in Chapter Three.

10

Chapter Three: Methodology

3.1 Introduction

Chapter Three details the methodological approach we employ to examine whether

fundamental and technical information act as substitutes or complements in equity

valuation exercises. We apply the methodology detailed in this chapter to three

datasets, specifically: One comprising listed Australian companies (Chapter Five);

One comprising US listed companies (Chapter Six); and, finally, An international

dataset comprising listed companies in Australia, the US and seven other countries,

namely, Canada, France, Germany, Hong Kong, Japan, Singapore, and the UK

(Chapter Seven). Our international analysis sees us tit models using data from each

of the nine countries in isolation as well as using a pooled dataset incorporating

observations from all countries. Further detail regarding these datasets, including

their construction, is provided in Chapter Four.

To ensure comparability with the extant literature and to assess the importance of

fundamental factors in explaining share prices in each of our datasets, we

commence with a discussion of the fundamental models we fit (Section 3.2).

Thereafter, we overview the model comprising solely technical factors which we fit

to test the importance of this class of information in equity valuation exercises

(Section 3.3). Next, we present the hybrid models which evaluate whether

fundamental and technical factors act as complements or substitutes in explaining

share prices (Section 3.4). Following this, we discuss the tests we utilise to assess

the relative explanatory power of fundamental, technical and hybrid models

(Section 3.5). Finally, we detail the robustness testing utilised in investigating

whether the US dominates the pooled sample (Section 3.6).

II

3.2 The Fundamental Models

We employ two models to examine the ability of fundamental factors to explain

equity prices. First, we fit a two-factor fundamental model similar to that of Collins

et al (1997), relating price to the book value per share and current earnings per

share. This model is formally presented as follows:3

Pt+l= a + ß ]BVPSt + ß2EPSt ( 1)

Where:

P,+i = the firm’s end-of-month share price in the month forecast

earnings for the coming fiscal year are announced;

BVPS, = the book value of the firm’s equity calculated as at the end

of the most recent fiscal year relative to month t\ and,

EPS, = the diluted earnings per share of the firm calculated at the

end of the most recent fiscal year relative to month t and

announced to the market in month t.

Previous testing of models similar to (1) reveals that price is highly positively

dependent on book value per share (see, for example, Collins et al, 1997; Dechow

et al, 1999; and, Ely and Waymire, 1999). Two reasons have been advanced for this

dependence, namely that book value represents the resources a firm has which can

be devoted to generate future earnings and also measuring the liquidation or

adaptation value of the firm’s assets (Berger, et al, 1996; and, Burgstahler and

Dichev, 1997, respectively).

3 To ensure our results are unbiased, we estimate regression coefficients in all testing using heteroscedasticity and autocorrelation (HAC) consistent standard errors. Specifically, HAC standard errors are calculated using the Newey-West (1987) adjustment.

12

As with book value per share, research documents current earnings per share as a

positive explanator of share price (see, for example, Easton, 1985; Collins et al,

1997; Dechow et al, 1999; and, Ely and Waymire, 1999). The main explanation

offered for this finding is that contemporaneous earnings per share serves as a proxy

for the current value of the firm, while book value per share represents the firm’s

exit value (see, for example, Barth et al, 1996).

More recent research supplements a model similar to (1) with forecast earnings per

share (see, for example, Dechow et al, 1999), arguing that it represents a proxy for

the other value-relevant information variable included in Ohlson’s (1995) model.

We test an unconstrained version of the resultant model, which is expressed as

follows:

Pl+[= a + ß{B VPSt + ß2EPSt + ß,FEPSl+l (2)

Where:

Pt+i = the firm’s end-of-month share price in the month forecast

BVPSt


= the book value of the firm’s equity calculated as at the end

EPSt

of the most recent fiscal year relative to month t\

= the diluted earnings per share of the firm calculated at the

FEPSt+1

end of the most recent fiscal year relative to month t and announced to the market in month t; and,

= the consensus forecast earnings per share for the firm, asforecast in the middle of the month following the release of actual earnings per share figures for the most recent fiscal year.

13

Research fitting models similar to (2) to US datasets yields an interesting result:

Whilst price exhibits the expected positive statistical dependence on both book

value per share and the consensus forecast earnings per share, current earnings per

share ceases to be a significant explanator given the presence of the aforementioned

independent variables. Dechow et al (1999) argue that such a result is consistent

with the consensus forecast earnings measure not only subsuming the information

contained in the current earnings figure, but also offering incremental information

about the future prospects of the company. Results o f fitting Models (1) and (2)

using Australian, US and international datasets are reported in Chapters Five, Six

and Seven, respectively.

3.3 The Technical Model

In providing evidence on the power of technical factors to explain contemporaneous

equity values, we model price as a function of past price and our momentum

measures. Our model is formally presented as follows:

Pt+\ —GC+ ß xPt_5 + ßi^Up "*■ ßi^Down (3)

Where:

Pt+i

Pt-5

Dup

the firm’s end-of-month share price in the month forecast


the firm’s end-of-month share price six months prior to

that denoted by Pt+I\

a dummy variable equal to 1 if the stock holding period

return in the six month period commencing one year prior

to the measurement of Pt+] placed it in the highest

performance decile, else 0; and,

14

Doown = a dummy variable equal to 1 if the stock holding period


to the measurement of Pt+J placed it in the lowest

performance decile, else 0.

Model (3) incorporates lagged price as an explanator given that the technical

literature agrees on its ability to forecast future returns (see, for example, Lo and

MacKinlay, 1988 and 1999)4 Similarly, momentum factors are included in light of

strong evidence suggesting performance persistence in equity markets (see, for

example, Jegadeesh and Titman, 1993) and the robustness of these findings to

critiques of data-snooping biases (see, for example, Jegadeesh and Titman, 2001;

and, Grundy and Martin, 2001) and economic insignificance (see, for example,

Korajczyk and Sadka, 2004).

The momentum factors incorporated in Model (3) are dummy variables capturing

extreme past return performance and are assigned based on the momentum measure

advanced by Jegadeesh and Titman (1993 and 2001). In constructing these

variables, we first calculate the buy and hold return on shares accruing over the six

month period commencing exactly one year from the time we model price, an

approach analogous to calculating Jegadeesh and Titman’s (1993 and 2001)

formation period return. Based on these returns, we rank shares and assign them to

performance deciles.

Shares included in the top (bottom) decile are allocated a D\jp (Doown) dummy equal

to one in order to reflect their extreme positive (negative) performance over the

4 A considerable body of literature provides evidence on the spuriousness of results obtained when regressing contemporaneous dependent variables on their lagged values in the context of time series studies (see, for example, Yule, 1926; and, Granger and Hyung, 2001). However, a subsection of this literature confirms this issue does not extend to research utilising cross-sectional data sets (see, for example, Ferson et al 2003a and 2003b), and hence, is not a concern in this dissertation.

15

period. Conversely, all shares in the remaining deciles are assigned momentum

dummies equal to zero. If performance does indeed persist over the ensuing six

months, a timeframe equivalent to Jegadeesh and Titman’s (1993 and 2001)

performance period, we expect to see D\jp (Doown) as a significantly positive

(negative) explanator of price when fitting Model (3). The findings pertaining to the

technical model are detailed in Section 5.3 (Australia), Section 6.3 (the US), and

Section 7.3 (the international comparative study).

3.4 The Hybrid Models

After fitting models of price as a function of either fundamental or technical factors,

we incorporate both sets of measures to generate our hybrid models. More

specifically, we supplement Models (1) and (2) with the suite of technical factors

included in (3), yielding Models (4) and (5), below:

PM = a+&BVPS, + ß2EPS,+ß1Pt_s + ßADUp + ß 5DDma (4)

P,tl = « + ßBVPS,+ ß2EPS, + ß}FEPS,t l+ ß,P,_,+ ß,DUp + ß,DDm (5)

W h e re :

Pt+i = the firm ’s end-of-m onth share p rice in the m onth forecast

BVPSt

earnings for the com ing fiscal year are announced;

= the book value o f the firm ’s equity calculated as at the end

EPSt

o f the m ost recent fiscal year relative to m onth t\

= the dilu ted earnings p er share o f the firm calculated at the

FEPSt+I

end o f the m ost recent fiscal year relative to m onth t and

announced to the m arket in m onth t\= the consensus forecast earnings per share for the firm , as

forecast in the m iddle o f the m onth follow ing the release

o f actual earnings p er share figures for the m ost recent

fiscal year.

