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THE EFFECTS OF MANDATORY IFRS ADOPTION ON

EARNING MANAGEMENT: THE CASE OF SRI LANKA

By

Wijesinghe B.M.T.M

Registration No.: MC 70499 Index No.: CPM 10815

Thesis submitted to the University of Sri Jayewardenepura

in partial fulfilment of the requirements for the degree of

BSc. Accounting (Special) Degree Programme

(ACC 4626)

Department of Accounting

Faculty of Management Studies and Commerce

University of Sri Jayewardenepura

Nugegoda.

February, 2017

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AUTHENCITY STATEMENT

I certify that the attached material is my original work. No other person’s work or

ideas have been used without acknowledgement. Except where I have clearly stated

that I have used some of this material elsewhere, I have not presented it for

examination / assessment in any other course or unit at this or any other institution.

Signature: ……………………………… Date:………………………..

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CONFIRMATION STATEMENT

“I certify that the above statement made by the candidate is true and that this project is

suitable for submission to the Department of Accounting, University of Sri

Jayewardenepura for the purpose of evaluation”

……………………………... ……………………

Signature of the Supervisor Date

(Dr. Athula Manawaduge)

……………………………... ……………………

Signature of the Supervisor Date

(Dr. Roshan Ajward)

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ACKNOWLEDGEMENT

I would like to express my sincere gratitude to my research supervisors, Dr. Athula

Manawaduge and Dr. Roshan Ajward for the continuous support given to enhance my

knowledge in earnings management and research with their dedication, motivation,

enthusiasm, and immense knowledge. Their guidance helped me throughout the

research process and writing of this thesis.

Furthermore, I would also like to acknowledge with much appreciation the crucial

role of the subject coordinator, Prof. Kennedy D. Gunawardana who provided

immense support for the research. I would be also grateful to Dr.

A.H.N.Kariyawasam, Head of the Department for his support.

I would like to express my gratitude towards my parents and Mr. Shameela

Gurulumulla for their kind co-operation and encouragement which help me in

completion of this research.

My thanks and appreciations also go to my colleagues in developing the research and

people who have willingly helped me out with their abilities.

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ABSTRACT

BACKGROUND

This study examines the impact of the mandatory adoption of International Financial

Reporting Standards (IFRS) in Sri Lanka on earning management practices by

focusing on two perspectives; (1) Discretionary accruals and (2) Small positive

earnings. Most empirical studies carried out on this area have revealed conflicting

results. While majority of studies have identified that the earning management does

not significantly improved during the post adoption period, IFRS are perceived to be

minimized the earning management habits as they represent a collection of the

world’s best accounting practices. Even though the considerable amount of researches

have already conducted regarding to the effects of mandatory IFRS adoption in every

country, only a few effective researches have been conducted in Sri Lankan context.

This research is expected to identify whether the mandatory IFRS adoption impact the

earning management of the firms listed in Colombo Stock Exchange (CSE) in Sri

Lanka and the impact on earning management towards the accounting quality.

METHODS

Annual reports and other publicly available data have used as the method of collecting

data regarding the effects of mandatory IFRS adoption on earning management. The

sample of the study includes the firms listed in beverage, food and tobacco sector,

manufacturing sector and Chemical and Pharmaceutical sector at Colombo Stock

Exchange (CSE) in Sri Lanka. The study was conducted using a study sample of 46

firms and the quantitative research methods were employed. The analyses were

conducted by using statistical methods which include descriptive statistics methods

and inferential statistics methods such as correlation, paired sample t test and

regression analysis.

DISCUSSION AND CONCLUSION

The firms’ poor earning quality mislead the users of financial statements and

ultimately in resulted to the higher cost of debt and equity. The International

Accounting Standard Board (IASB) issued IFRS to enhance the capability of the

financial statements and to increase the comparability and the flexibility towards

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them. By contrasting the objectives of IASB, most of the researchers found that the

post adoption of IFRS has resulted in decrease the quality of earning and finally it will

negatively affect to the quality of accounting as well. However, this research finds

that positive change in earning management and accounting quality in post-IFRS

period in Sri Lankan context.