16

P,_5 = the firm’s end-of-month share price six months prior to

that denoted by Pt+];

DUp = a dummy variable equal to 1 if the stock holding period


to the measurement of P,+i placed it in the highest

performance decile, else 0; and,

DDown = a dummy variable equal to 1 if the stock holding period


to the measurement of Pt+] placed it in the lowest

performance decile, else 0.

Results of fitting Models (4) and (5) using Australian, US and international datasets

are reported in Chapters Five, Six and Seven, respectively.

3.5 Evaluating the Relative Strength of the Models

In order to provide a meaningful comparison of the explanatory power of Models

(1) to (5) and, in doing so, draw inferences regarding the model best able to explain

contemporaneous share prices, we use three goodness of fit criterion, namely the

adjusted,/?2, AIC, and likelihood ratio tests. While a comparison of adjusted,/?2

values is meaningful given all models have the same dependent variable, namely,

contemporaneous price, AIC estimates are also utilised given their consideration of

entropy and ability to report the goodness o f fit for a particular model by trading off

the complexity of a model against how well it fits the data (see, for example,

Akaike, 1974).

Further, the use of likelihood ratio tests are ideal in this dissertation due to their

ability to compare two competing models where one is the nested version of the

other (see, for example, Felsenstein, 1981; Huelsenbeck and Crandall, 1997; and,

Huelsenbeck and Rannala, 1997). As such, these three measures provide the

17

necessary information to determine whether technical and fundamental information

act as substitutes or complements, with results using Australian, US and

international datasets reported in Chapters Five, Six and Seven, respectively.

3.6 Does the United States Dominate the Pooled Sample?

We merge the observations from the nine countries studied in this thesis to obtain a

pooled sample. Over 65% of the resultant pooled sample observations relate to US

listed companies.5 A concern arising from this is that the US may dominate the

aggregate results. As such, to control for the US, we include slope and interactive

dummy variables in Models (1) through (5).6 Specifically, D ^ n u s equals one for any

observation pertaining to non-US listed companies, or zero otherwise. In evaluating

these results, when D ^ onu s equals zero, we will obtain coefficient values that match

those observed in our US findings. Results of this testing is reported in Section 7.6.

5 Sample sizes relating to individual countries are contained in the descriptive statistics for each individual country in Appendix A.6 For brevity, we do not restate the Models. However, they are formally presented for the fundamental models in Tables 7.7 and the models containing technical factors in Table 7.8.

18

Chapter Four: Data

4 .1 1ntroduction

Chapter Four commences with an overview of the process employed to collect the

three datasets used to evaluate the complementary nature of fundamental and

technical information in equity markets (Section 4.2). Specifically, we first consider

a dataset comprising listed Australian companies, followed by a sample that solely

consists of US listed companies. Finally, the international dataset comprises listed

companies in Australia, the US and seven other countries, namely: Canada; France;

Germany; Hong Kong; Japan; Singapore; and, the UK.

Furthermore, in testing the international dataset, we consider each of the nine

countries in isolation as well as using a pooled dataset incorporating observations

from all countries. The datasets include all listed companies over the sample period

January 1990 through December 2004. Thereafter, the chapter provides a discussion

of the filters applied to the initial datasets (Section 4.3), together with descriptive

statistics for the final datasets utilised in testing (Section 4.4).

4.2 Dataset Construction

Initially, the three datasets employed in testing comprise the universe of companies

for which all necessary data is available. Details of the data, together with their

respective sources, are provided in Table 4.1, with a timeline for variable

construction provide in Figure 4.1.

19

Table 4.1: Variable Definition and MeasurementTable 4.1 includes the definitions of all variables employed in Models (1) to (5). More specifically, the table details the manner in which variables are calculated, as well as providing information on the source of variable constituents.

V ariable D efinition U nited S ta tesO ther

C ountries

P<+,

T he firm ’s end-of-m onth share p rice in the m onth forecast earnings for the com ing fiscal year are announced. This share p rice is adjusted for cap ita lisation changes.

C R SPD ataStreamInternational

Pt-5

T he firm ’s end-of-m onth share p rice six m onths p rior to that denoted by P t+]. T his share price is ad justed for cap ita lisation changes.


BVPS,T he book value per share o f the firm calculated as at the end o f the m ost recent fiscal year relative to m onth t.

C O M PU ST A TIndustrialA nnual7

D ataStreamInternational

EPS,

T he earnings per share o f the firm calcu lated at the end o f the m ost recent fiscal year relative to m onth t and announced to the m arket in m onth t.

C O M PU ST A TIndustrialA nnual8

D ataStreamInternational

F E P St+i

T he consensus forecast earnings per share for the firm, as forecast in the m onth follow ing the release o f actual earnings per share figures for the m ost recent fiscal year. Forecast earnings are adjusted for capita lisation changes and are announced in the m iddle o f the m onth, though the exact date varies slightly.

I/B /E /S I/B /E/S

Dup

A dum m y variab le equal to 1 i f the stock holding period return in the six m onth period com m encing one year p rio r to the m easurem ent o f P t+I p laced it in the h ighest perform ance decile, else 0.


P Down

A dum m y variable equal to 1 i f the stock holding period return in the six m onth period com m encing one year p rio r to the m easurem ent o f P,+i p laced it in the low est perform ance decile, else 0.

C RSPD ataStreamInternational

7 The book value of the firm’s equity (data60) scaled by shares outstanding (data25) and subsequently adjusted for capitalisation changes (data27).8 The diluted earnings per share of the firm (data57) adjusted for capitalisation changes (data27).

20

Examination of Table 4.1 reveals that, for some countries, data sources vary. More

specifically, share price and returns data for the US is obtained from the Center for

Research and Security Prices (“CRSP”) files, with share prices and returns for the

other countries collected from DataStream International. In addition, accounting

variables for the US are sourced from Compustat Industrial Annual files, with

information for the other countries obtained from DataStream International. Further,

for all countries in the sample, we acquire consensus earnings per share forecasts

from I/B/E/S.

Initially, as depicted graphically in Figure 4.1, we utilise return information for the

entire universe of companies in each country to calculate the momentum dummies

in the manner described in Section 3.3. In addition, we obtain the date in which the

accounting information is released to the market from I/B/E/S. Specifically, we

ascertain both book value per share and (diluted) current earnings per share

measures relating to the most recently ended fiscal year, and merge these values

together using unique company identifiers.

Thereafter, we collect consensus analysts’ earnings per share forecasts in the month

following the release of the accounting information.9 To ensure the comparability of

the forecast figures obtained from I/B/E/S with the reported (diluted) earnings

figures obtained from Compustat and DataStream International, before proceeding

further, we convert all forecast figures reported on a primary basis into diluted

equivalents. In undertaking this exercise, we exclude any observation for which the

basis of reporting forecast earnings figures cannot be ascertained.

9 This ensures that the analysts’ forecasts incorporate the released accounting information.

22

Finally, with respect to the dependent price variable incorporated in modelling, as

forecast earnings figures are invariably released in the middle of any given month,

to ensure the market has had opportunity to impound this information, we take

prices at the end of the same month. This matching approach is similar to that

employed by previous research including that of Dechow et al (1999). We also

obtain the end of month price six months prior to the aforementioned share price for

modelling technical information. Lastly, after merging these end of month share

prices based on the unique company identifier, we remove any incomplete

observation from the resultant dataset.10

4.3 Filtering Procedure

After merging the aforementioned datasets, we apply several filters to the resultant

sample. Specifically, consistent with prior work including that of Collins et al

(1997) and Morel (2003), we remove from the sample any companies with book

values per share equal to or less than zero.* 11 Further, consistent with each individual

country’s reporting requirements, we exclude firms who do not disclose their annual

financial information to the market within the required time of the fiscal year end.12

Finally, we perform diagnostic tests to identify any influential data points. These

points are removed from the dataset, yielding a final pooled cross-sectional sample

of 51,689 firm-year observations. A breakdown of observations for each country is

provided in the descriptive statistics tables in Appendix A.

10 The removal of incomplete observations results in the exclusion of firms that are not followed by analysts. An obvious extension to this study would be to re-perform testing on a dataset including these firms. However, for consistency across testing, we only consider observations that contain all necessary variables.11 Collins et al (1997) and Morel (2003) remove such observations, given they are not economically rational.12 This results in only 46 observations being removed from the sample. Re-performing testing including these observations reveals that their exclusion has no significant impact on the results reported.

23

4.4 Descriptive Statistics

Descriptive statistics and correlation coefficients calculated for the pooled

international dataset are presented in Tables 4.2 and 4.3, respectively. The statistics

calculated in respect of each individual country are presented in Appendices A and

B, respectively.13

From Table 4.2 it is evident that the sample companies are representative of the

market as a whole, being drawn from the entire size gamut. Further, Table 4.3 (and

Appendix B for individual country correlation matrices) reveals nothing of great

concern with respect to multicollinearity. While the correlation between Pt+l and

Pt_5 is 0.84, this value is unsurprising given that these variables represent the price

of a company six months apart. To allay any non-stationarity concerns in relation to

price, we perform an Augmented Dickey-Fuller test, which confirms that price is

indeed stationary.

13 The international comparative study necessitates the reporting of all variables for each country to be expressed in one common currency. As such, we collect DataStream International exchange rates to convert all variables into US dollars.

24

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Chapter Five: Australian Results

5.1 Introduction

Chapter Five presents the results of fitting the fundamental, technical and hybrid

models to our Australian dataset, and commences with a discussion of the

significance of variables included in the fundamental models (Section 5.2).

Thereafter, the chapter reports findings relating to the importance of technical

factors in explaining contemporaneous equity prices of listed Australian companies

(Section 5.3). Next, preliminary evidence regarding the complementary nature of

fundamental and technical factors in equity valuation exercises is provided via a

discussion of the results of fitting our hybrid models (Section 5.4). Finally, the

chapter concludes with a comparison of the explanatory power of our hybrid models

relative to those incorporating solely fundamental or technical factors, providing

further evidence that models incorporating both sets of factors are more powerful

than those including either class in isolation (Section 5.5).

5.2 Fundamental Models

To provide Australian evidence on the relationship between price and fundamental

factors, and to allow comparison of our findings with those reported in the extant

literature, we fit Models (1) and (2), with results presented in Table 5.1.

27

Table 5.1: Australian Results of Fitting Fundamental ModelsTable 5.1 presents the results in Australian dollars of fitting Models (1) and (2), below, with heteroscedasticity and autocorrelation consistent t-statistics provided in parentheses.

Pl+l = a + ß lBVPSl + ß 2EPSt (1)Pt+l= a + ßxBVPSt + ß 2EPSt + ß3FEPSt+l (2)

Notation employed in this table is as follows: Pt+i is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPSt+] is the consensus forecast earnings per share for the firm, as forecast in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though the exact date varies slightly.

(1) (2)

Intercept 0.5104 0.2137

(2 .9773***) (1.9023)

BVPS, 1.7070 1.1736

(15.9036***) (4 .9231***)

EPS, 1.3940 0.7747

(2 .0205**) (1.2020)

FEPSlTl 5.5981(3.1112***)

Sample 1,772 1,772

Adjusted RJ 0.55 0.62

Akaike Info Criterion 5.2258 5.0663

F-Statistic 1,090*** 955.7***

Log Likelihood -4,627 -4,485

**Denotes significance at the 5% level; and, *** Denotes significance at the 1% level.

28

The results pertaining to the fitting of Model (1) confirm that price is highly

positively dependent on both book value per share and earnings per share. The

significance of book value per share in explaining price is consistent with the clean

surplus framework proposed by Ohlson (1995); also providing further evidence that

book value per share represents the liquidation or adaptation value of the firm’s

assets (Burgstahler and Dichev, 1997). The positive statistical dependence of price

on current earnings per share supports the argument that contemporaneous earnings

per share is a proxy for the current value of the firm (Barth et al, 1996). Further, this

positive dependence of price on both book value per share and earnings per share is

consistent with the findings from the extant valuation literature (see, for example,

Collins et al, 1997). Overall, Model (1) is highly significant, with a F-statistic of

1,090 and an adjusted R2 of 55%.

The results of fitting Model (2) provide further evidence of price being positively

statistically dependent on book value per share. Further, the model reveals a

significant positive relationship between forecast earnings per share and share price.

It is interesting to note, however, that the inclusion of forecast earnings per share in

the model sees contemporaneous earnings per share become an insignificant

explanator of share price. This finding is consistent with prior studies and supports

the argument that forecast earnings per share subsume current earnings figures as

well as offering incremental information about the future value of the firm (Dechow

et al, 1999). Again, as with Model (1), Model (2) is highly significant, with a F-

statistic of 955, and an adjusted R2 of 62%.

29

5.3 Technical Model

To provide evidence on the power of models solely comprising technical factors to

explain price, we fit Model (3). Results of this modelling, presented in Table 5.2,

reveal that all technical measures are highly significant in explaining

contemporaneous share prices, and are significant in the predicted directions.

Specifically, consistent with the technical trading literature (see, for example, Lo

and MacKinlay, 1988 and 1999), contemporaneous share prices exhibit a positive

dependence on lagged price. Further, shares exhibiting returns in the six month

formation period that place them in the top (bottom) performance decile continue to

enjoy similar positive (negative) performance in the subsequent six months. This

persistence results in systematically higher (lower) prices for these particular firms

at the time we model price, namely at the conclusion of the twelve-month period,

and is consistent with the performance persistence documented by the momentum

literature (see, for example, Jegadeesh and Titman, 1993 and 2001). Overall, the

model is highly significant, with a F-statistic of 2,125 and an adjusted R2 of 78%.

30

Table 5.2: Australian Results of Fitting Models Including Technical FactorsTable 5.2 presents the results in Australian dollars of fitting Models (3) through (5), below, with heteroscedasticity and autocorrelation consistent t-statistics provided in parentheses.

Pt+l= a + ß x Pt_ 5 + ß2DUp + ß^DDown (3)

A , = « + ßßVPS, + ß-fiPS, + ß,P,_5 + ß4DUp + ß ß Dm (4)

PM =oc+ ß,BVPS, + ß 2EPS,+ ß,FEPS,t i + (5)

Notation employed in this table is as follows: P,+i is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; Pt.5 is the firm’s end-of-month share price six months prior to that denoted by P,+i. This share price is adjusted for capitalisation changes; DUp is a dummy variable equal to 1 if the stock performed in the top decile in the six month period commencing one year prior to the measurement of Pt+I, else 0; DDown is a dummy variable equal to 1 if the stock performed in the lowest decile in the six month period commencing one year prior to the measurement of Pt+i, else 0; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPS,+] is the consensus forecast earnings per share for the firm, as forecast in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though the exact date varies slightly.