When analyzing the descriptive statistics of |DACC| and SPOS, the mean and the

standard deviation have been decreased in post-IFRS period than pre- IFRS adoption

period. These results shadow a positive change in accounting quality in the post-IFRS

period. According to the Pearson correlation, relationship between |DACC| and other

independent variables in post-IFRS period is lower than the relationship between

|DACC| and other independent variables in the pre-IFRS period. These results also

detected expected positive change in accounting quality in post-IFRS period. As per

the multivariate analysis of |DACC|, only CFO, PIFRS, LOSS and GROWTH

represents positive test statistic values and positive standardized coefficient beta while

all other variables represents negative test statistic values and negative standardized

coefficient beta and all other variables are significant at 95% confidence level. On the

other hand, according to the multivariate analysis of SPOS, only CFO recorded the

negative test statistic values and negative standardized coefficient beta which is

significant at 95% confidence level.

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TABLE OF CONTENTS

Chapter 01: Introduction .......................................................................................... 11

Background Of The Study .................................................................................... 11

Problem Statement And Research Question.......................................................... 14

Problem Statement ........................................................................................... 14

Research Question ............................................................................................ 14

Research Objectives ............................................................................................. 15

Significance Of The Study ................................................................................... 16

Chapter Summary ................................................................................................ 17

Chapter 02: Litrature Review ................................................................................... 18

Background: IFRS Adoption ................................................................................ 18

Accounting Quality .............................................................................................. 20

Determinants Of Accounting Quality ................................................................... 22

Financial Reporting Regulatory Background In Sri Lanka ................................ 22

Accounting Standards ....................................................................................... 23

Financial Information Incentives ...................................................................... 24

Measures Of Accounting Quality ......................................................................... 26

Value Relevance Approach............................................................................... 27

Timely Loss Recognition Approach .................................................................. 27

Earnings Management Approach ...................................................................... 27

Empirical Findings ............................................................................................... 30

Chapter Summary ............................................................................................. 32

Chapter 03: Methodology ........................................................................................ 33

Overview ............................................................................................................. 33

Conceptual Diagram ............................................................................................ 33

Sampling And Data Collection ............................................................................. 34

Data Management ................................................................................................ 38

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Data Analysis Strategies ....................................................................................... 39

Hypothesis Development ..................................................................................... 39

Models ................................................................................................................. 40

Discretionary Accruals ..................................................................................... 40

Small Positive Earnings .................................................................................... 42

Chapter Summary ................................................................................................ 43

Chapter 04: Analysis ............................................................................................... 44

Overview ............................................................................................................. 44

Descriptive Statistics ............................................................................................ 45

Correlation ........................................................................................................... 48

Univariate Analysis .............................................................................................. 58

Multivariate Analysis ........................................................................................... 60

Chapter 05: Conclusion & Recommendation ........................................................... 69

Findings And Conclution ..................................................................................... 69

Limitations And Recommendatons....................................................................... 71

References ........................................................................................................... 73

Appendix ............................................................................................................. 78

Appendix 01 ..................................................................................................... 79

Appendix 02 ........................................................................................................ 81

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LIST OF FIGURES

Figure 2-1: Determinants of Accounting Quality ..................................................... 22

Figure 2-2: Measures of Accounting Quality ........................................................... 26

Figure 3-1: Conceptual Framework ........................................................................ 34

Figure 3-2: Beverages, Food and Tobacco Sector Sample ....................................... 36

Figure 3- 3: Manufacturing Sector Sample .............................................................. 36

Figure 3- 4: Chemical and Pharmaceutical Sector Sample ...................................... 37

Figure 3- 5: Sector-wise Distribution ...................................................................... 38

Figure A-1: Normal P-P plot of |DACC| .................................................................. 79

Figure A-2: Randomness of errors of |DACC| ......................................................... 81

Figure A-3: Normal P-P plot of SPOS ..................................................................... 82

Figure A-4: Randomness of errors of SPOS ............................................................. 83

LIST OF TABLES

Table 3-1: Sample Selection Method ........................................................................ 35

Table 4-1: Winsorization Process ............................................................................ 44

Table 4-2: Winsorization Values .............................................................................. 45

Table 4-3: Descriptive Statistics .............................................................................. 46

Table 4-4: Descriptive Statistics of Cross Sectional |DACC| .................................... 46

Table 4-5: Descriptive Statistics of Variables Used to Estimate SPOS ..................... 47

Table 4-6: Correlation Analysis of Pre-IFRS explanatory variables (|DACC|)......... 49

Table 4-7: Correlation Analysis of Post-IFRS explanatory variables (|DACC|) ....... 50

Table 4-8: Correlation Analysis of Pre-IFRS explanatory variables (SPOS) ............ 52

Table 4-9: Analysis of Post-IFRS explanatory variables (SPOS) .............................. 55