(3) (4) (5)

Intercept 0.9744 0.1833 0.0599(5.4759***) (2.1613**) (0.8182)

BVPSt 0.6314 0.4656(5.2364***) (3.4207***)

EPS, 0.4737 0.2662(1.6343) (0.9342)

FEPSt+I 2.5083

(2.0625**)

Pt-5 0.7815 0.6174 0.5757(17.9774***) (9.4853***) (8.5304***)

D<jp 1.2722 1.5962 1.5667

(4.3879***) (5.5882***) (4.6510***)

D D ow n -2.3515 -1.3996 -1.2396

(-4.7107***) (-3.9728***) (-3.7863***)

Sample 1,772 1 ,772 1 ,772

Adjusted R2 0 .7 8 0 .82 0 .83

Akaike Info Criterion 4 .5 0 2 6 4 .3 1 0 7 4 .2 4 1 3

F-Statistic 2 ,1 2 5 * * * 1 ,6 2 3 * * * 1 ,4 7 2 * * *

Log Likelihood -3 ,9 8 5 -3 ,8 1 3 -3 ,751


31

5.4 Hybrid Models

The results of fitting our hybrid models, Models (4) and (5), are presented in Table

5.2. Findings pertaining to Model (4) provide further evidence of the importance of

book value per share in explaining contemporaneous share price. This positive

dependence is consistent with the extant literature which argues that book value

represents the liquidation or adaptation value of the firm’s assets (Berger et al,

1996). However, including the technical measures sees contemporaneous earnings

become an insignificant explanator of share price, suggesting that our technical

factors subsume value relevant information contained in current earnings.

In regards to the technical factors, consistent with Lo and MacKinlay (1988 and

1999), the findings of Model (4) again confirm the positive dependence of past

share price on contemporaneous price. In addition, both momentum dummies are

highly significant in explaining price, and are significant in the expected direction.

Moreover, shares exhibiting past return performance continue to experience similar

performance in the subsequent six months. This positive (negative) persistence

results in higher (lower) prices for the top (bottom) performers at the time we model

price, at the end of the twelve-month testing period. This finding is consistent with

the momentum literature (see, for example, Jegadeesh and Titman, 1993 and 2001).

Further, the inclusion of the three technical measures sees a marked improvement in

the explanatory power of the valuation model. Specifically, Model (4) is highly

significant, with a F-statistic of 1,623 and an adjusted R2 of 82%. This dramatic

increase in R2 from 55% in Model (1) to 82% in Model (4) provides preliminary

evidence on the complementary nature of fundamental and technical factors in

equity valuation exercises.

32

Our findings are further verified by fitting Model (5), which confirms the results of

testing Model (2) and provides additional support for the incremental importance of

technical information in valuation models. Results further highlight the significance

of both book value per share and forecast earnings per share in explaining

contemporaneous share price, but sees current earnings per share remain

insignificant.

Similar to Model (4), the technical factors are all highly significant, with the

coefficients on the dummy variables being in the expected direction. Overall, Model

(5) is highly significant with a F-statistic of 1,472 and has an adjusted R2 of 83%. It

is also evident that the inclusion of the technical factors has seen a substantial

increase in the model’s/?2, from 62% in Model (2) to 83% in Model (5). This

increase in explanatory power is further evidence of the complementary nature of

fundamental and technical analysis.


To compare the power of the models overall, we initially compare their adjusted R2

values: As all models have the same dependent variable, comparing the adjusted

R2 values is meaningful. However, to further confirm our findings, we also examine

the models’ AIC estimates, which we argue are more robust in light of their ability

to account for entropy as well as a given model’s fit. It is evident from the values of

the adjusted/?2 and AIC that Models (1) through (5) are of increasingly good fit.

Specifically, the R2 values increase from 55% in Model (1) to 83% in Model (5).

This marked increase in the power of the overall models is further confirmed by the

33

examination of AIC values, which decreases considerably through Models (1) to (5)

from 5.2258 in Model (1) to 4.2413 in Model (5).

Despite the above findings, the important question, not answered by examining

R2 or AIC values, is whether the fitting of a hybrid model, one that includes both

fundamental and technical measures, sees a statistically significant improvement in

the ability to explain contemporaneous share prices relative to models only

comprising fundamental or technical factors. We resolve this issue by calculating

likelihood ratios, which are presented in Table 5.3. Overall, in comparing these

ratios we confirm that the hybrid models provide a statistically significant increase

in explanatory power relative to fundamental or technical models. These results

provide more definitive evidence of the complementary nature of fundamental and

technical information, as models incorporating both sets of information are better

able to explain contemporaneous share prices that those considering either in

isolation.

34

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5.6 Conclusion

Chapter Five presents the results of fitting the fundamental, technical and hybrid

models to a dataset of listed Australian companies and provides strong evidence of

the complementary nature of fundamental and technical measures in explaining

contemporaneous price. Initially, to ensure comparability of our study with extant

literature, the chapter considers the ability of models solely comprising fundamental

factors. Results are consistent with previous studies, revealing the positive

dependence of price on book value per share and earnings per share (see, for

example, Collins et al, 1997). Further, consistent with Dechow et al (1999), the

inclusion of forecast earnings into model fitting sees earnings per share ceasing to

significant in explaining share prices.

Thereafter, the chapter evaluates the strength of a model that only incorporates

technical factors in explaining price. Results of this testing confirms the positive

dependence of past share price on contemporaneous price, a finding consistent with

Lo and Mackinlay (1988 and 1999). Further, consistent with the momentum

literature, testing reveals the significance of both momentum dummies in explaining

price (see, for example, Jegadeesh and Titman, 1993 and 2001).

Next we consider the ability of our hybrid models to explain contemporaneous

share prices and, in doing so, provide the first evidence of the complementary

nature of fundamental and technical factors in equity valuation exercises. More

specifically, we find that augmenting fundamental models with our suite of

technical factors sees a marked increase (decrease) in adjusted R2 values (AIC

values), with the likelihood ratios confirming the significance of these changes.

Overall, these results confirm that fundamental and technical factors act as

36

complements rather than substitutes in equity valuation exercises in an Australian

context.

37

Chapter Six: United States Results

6.1 Introduction

Chapter Six presents the results pertaining to our US dataset. We commence by

considering models examining fundamental factors in isolation (Section 6.2).

Following this, we evaluate the significance of technical factors in explaining

contemporaneous share prices of US listed companies (Section 6.3). Subsequently,

we examine the complementary nature of fundamental and technical factors in

equity valuation exercises by considering our hybrid models (Section 6.4). Lastly,

to provide further evidence on the complementary nature of fundamental and

technical analysis we compare the explanatory power of our hybrid models with

those models only considering either type of factors in isolation (Section 6.5).


Prior to considering whether fundamental and technical analyses complement one

another in the context of equity valuation exercises, we examine the explanatory

power of each type of analysis in isolation. We commence by discussing the results

of fitting Models (1) and (2) in the US, which explain price solely as a function of

fundamental factors. These results are formally presented in Table 6.1.

38

Table 6.1: United States Results of Fitting Fundamental ModelsTable 6.1 presents the results in US dollars of fitting Models (1) and (2), below, with heteroscedasticity and autocorrelation consistent t-statistics provided in parentheses.

Pt+x= a + ß xBVPSt + ß2EPSt (l)

Pt+X= a + ßxBVPSt + ß2EPSt + ß,FEPSt+x (2)Notation employed in this table is as follows: P,+] is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPS,+i is the consensus forecast earnings per share for the firm, as forecasted in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though the exact date varies slightly.

(1) (2)

Intercept 8.26 7.37(34.85***) (34.94***)

BVPS, 0.91 0.51(24.07***) (16.69***)

EPS, 1.54 0.17(4.91***) (1.83)

FEPS,+1 4.94(17.78***)

Sample 33,028 33,028

Adjusted R2 0.35 0.42


F-Statistic 8,820*** 8,095***



39

With respect to Model (1), testing reveals that price is highly positively dependent

on book value per share, a finding consistent with the clean surplus valuation

framework advanced by Ohslon (1995), the liquidity and adaptation value of assets

argument and the results of prior empirical testing (see, for example, Collins et al,

1997; Dechow et al, 1999; and, Ely and Waymire, 1999). Testing also reveals that

price exhibits a highly positive statistical dependence on current earnings per share.

Again, this finding is consistent with the extant literature (see, for example, Easton,

1985; Collins et al, 1997; Dechow et al, 1999; and, Ely and Waymire, 1999) and

the argument that earnings per share serves as a proxy of the firm’s value in use.

Furthermore, the findings pertaining to Model (1) in the US are consistent with the

Australian results in Section 5.2. Overall, the model is highly significant with aF-

statistic of 8,820 and an adjusted R2 of 35%.

The results of fitting Model (2) differ somewhat from those pertaining to Model (1).

Specifically, while the inclusion of consensus forecast earnings per share does not

alter findings with respect to book value, its introduction sees contemporaneous

earnings become an insignificant explanator of share price. Instead, the forecast

earnings measure itself is revealed as a significant and positive explanator of price.

These findings are consistent with Dechow et al (1999), who argue that forecast

earnings per share not only subsumes current earnings figures, but also offers

incremental information about the ongoing value of the firm.

Notwithstanding these differences, Model (2) is highly significant in explaining

equity prices, with a F-statistic of 8,095 and an adjusted R2 of 42%. Further, our

Model (2) findings for US companies is consistent with the results of empirical

testing performed on the Australian dataset in Section 5.2. Specifically, consistent

40

with Dechow et al (1999), both datasets highlight that earnings forecasts contain

important information about the future prospects of the firm.

6.3 Technical Model

Next, in considering the ability of technical analysis to explain contemporaneous

price in the US, we examine the results of fitting Model (3), which are presented in

Table 6.2. Results show that, consistent with the Australian dataset (Section 5.3),

all technical factors are highly significant in explaining contemporaneous price and

are significant in the predicted directions.

Specifically, not only do contemporaneous prices exhibits a positive dependence on

lagged prices, shares exhibiting returns in the six month formation period that place

them in the top (bottom) performance decile continue to enjoy similar positive

(negative) performance in the subsequent six months. This persistence results in

systematically higher (lower) prices for these particular firms at the time we model

price, namely at the conclusion of the twelve-month period, and is consistent with

the performance persistence documented by the momentum literature (see, for

example, Jegadeesh and Titman, 1993; and, Jegadeesh and Titman, 2001).

Moreover, the overall model is highly significant with a F-statistic of 33,400 and an

adjusted R2 of 75%. Interestingly, results suggest that technical analysis has a

greater ability to explain equity values in isolation than fundamental analysis.

41

Table 6.2: United States Results of Fitting Models Including Technical FactorsTable 6.2 presents the results in US dollars of fitting Models (3) through (5), below, with heteroscedasticity and autocorrelation consistent t-statistics provided in parentheses.

Pt+l = a + ß\Pt_ 5 + ß2DUp + ß,DDown (3)

PM = a+ßlBVPS, + ßlEPSl +ß,P,.,+ßtDl¥+ßsDam. (4)

P,t l =a + ß,BVPS, + ß2EPS, + ß.FEPS^ + ßj>,_% + ßsDUp + ß,PDm (5)

Notation employed in this table is as follows: P,+1 is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; P,.5 is the firm’s end-of-month share price six months prior to that denoted by P,+i. This share price is adjusted for capitalisation changes; DUp is a dummy variable equal to 1 if the stock performed in the top decile in the six month period commencing one year prior to the measurement of Pt+], else 0; DDon7l is a dummy variable equal to 1 if the stock performed in the lowest decile in the six month period commencing one year prior to the measurement of P[+1, else 0; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPS,+i is the consensus forecast earnings per share for the firm, as forecasted in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though the exact date varies slightly.

(3 ) (4 ) (5 )

Intercept2.11

( 11.74***)1.68

( 10.59***)1.65

( 11.51***)

BVPS, 0.13(9 .06***)

0.05(3 .97***)

EPS, 0.51(4 .50***)

0.16(2 .84***)

FEPS,+j1.42

(9.23***)

P t-50.90 0.83 0.80

(71.27***) (49.44***) (44.07***)

r \ 0.91 1.43 1.45E>up (3 29***) (5.32***) (5.61***)

-1.28 -0.65 -0.45‘- - 'D o w n (-8.30***) (-3.88***) (-3.37***)

Sample 33,028 33,028 33,028

Adjusted R2 0.75 0.76 0.77

Akaike Info Criterion 6.66 6.62 6.60

F-Statistic 33,400*** 20,960*** 18.020***

Log Likelihood -109,922 -109,361 -108,970


42

6.4 Hybrid Models

Whilst the preceding discussion provides US evidence of the explanatory power of

both fundamental and technical analysis in isolation, it says nothing about whether

they act as complements in equity valuation exercises. We provide evidence on this

by fitting Models (4) and (5), with results of this testing provided in Table 6.2.

With respect to the former, results reveal the significance of both types of analysis

in explaining share price. More specifically, consistent with the findings in relation

to Model (1) and the extant literature (see, for example, Collins et al, 1997; and, Ely

and Waymire, 1999), book value per share and earnings per share are significant

positive explanators of contemporaneous share price.

Further, consistent with Model (3), testing reveals the importance of technical

analysis even in the presence of fundamental factors, with lagged price and both

momentum dummies remaining significant in explaining contemporaneous price.

These Model (4) findings for the US coincide with the Australian results (Section

5.4), showing the importance of both fundamental and technical measures as

explanators of share price. Additionally, Model (4) is highly significant with a F-

statistic of 20,960, and has an adjusted R2 of 76%.

As with Model (4), the results of fitting Model (5) lend support to the

complementary relationship between fundamental and technical analysis,

confirming the significance of each type of measure even given the presence of the

other. Interestingly, in the context of our hybrid model, the inclusion of the forecast

earnings per share does not detract from the significance of the contemporaneous

earnings measure in explaining price. This finding is at odds with that of Dechow et

43

al (1999), who report that forecast earnings per share subsume the information

contained in the current earnings measure.

Furthermore, the US results differ to that of the Australian findings for Model (5)

(Section 5.4). Specifically, the Australian findings concur with those of Dechow et

al (1999), with earnings per share insignificant with the inclusion of forecast

earnings per share in model fitting. Despite this point of difference, Model (5) is

highly statistically significant with a F-statistic of 18,020 and an adjusted R2 of

77%.


To more comprehensively evaluate the relative explanatory power of Models (1) to

(5), we augment the ensuing analysis of adjusted R2 measures with a consideration

of A1C values, with both measures included in Tables 6.1 and 6.2. We do this as,

even though the response variable in all models is identical, and therefore a

comparison of their R2 values is meaningful, this goodness-of-fit measure is

deficient insofar as it fails to adequately consider entropy as well as a model’s fit.

Consequently, we also undertake a comparison of models’ AIC estimates, which

have the added benefit of greater suitability in large samples. Examination of R2

and AIC values reveals that Models (1) through (5) are of increasingly good fit, as

evidenced by a marked increase in the former and decrease in the latter. Moreover,

the inclusion of both fundamental and technical analyses in valuation models sees

an increase in R2 measures relative to Models (1) to (3), and a corresponding drop

in AIC values.

44

Despite the preceding discussion, the critical question is whether fitting a hybrid

model sees a statistically significant improvement in the ability to explain

contemporaneous price relative to fitting models comprising either fundamental or

technical factors in isolation. An answer is provided via consideration of the

likelihood ratios reported in Table 6.3.

A comparison of these ratios confirms that hybrid models provide a statistically

significant increase in explanatory power relative to fundamental or technical

models. In further robustness testing, we rerun the regressions outlined in Table 6.3,

using change in price as the dependent variable (see, for example, Beaver et al,

1980; and, Barth et al, 1990). Inferences regarding the complementary nature of

fundamental and technical analysis remain unchanged, although the explanatory

power of the resultant models is markedly lower.

Taken as a whole, our findings not only reveal the complementary nature of

fundamental and technical information, but serve to highlight the benefits of

including both analyses in equity valuation exercises. These findings in the US are

consistent with the Australian results in Section 5.5, highlighting the superiority of

hybrid valuation models.

45

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6.6 Conclusion

This chapter commences by considering the ability of models fitting fundamental

and technical factors in isolation. Results of fitting fundamental models, consistent

with the extant literature (see, for example, Collins et al, 1997; and, Ely and

Waymire, 1999), reveals the positive dependence of price on book value per share

and earnings per share. Furthermore, in agreement with Dechow et al (1999) the

inclusion of forecast earnings per share in model fitting sees earnings per share

become insignificant in explaining contemporaneous price. Subsequently, we

consider a model that consists solely of technical factors, with results highlighting

contemporaneous share prices dependence on lagged price, and the two momentum

dummy variables.