Table 4-10: Univariate Analysis of |DACC| ............................................................. 58

Table 4-11: Univariate Analysis of Cross Sectional |DACC| .................................... 59

Table 4-12: Univariate Analysis of SPOS ................................................................ 59

Table 4-13: Summary of Model 01 ........................................................................... 60

Table 4-14: Coefficients of Model 01 ....................................................................... 61

Table 4-15: Exclude Variables of Model 01 ............................................................. 62

Table 4-16: Summary of Model 02 ........................................................................... 62

Table 4-17: Coefficients of Model 02 ....................................................................... 63

Table 4-18: Exclude Variables of Model 02 ............................................................. 64

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Table 4-19: Summary of |DACC| Models ................................................................. 64

Table 14-20: Coefficients of |DACC| Models ........................................................... 65

Table 4-21: Excluded Variables of |DACC| Models ................................................. 66

Table 4-22: Summary of SPOS Model ...................................................................... 67

Table 4-23: Coefficients of SPOS Models ................................................................ 67

Table 4-24: Excluded Variables of SPOS Models..................................................... 68

Table A-1: Durbin Watson statistic of |DACC| ......................................................... 80

Table A-2: Durbin Watson statistic of |DACC| ......................................................... 82

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CHAPTER 01: INTRODUCTION

BACKGROUND OF THE STUDY

As per Umobong and Akani (2015, p. 61) the importance of accounting regulations

and accounting standards of reporting practices are encouraged to change due to

financial crises and the collapse of number of high quality, large scale companies in

western countries and ultimately this sketched world devotion to the quality of

financial reporting. In addition, Bharath, Sunder and Sunder’s observation in 2008

(cited in Lopes, Cerqueira & Brandao 2010), indicated that evaluation of firm’s

capability to repay debt and pay dividend will harder if the accounting quality is poor.

Furthermore, Francies et al. mentioned (2005, p. 13, cited in Lopes, Cerqueira &

Brandao 2010) that poor accounting quality resulted to higher cost of debt and equity.

Therefore the adoption of IFRS of reporting has possibly smooth cross-border

comparability, led to increase reporting transparency, decrease information cost,

reduce information asymmetry, and thereby increase the liquidity, competitiveness,

and efficiency of markets (Ball 2006; Choi & Meek 2005, cited in Horton, Serafeim

& Serafeim 2012). According to the Daniel’s observation (unpub.) enhancement of

the comparability for the users of financial statements and upgraded capital allocation

are the crucial benefits of an international set of accounting standards. According to

the Verdi’s observation in 2006 (cited in Lopes, Cerqueira & Brandao 2010) higher

accounting quality led to increase investment efficiency and ultimately it resulted to

reduces financial information unevenness and by ensuring that earnings are more

representative of future cash flows (Teruel, Solano & Ballesta 2009, cited in Lopes,

Cerqueira & Brandao 2010).

The Board of the International Accounting Standards Committee (IASC) initiated

introducing International Accounting Standards (IAS) in 1973. The major persistence

of those standards was to combine the financial reporting desires of European firms.

In the month of April 2001, the IASC converted as the International Accounting

Standards Board (IASB) and becomes the standard setting body for European firms.

The IASC issued IAS from 1973 and subsequently IASB has started issuing

accounting standards which are named as International Financial Reporting Standards

(IFRS).

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The purpose of this study is to investigate whether the mandatory IFRS adoption

improves the earning management of the organization and ultimately how it affected

to the earning quality in the case of Sri Lankan context.

Prior to the adoption of IFRS, Sri Lankan firms were required to prepare their

financial statements in compliance with Sri Lankan Accounting Standards (SLAS).

The Institute of Chartered Accountants of Sri Lanka (ICASL) announced SLFRS and

LKAS (Sri Lankan Financial Reporting Standards and Lanka Accounting Standards)

in 2011 with the authorization of the International Accounting Standard Board (IASB)

are fundamentally based on the International Accounting Standards published by

IASB (Sri Lanka Accounting Standards 2011). But the mandatory adoption of IFRS

was postponed to financial period beginning on or after 1st of January 2012 due to

extraordinary practical nature of IFRS and other reasons. All the listed companied in

Sri Lanka were required to prepare their financial statements in accordance with IFRS

with effect from the financial period beginning on or after 1st of January 2012.

Therefore, this research observes the effects of mandatory adoption of International

Financial Reporting Standards (IFRS) from January 1st of 2012 on earning

management of Sri Lankan firms.