Consistent with the Australian results, testing within the US further confirms the

complementary nature of fundamental and technical analysis by showing that, while

each performs well in isolation, models integrating both have superior explanatory

power: The integration of both analyses in equity valuation models sees

considerable increases in adjusted R2 values and marked drops in corresponding

AIC figures, with the significance of our results further verified by the highly

significant results of likelihood ratio testing.

47

Chapter Seven: International Results

7.1 Introduction

The previous two chapters examine the complementary nature of fundamental and

technical analysis on Australian (Chapter Five) and US (Chapter Six) datasets. In

this chapter, to provide international evidence, we extend this analysis by

considering nine countries, namely: Australia; Canada; France; Germany; Hong

Kong; Japan; Singapore; the UK; and, the US. Specifically, we examine these

countries on an individual basis, as well as in aggregate.

Moreover, to allow for the possibility that fundamental and technical analyses are

not complementary, we commence by modelling price solely as a function of

fundamental factors (Section 7.2). Thereafter, we consider the ability of technical

factors to explain contemporaneous share prices (Section 7.3). Next, we fit our

hybrid models (Section 7.4) and, consider the performance of these models relative

to those modelling price solely as a function of either fundamental or technical

factors (Section 7.5). Lastly, we re-perform testing on the pooled sample with the

inclusion of dummy variables to identify differences between the US and other

countries (Section 7.6).


Before evaluating the complementary nature of fundamental and technical analysis

in valuation exercises, we examine the explanatory power of each type of analysis

in isolation. Specifically, we commence by assessing the results of the two

fundamental models, namely, Models (1) and (2). Table 7.1 and Table 7.2 report the

48

results for Models (1) and (2), respectively, for the individual countries and the

pooled sample.

In regards to Model (1), testing reveals that across all nine countries, price is highly

positively dependent on book value per share. This finding is consistent with the

clean surplus valuation framework advanced by Ohslon (1995), the liquidity and

adaptation value of assets argument, and the results of prior empirical testing (see,

for example, Joos and Lang, 1994; Collins et al, 1997; Dechow et al, 1999; Ely and

Waymire, 1999; and, Ota, 2002). Further, results for all countries except Hong

Kong find that price is positively dependent on current earnings per share. Again,

this finding is consistent with the extant literature (see, for example, Easton, 1985;

Joos and Lang, 1994; Collins et al, 1997; Dechow et al, 1999; Ely and Waymire,

1999; and, Ota, 2002) and the argument that earnings per share serves as a proxy of

the firm’s value in use.

Furthermore, the individual country findings are consistent with the results obtained

from the pooled sample. Overall, all results for Model (1) are highly significant

with F-statistics ranging from 447 (Germany) to 8,820 (US), and 37,220 for the

pooled sample. In addition, the countries have adjusted/?2 values ranging from 35%

(US) to 62% (Hong Kong), with the pooled sample reporting an adjusted/?2 value of

59%.

49

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The results pertaining to Model (2) provide further evidence of price being

positively statistically dependent on book value per share across all countries.

However, while the inclusion of consensus forecast earnings per share does not alter

findings with respect to book value, its introduction sees contemporaneous earnings

become an insignificant explanator of share price in six of the nine countries in the

study, as well as the aggregate pooled sample. Rather, forecast earnings are a

significant and positive explanator of price. This finding is consistent with prior

studies and supports the argument that forecast earnings per share subsume current

earnings figures, as well as offering incremental information about the future value

of the firm (Dechow et al, 1999).

Conversely, the results of Model (2) for Canada and Singapore fail to find a

significant relationship between forecast earnings and share price. Instead, both

countries continue to document a significant positive relationship between current

earnings per share and contemporaneous price. Notwithstanding these differences,

Model (2) is highly significant in explaining equity prices across all countries, with

F-statistics ranging from 442 (Singapore) to 8,095 (US), and 42,460 for the pooled

sample. Further, the adjusted R2 values range from 42% (US) to 82% (Hong Kong),

with the pooled sample having an adjusted R2 of 71%.

7.3 Technical Model

Following the above examination on the relationship between fundamental analysis

and price (Section 7.2), we also fit a model where contemporaneous share price is

solely a function of technical factors, namely Model (3). The results pertaining to

the fitting of Model (3) are presented in Table 7.3.

52

Tab

le 7

.3:

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rnat

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Results show that across seven of the nine countries, as well as the pooled sample,

all technical factors are highly significant in explaining contemporaneous price and

are significant in the predicted directions. However, in two countries, namely, Japan

and Singapore, shares do not exhibit performance persistence, with the dummy

variables failing to be significant in model fitting. This finding is consistent with

Griffin et al (2003), who fail to find evidence of momentum in either Japan or

Singapore.

Specifically, for all countries and the pooled sample, consistent with Lo and

MacKinlay (1988 and 1999), we see that contemporaneous prices exhibit a positive

dependence on lagged prices. In addition, we see that for the majority of countries,

shares exhibiting returns in the six month formation period that place them in the

top (bottom) performance decile continue to enjoy similar positive (negative)


This persistence results in systematically higher (lower) prices for these particular

firms at the time we model price, namely at the conclusion of the twelve-month

period, and is consistent with the performance persistence documented by the

momentum literature (see, for example, Jegadeesh and Titman, 1993 and, 2001 for

US evidence; Liu et al, 1999 for momentum findings in the UK; Rouwenhorst,

1998 and Nijman et al, 2002 for evidence of momentum profits in Europe; and,

Rouwenhorst, 1999 and Griffin et al, 2003 for evidence in a number of developing

markets). Surprisingly, however, in contrast to Griffin et al, 2003, we document

performance persistence in Hong Kong.

54

Overall, the results for Model (3) for all countries and the pooled sample are highly

significant with F-statistics ranging from 800 (Germany) to 33,400 (US), and

158,200 for the pooled sample. Furthermore, the adjusted R2 values range from

72% (France and Germany) to 94% (Hong Kong), with the pooled sample reporting

an adjusted R2 value of 90%.

7.4 Hybrid Models

Whilst the preceding sections provide an international examination on the

explanatory power of both fundamental and technical analysis in isolation, it says

nothing about whether they act as complements in equity valuation exercises. We

provide evidence on this by fitting Models (4) and (5), with results presented in

Table 7.4 and 7.5, respectively.

Results pertaining to Model (4) reveal the significance of both types of analysis in

explaining share price. In respect to fundamental analysis, the findings for France,

Japan, the UK and the US are consistent with the findings from fitting Model (1) in

these countries. More specifically, inline with the extant literature (see, for example,

Collins et al, 1997; and, Ely and Waymire, 1999), book value per share and

earnings per share are significant positive explanators of contemporaneous share

price.

Interestingly, when fundamental and technical variables are included in modelling,

the earnings per share variables become insignificant for both Australia and

Germany. Furthermore, results from fitting Model (4) in Canada, Hong Kong and

Singapore sees book value per share ceasing to be important in explaining

55

contemporaneous price. We argue these results are due to the information content

contained in technical factors subsuming the fundamental variables.

Further, consistent with Model (3), testing reveals the importance of technical

analysis even in the presence of fundamental factors. Consistent with the findings in

Section 7.3, all countries find that lagged price is significant in explaining

contemporaneous price. In addition, the results for all countries, except Japan and

Singapore, find that both momentum dummies are highly significant in explaining

price, and are significant in the expected directions. Overall, for all countries,

Model (4) is highly significant with F-statistics ranging from 614 (Germany) to

20,960 (US), with the pooled sample reporting a F-statistic of 110,000. Further, the

adjusted R2 values range from 76% (France and the US) to 94% (Hong Kong), with

the pooled sample having a adjusted R2 of 91%.

As with Model (4), the results of fitting Model (5) lends support to the

complementary relationship between fundamental and technical analysis,

confirming the significance of each type of measure even given the presence of the

other. Consistent with the findings from Model (2) for Australia, France and

Germany, book value per share and forecast earnings per share are significant

positive explanators in explaining contemporaneous share price, but current

earnings per share remain insignificant. This result concurs with the findings of

Dechow et al (1999), who argue that forecast earnings per share subsume the

information contained in the current earnings measure.

56

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ble

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However, interestingly, for Japan and the US, as well as the pooled sample, the

inclusion of forecast earnings per share does not detract from the significance of the

contemporaneous earnings measure in explaining price. Furthermore, for both Hong

Kong and the UK, the inclusion of forecast earnings and the technical measures sees

both book value per share and earnings per share become insignificant in modelling.

Results pertaining to Canada and Singapore highlight the only significant

fundamental measure as earnings per share.

Similar to Model (4), except for Japan and Singapore, the technical factors are all

highly significant, with the coefficients on the dummy variables being in the

expected direction. Overall, across all countries and the pooled sample, Model (5) is

highly statistically significant with F-statistics ranging from 659 (Germany) to

18,020 (US), with the pooled sample having a F-statistic of 101,600. In addition, the

adjusted R~ values range from 77% (US) to 95% (Hong Kong), with the pooled

sample having an adjusted R2 of 92%.


To evaluate the relative explanatory power of Models (1) to (5) for all countries and

the pooled sample, we augment the ensuing analysis of adjusted R2 measures with

a consideration of AIC values, with both measures included in Tables 7.1 through

7.4. We do this as, even though the response variable in all models is identical, and

therefore a comparison of their R2 values is meaningful, this goodness-of-fit

measure is deficient insofar as it fails to adequately consider entropy as well as a

model’s fit.

59

Consequently, we also undertake a comparison of models’ AIC estimates, which

have the added benefit of greater suitability in large samples. Overall, an

examination of the R2 and AIC values for the individual countries and the pooled

sample reveals that Models (1) through (5) are of increasingly good fit, as

evidenced by a marked increase in the former and decrease in the latter. Moreover,

the inclusion of both fundamental and technical variables in model fitting

corresponds to an increase in R2 measures relative to Models (1) to (3), and a drop

in AIC values.

However, whilst the above discussion highlights the increasing explanatory power

of the models, the most important question is whether fitting a hybrid model creates

a statistically significant improvement in the ability to explain contemporaneous

share price relative to fitting models containing either fundamental or technical

factors in isolation. To answer this, we consider likelihood ratios, which compare

two competing models where one is the nested version of the other (see, for

example, Felsenstein, 1981; Huelsenbeck and Crandall, 1997; and, Huelsenbeck

and Rannala, 1997). We present the results of these likelihood ratios in Table 7.6.

60

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A comparison of these ratios across countries confirms that hybrid models provide a

statistically significant increase in explanatory power relative to fundamental or

technical models. In regards to fundamental Models (1) and (2), the likelihood

ratios across all countries, as well as the pooled sample, highlight that fundamental

models including forecast earnings per share better explain contemporaneous share

price. Specifically, the pooled sample likelihood ratio for the comparison of Models

(1) and (2) is 18,030.

However, when comparing these fundamental models to models incorporating both

fundamental and technical factors, that is, Model (4) and (5), it is evident that the

hybrid models provide a statistically significant increase in explanatory power.

Specifically, the likelihood ratio between Model (1) and Model (4), and Model (2)

and Model (5), for the pooled sample is 80,762 and 67,598, respectively. In

addition, evaluating the explanatory power of Model (3), the technical model, in

comparison to Model (4) and Model (5), further highlight the complementary nature

of fundamental and technical factors. This is reflected in the statistically significant

likelihood ratio results for the pooled sample (6,934 and 11,800 for the likelihood

tests comparing Model (3) with Models (4) and (5), respectively).

In comparing Models (4) and (5), the majority of countries, and the pooled sample,

find that Model (5) provides a statistically significant increase in explanatory

power. Moreover, Canada is the only country whose results indicate that Model (4)

is the best model, that is, the hybrid model that does not contain consensus earnings

forecasts in fitting. This result is unsurprising given the insignificance of earnings

forecasts in model fitting for Canada.

62

Overall, both on an individual country basis and in the aggregate pooled sample,

our findings not only reveal the complementary nature of fundamental and technical

information, but serve to highlight the benefits of including both analyses in equity

valuation exercises. Specifically, the likelihood ratio tests confirm that the hybrid

models do provide a statistically significant increase in explanatory power relative

to both fundamental and technical models.

7.6 Does the United States Dominate the Pooled Sample?

This section re-examines the pooled sample by testing Models (1) through (5) with

the inclusion of dummy variables to differentiate between the US and other

countries in the sample. We perform this testing as the US dominates the pooled

sample, with over 65% of the observations being from US listed companies.

Initially, we examine models that consider the fundamental variables in isolation.

The results for these models are presented in Table 7.7.

In evaluating these results it is important to note that when D ^ n u s equals zero, the

observation is a US listed company, and as such, the values on the coefficients will

match those obtained in our US findings (Chapter Six). Results obtained from

modelling the fundamental models verify this, with the coefficients of the US

companies matching those acquired in Table 6.1.

63

Table 7.7: Pooled Sample Results of Fitting Fundamental Models Controllingfor the United States

Table 7.7 presents the results in US dollars of fitting Models (1) and (2), below, including dummy variables to control for US observations. Heteroscedasticity and autocorrelation consistent t-statistics are provided in parentheses.

P,tl = a+ß,DN<mUS + ß2BVPS, + ß,(BVPS,*D„mUS)+ ß tE (1)P,t l = a + ß,DNmUS + ß2BVPS, + ßßBVPS,*D„onUS) + ß4EPS, + ß5(,EPS,*D„M )

+ß„FEPSM + ß1(FEPSM *DNmUS)Notation employed in this table is as follows: P,+/ is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; DNonus^ a dummy variable equal to 1 if the observation is from a country other than the US, else 1; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPS,+1 is the consensus forecast earnings per share for the firm, as forecasted in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though the exact date varies slightly.

(1 ) (2 )

Intercept8.26

(33.29***)

7.37

(33.74***)

D N o n us Intercept17.40

(6.03***)

5.38

(2.60***)

BVPS,0.91

(23.64***)

0.51

(16.37***)

D N o n U S B VPS,0.53

(4.40***)

o . n

(0.62)

EPS,1.54

(4 91***)0.17

(1.83)

V I N o n U S EPS,1.40

(1.61)

-0.30

(-1.85)

FEPSt+]4.94

(17.85***)

D N o n U S FEPS,+ 15.03

(4.44***)

Sample 51,689 51,689

Adjusted R2 0.59 0.71


F-Statistic 15,100*** 18,270***


**Denotes significance at the 5% level; and,*** Denotes significance at the 1% level.

64

From Table 7.7, we can see that the results from fitting Model (1) with the inclusion

of D Non us changes the coefficient on book value per share for all countries in the

sample other than the US, but has no significant impact on the earnings per share

coefficient. Specifically, the book value per share coefficient is 0.91 for US

companies and 1.44 (0.91 for the US plus an additional 0.53 for Dnokus) for all other

companies in the sample. Furthermore, the results confirm earlier findings from the

pooled sample, with the positive dependence of contemporaneous share price on

book value per share and earnings per share.

In addition, consistent with Dechow et al (1999) the inclusion of forecast earnings

per share in Model (2) sees contemporaneous earnings ceasing to be a significant

explanator of share price. In contrast to the findings of Model (1), the inclusion of

D Non us has no significant impact on book value per share in Model (2), but does

have a positive affect on the forecast earnings per share coefficient for all countries

in the sample other than the US.

The results pertaining to the technical and hybrid models are reported in Table 7.8.

In regards to the technical model, namely, Model (3), the inclusion of DNonUS sees a

statistically significant difference on Doown for non-US companies, with the

coefficient decreasing from -1.28 to -9.08 (-1.28 for the US plus an additional -7.80

for DNonUS)•

Despite controlling for US observations, results obtained for the hybrid models

further verify the complementary nature of fundamental and technical analysis.