There are three measurements of accounting quality; namely (1) Earning

management, (2) Timely loss recognition and (3) Value relevance which is frequently

used in recent studies (Hans et al. 2015; Umobong & Akani 2015; Lin, Cheong &

Gould 2012). To analyze the quality of accounting, the researchers (Hans et al. 2015)

compare the firms’ pre and post IFRS adoption separately for voluntary adopters and

resisters using above mentioned measurement criteria. According to the Lops,

Cerqueira and Brandao (2010, p. 27), there are other variables that impacted to the

quality of accounting including the size of the firm, leverage of the firm, etc.

Marker and Pearson (2000) suggested that the earning management is selecting

generally accepted methods (in the case of this study, it’s IFRS) with concern for

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presence rather than reality and it includes indirect techniques such as varying

reported earnings through performance timing. According to the Barth et al.

observation in 2008 (cited in Hans et al. 2015), earning management practices can

identify in two ways; (1) Earning smoothing including variability of changes in

earnings, variability of changing earnings relative to the variability of changes in cash

flows and negative correlation between accruals and cash flows, (2) Managing

towards small positive earnings. Earning management can measure by using two

ways; (1) The discretionary accrual model, and (2) The earning distribution model

(Umobong & Akani 2015). Most of the prior researchers used the first method to

measure the earning management as it leads to higher comparability.

Since most of the existing research articles implied that the effect of mandatory IFRS

adoption leads to higher accounting quality, this research is expected to explore how

the earning management changes in relation to mandatory IFRS adoption and its

overall impact on accounting quality of Sri Lankan firm. Furthermore, the research is

designed to analyze whether Sri Lankan firms practice the reporting of positive

earnings. Annual reports and other published information are used as sources of data

collection and analysis has been done based on the gathered data. Finally, the

hypotheses are tested to find out the direction and magnitude of earning management

practices of Sri Lankan firms.

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PROBLEM STATEMENT AND RESEARCH QUESTION

Problem Statement

In Sri Lanka, it is compulsory for all corporate bodies to have a statutory audit in

accordance with the Companies Act No. 7 of 2007. From time to time the Institute of

Chartered Accountants of Sri Lanka (ICASL) empowers by the Sri Lanka Accounting

and Auditing Standards Act No. 15 of 1995 to adopt and publish in the gazette,

accounting standards for the purpose of maintaining uniformity and high quality in

financial reporting of firms (Fernando 2010). This is the main reason of adopting

IFRS by the ICASL with the permission of the International Accounting Standards

Board (IASB). IFRS is generally perceived to be more principle based than other

financial reporting standards, which is supposed to be more rule based. Generally this

can be identified as an advantage, since principle based standards provide greater

flexibility for different situations (Ames unpub).

In recent financial reporting context, the adoption of IFRS becomes a major problem,

because some researchers argued that it will lead to decrease accounting quality

including earning management (Christensen et al. 2015). Some of the regulatory

bodies are given their attention to find out the root cause of this matter. However as

far as researcher concern, in Sri Lanka there is lack of effort put to analyze the effects

of mandatory IFRS adoption towards the accounting quality. Since there is a space of

conducting research in the field of mentioned gap in Sri Lanka, this research is

organized to address the effects of mandatory IFRS adoption on earning management

towards the earning quality.

The stakeholders namely; investors, employees, lenders, government, suppliers,

customers and the public are highly rely on the financial statements of the firm.

Therefore, the accountants, internal and external auditors should responsible to follow

the guidelines prescribed by the International Financial Reporting Standards (IFRS).

Research Question

IFRS have been adopted around 147 countries at the end of the October, 2015 (PWC

2015). This is almost two third of the countries throughout the world since these

accounting standards may offer benefits to the firms to improve the flexibility and

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comparability accordingly. The research is designed to find out whether there is an

impact on the earning management of Sri Lankan listed firms through the mandatory

adoption of IFRS and to assess its impact on earning quality.

Most of the prior researches studies have found that the earning management has

diminished with the mandatory adoption of IFRS while the timely loss recognition

and the value relevance increases (Pascan 2015). Therefore the researcher expected to

identify the influence of mandatory adoption of IFRS towards the earning

management in Sri Lankan context. With the purpose of that, the research question is

developed as follows.

‘Does mandatory IFRS adoption impact on the earning management of the firms

listed in Colombo Stock Exchange (CSE) in Sri Lanka?’