Specifically, for Model (4), all technical factors remain highly significant in the

65

predicted direction. Further, consistent with the pooled sample findings in Section

7.4; all fundamental factors are significant in explaining contemporaneous price.

Interestingly, the inclusion of D^onus in Model (4) only affects the momentum

dummy variable, Dyp. Specifically; the coefficient is statistically significant,

increasing by 8.55, from 1.43 to 9.98. Again, this finding is evidenced in Model (5),

with DUp having a significantly higher coefficient of 10.36 for non-US companies

(1.45 for the US plus an additional 8.91 for D^onus) in the sample, compared to 1.45

for US companies.

66

Table 7.8: Pooled Sample Results of Fitting Models Including Technical Factors Controlling for the United States

Table 7.8 presents the results in US dollars of fitting Models (3) through (5), below, including dummy variables to control for US observations. T-statistics in parentheses are heteroscedasticity and autocorrelation consistent.

=«+ÄO ».«+A^-5+A(^-5*o*»IS) + A A * + Ä ( o * * o ^ ) + Ä ö n».+A (D n_ * o * . U!) (3)PM = a + ß lD„M S+ ß 1BVPSl + ß>(BVPS,*D„„m ) + ß ,E P S,+ ßs(EPS,*Df,„us)+ ß tP,_s

* D u e lls ) + ß*DUp + ßv(Du, * Dm»us) + Ä i]A*™, + A |(Da.™ * ö /*»us)PM = a + ß lDKMS+ ß2B V P S,+ ß ,(B V P S,»D ^us)+ ß iEPS,+ßßEPSl *Dm,us) + ßi FEPSM

+ Ä W « *;>«»)+A ^ s + A « .s * » ,. J + A ö » * o « ) + M » - (5)+ ß u ^ D o w n *

Notation employed in this table is as follows: Pl+, is the firm’s end-of-month share price in the month forecast earnings for the coming fiscal year are announced. This share price is adjusted for capitalisation changes; P,_5 is the firm’s end- of-month share price six months prior to that denoted by Pt+I. This share price is adjusted for capitalisation changes; DUp is a dummy variable equal to 1 if the stock performed in the top decile in the six month period commencing one year prior to the measurement of Pt+h else 0; D is a dummy variable equal to 1 if the stock performed in the lowest decile in the six month period commencing one year prior to the measurement of Pt+i, else 0; DNonUS is a dummy variable equal to 1 if the observation is from a country other than the US, else 1; BVPS, is the book value per share of the firm’s equity, calculated as at the end of the most recent fiscal year and adjusted for capitalisation changes; EPS, is the earnings per share of the firm, calculated at the end of the most recent fiscal year, announced to the market in month t and adjusted for capitalisation changes; and, FEPS,+I is the consensus forecast earnings per share for the firm, as forecasted in the month following the release of actual earnings per share figures for the most recent fiscal year. Forecast earnings are adjusted for capitalisation changes and are announced in the middle of the month, though theexact date varies slightly.

(3 ) (4 ) (5 )

In tercep t 2.11(11.29***)

1.68(10.17***)

1.65(11.06***)

D Non us In tercep t11.12(1.81)

4.98(2.38**)

2.90(2.67***)

B V P S , 0.13(8.93***)

0.05(3.94***)

D nohus B VPSt0.14

(1.09)0.08

(1.16)

EPS, 0.51(4.49***)

0.16(2.83***)

DNon US EPS,0.34

(0.89)-0.03

(-0.25)

F E P St+i 1.42(9.10***)

DNonUS FEPS,+ 11.45

(1.52)

Pt-50.90 0.83 0.80

(68.48***) (47.68***) (42.43***)

DNon US P 1-50.05 -0.01 -0.04

(0.74) (-0.01) (-0.36)n 0.91 1.43 1.45E'Up (3.20***) (5.20***) (5.49***)

n n 2.24 8.55 8.91U Non US Up (0.61) (3.26***) (3.44***)

r) -1.28 -0.65 -0.45t-'Down (-8.19***) (-3.86***) (-3.35***)

-7.80 -2.71 -4.56t^NonUS Ft Down (-2.54**) (-0.77) (-1.75)

Sample 51,689 51,689 51,689

Adjusted R‘ 0.90 0.91 0.92

Akaike Info Criterion 11.03 10.90 10.81

F-Statistic 68,410*** 50,220*** 47,030***

Log Likelihood -285,172 -281,816 -279,404


67

7.7 Conclusion

This chapter provides further evidence on the complementary nature of fundamental

and technical analysis in explaining contemporaneous share price. Initially, we

consider models solely fitting either fundamental or technical variables. In regards

to the fundamental models, consistent with the extant literature (see, for example,

Collins et al, 1997) the majority of countries find that contemporaneous share price

is positively dependent on book value per share and earnings per share.

Furthermore, consistent with Dechow et al (1999), the inclusion of forecast

earnings per share generally subsumes the information contained in earnings per

share.

Examination of the technical model revealed, unsurprisingly, that price is positively

dependent on lagged price. Further, results for seven of the nine countries find that

shares exhibiting returns in the six month formation period that place them in the

top (bottom) performance decile continue to enjoy similar positive (negative)


However, the results of testing our hybrid models across all countries reveals the

superior explanatory power of these models relative to those considering either

fundamental or technical variables in isolation. The strength of our hybrid models

is best evidenced by their markedly higher (lower) adjusted R2 (AIC) values

relative to models solely incorporating either fundamental or technical measures,

with further verification provided by the highly significant likelihood ratio tests.

68

Chapter Eight: Conclusion

The extant literature invests considerable effort in assessing the importance of

fundamental and technical factors in equity valuation exercises. However, in doing

this, the literature invariably focuses on one set of factors without reference to the

other. In employing such an approach, the literature neglects the possibility that

fundamental and technical factors could serve as complements rather than

substitutes in equity valuation exercises. In bridging this gap in the literature, we

propose an equity valuation model integrating both fundamental and technical

measures.

Prior to considering whether fundamental and technical analyses complement one

another in the context of equity valuation exercises, we examine the explanatory

power of each type of analysis in isolation. We consider the strength of these

models in Australia, the US, and seven other countries, as well as in aggregate.

Thereafter, we evaluate the explanatory power of our hybrid models in the

aforementioned countries, providing the first study on the complementary nature of

fundamental and technical analyses.

Consistent with evidence presented in the extant literature, preliminary testing

confirms the positive dependence of contemporaneous share prices on both book

value per share and earnings per share (see, for example, Collins et al, 1997,

Dechow et al, 1999). Further, consistent with Dechow et al 1999, the inclusion of

forecast earnings per share in these fundamental models generally sees earnings per

share ceasing to be significant in explaining share prices.

69

Results of fitting models only incorporating technical factors confirm the

importance of lagged price in explaining contemporaneous price. Further, testing

generally shows that shares exhibiting returns in the six month formation period that

place them in the top (bottom) performance decile continue to enjoy similar positive

(negative) performance in the subsequent six months. Such performance persistence

is consistent with the findings of the momentum literature (see, for example,

Jegadeesh and Titman, 1993 and 2001).

More importantly, fitting our hybrid models which incorporate both fundamental

and technical factors to explain equity prices, provide strong evidence that models

integrating both types of measure have superior explanatory power relative to

models incorporating either type in isolation. More specifically, the augmentation of

fundamental valuation models with our suite of technical measures sees marked

increases (decreases) in adjusted R2 values (AIC values) with the significance of

our results further highlighted by the significance of likelihood ratio testing.

Overall, our study yields considerable evidence regarding the complementary nature

of fundamental and technical factors in equity valuation exercises.

Finally, while we focus on the importance of fundamental and technical factors in

equity valuation, our findings have implications for the valuation of other financial

instruments. Specifically, future research may consider the complementary nature

of fundamental and technical analyses in other markets, such as foreign exchange.

70

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76

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