RESEARCH OBJECTIVES

The research is carried out to overcome the above mentioned research questions by

achieving following two main research objectives,

1. Examine the differences in earning management before and after the

adoption of IFRS in Sri Lanka.

2. To identify whether the mandatory IFRS adoption has an impact on the

earning management of the firms listed in Colombo Stock Exchange (CSE)

in Sri Lanka.

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SIGNIFICANCE OF THE STUDY

In the global context, many researchers have been conducted in relation to the effects

of mandatory IFRS adoption on earning management. However in Sri Lankan

context, a few effective researches studies have been conducted on this topic. And

also it is noticed that Sri Lankan researchers have not given adequate consideration to

identify the effects of mandatory IFRS adoption quality of accounting. Therefore, the

research is conducted in order to ascertain whether there is an influence of mandatory

IFRS adoption on earning management and earning quality.

During the past decade more concern has been given for the flexibility and the

comparability of the financial reporting and it has become a critical area in the

accounting field resulting that the IFRS become mandatory all around to the world.

This research aims to summarize the pattern of earning management, prior to IFRS

become mandatory (before 1st of January 2012) and after it is become mandatory

(after 1st January 2012).

The accountants, finance people and firms’ management are the responsible parties to

track the guidelines prescribed by the International Financial Reporting Standards

(IFRS). And also it is constitutional requirement for all firms listed in Colombo Stock

Exchange (CSE) in Sri Lanka to have a statutory audit as per the Companies Act No.

7 of 2007. Therefore the external auditors are responsible to make observation and

give independent opinion on whether the financial statements of listed companies are

compliance with IFRS requirements.

The investors, suppliers, lenders, government and other external stakeholders are very

much depending on the financial statements of the firm. Therefore it is essential to

maintain the quality of financial reporting and also the flexibility and the

comparability towards them. To sustain the flexibility and the comparability, the

Institute of Chartered Accountants of Sri Lanka (ICASL) announced SLFRS/ LKAS

which are adopted from IFRS with the permission of the International Accounting

Standard Board (IASB). To ascertain the quality of accounting, most of the prior

researches have used three measurement categories, namely earning management,

timely loss recognition and value relevance. This researcher is designed to identify the

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quality of accounting through one of the above measurement categories i.e., effect of

IFRS adoption on earning management.

Guidelines prescribed by the IFRS cannot be applied in some situation due to

practical consideration. For an example, SLFRS 13; Fair value measurement and

LKAS 41; Accounting for Biological assets. Therefore this research gives importance

on practical significance rather than theoretical significance.

CHAPTER SUMMARY

In the introduction chapter the researcher discussed about the background of the

research study, problem statement and research questions, research objectives and the

significance of the study.

The rest of the study is designed as follows. The second chapter is consists of

literature review together with background of the IFRS adoption, identification of

accounting quality, determinants, measures of accounting quality and empirical

findings are developed. The third chapter is consists of methodology area of the

research together with conceptual framework, sampling and data collection method,

data management, data analysis strategies, hypothesis and models used for calculated

the discretionary accruals and small positive earnings. The fourth chapter is consists

of analysis part of the research together with descriptive statistic and inferential

statistic including correlation, univariate analysis and multivariate analysis. The final

chapter includes the research findings, conclusion and recommendations for further

studies.

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CHAPTER 02: LITRATURE REVIEW

BACKGROUND: IFRS ADOPTION

The International Accounting Standards (IAS) was initiated in 1973 by the

International Accounting Standards Committee (IASC). This committee was

established by accounting bodies in several countries such as Australia, Canada,

Japan, France, Mexico, Germany, United States, Ireland, Netherlands and the United

Kingdom (Soderstrom & Sun 2007 ; Aljifri & Khasharmeh 2006, cited in Duarte,

Saur & Azevedo 2015).

According to the Suarte, Saur and Azevedo’s observations in 2015, IFRS were

mandatorily adopted on or after 1st January 2005, with the purpose of presenting

consolidated financial statements in public limited companies (PLC), in countries

which have conspicuous capital markets such as Australia, Hong Kong, South Africa,

Philippines and European Union (EU) members. On the other hand, certain

companies were excluded from mandatory IFRS adoption such as the companies

listed in the Alternative Investment Market (AIM) in United Kingdom (UK), Swiss

firms which are not multinationals. Other countries which do not have conspicuous

capital market such as Japan decided to adopt IFRS voluntarily. Eventhough it is

accepted to adopt IFRS over the world since year 2005, there are some countries still

follow their own financial reporting standards such as United States, China, Brazil,

Mexico, Malaysia and etc. (Suarte, Saur & Azevedo 2015).

Houqe et al. (2016, pp. 14-16) stated that accounting quality differ from one country

to another even though they use the same accounting standards, hence it depends on

extensive range of diverse philosophies and institutional settings. Although some vital

observations (Barth et al. 2008; Leuz et al. 2003, cited in Houqe et al. 2016)

documented that, ‘it improves the earnings quality by following IFRS adoption; others

provide evidence of either no improvement or decline in earning quality’.

Cairns’s observation in 2003 (cited in Citter, Tarca & Wee 2012), stated that ‘[t]he

adoption of international financial reporting standards (IFRS) from 1st January 2005

aims to improve quality of financial reporting and efficiency of capital markets’.

According to the Pownall and Schipper’s observation in 1999 (cited in Chua, Cheong

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& Gould 2012), IFRS provides an advantage to the financial statement users by

increasing the quality of financial reporting through enriching comparability from

corner to corner exchange markets and countries across the world. Milova stated that

(2014, p. 3), IFRS was designing a suitable method to meet the snowballing capital

market, addressing the superior comparability and transparency across multinational

companies. IFRS adoption improved the quality of accounting while GAAP and other

standards which designed locally are not able to meet the essentials of comparable

financial statements (Milova 2014). In global context, the investors as well as

financial analysts are less likely or do not face the interpretation problems when using

the same financial reporting standards to interpret the financial information in

financial statements (Chua, Cheong & Gould 2012). On the other hand, IFRS have

largely affected the earnings management of the firms. According to the Citter, Tarca

and Wee (2012, p. 400), ‘[i]f reported earnings are significantly affected by IFRS

adoption, firms have an incentive to ensure analysts understand the new earnings

number by providing timely and useful disclosure’.

According to the Houqe et al. study in 2016, once a firm adopt IFRS mandatorily,

measures like forecast accuracy of the information environment will escalation

expressively. Likewise it will end up with an upturn in market liquidity and decline in

cost of capital of the company accordingly (Daske et al. 2008, cited in Houqe et al.

2016). Another advantage of adoption of IFRS is that it disregards countrywide

accounting dissimilarities and quality of earnings leftovers diverse across countries

(Houqe et al. 2012, cited in Houqe et al. 2016). This kind of dissimilarities will arise

due to the factors like culture, legal system and other economic factors and ultimately

it will result in differences in financial statements as well (Doupnik & Perera 2009,

cited in Houqe et al. 2016). Other than above identified advantages, another three

basic advantages can be recognized due to adoption of IFRS, [1] Decrease in

distribution and increase in analyst estimate the accuracy (Brown, Preiato & Tarca

2009; Horton, Serafeim & Serafeim 2013; Byard, Ying & Yu 2011, cited in Ahmed,

Neel & Wang 2013), [2] Enhancements in liquidity and decrease in cost of equity

capital (Daske et al. 2008; Li 2010, cited in Ahmed, Neel & Wang 2013) and, [3]

Encouraging price responses to events proposing an increase in the probability of

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mandatory IFRS adoption (Armstrong et al. 2010, cited in Ahmed, Neel & Wang

2013).

IFRS adoption was comprehensively affected to the countries which followed Anglo-

Saxon accounting model; it has being subjected to the shareholder oriented

accounting system. Because of that the countries which use stakeholder oriented

accounting systems are anticipated to have likelihood to influence on their financial

statements as well (Hung & Subramanyam 2007; Paglietti 2009, cited in Uyar 2013).

On the other hand, countries which comes under European continental law such as

European countries has caused a substantial change point of view of accounting as

they move to a principle based system from a rule based system (Callao et al. 2010,

cited in Milova 2014). Milova’s observation (2014, p. 5) states that, The international accounting standards board (IASB) leans towards the principle-based

approach which needs managers’ as well as auditors’ professional judgment to ensure that a

financial statement will faithfully reflect the economic substance and transaction.

Previously, FASB preferred a rule based approach and today IASB converted it into

principle based approach through the adoption of IFRS which leads to a narrowly

prescribed guidance (Epstein 2009).

ACCOUNTING QUALITY

Accounting quality is a concept to which many research papers refer, but there is no

universally accepted definition. According to the Barth et al. observation in 2008

(cited in Umobong & Akani 2015), accounting quality is the ability of accounting

measures to revealed economic situation and performance of the firm. This relates to

the statement of financial position and the performances of the firms by affecting to

the statement of comprehensive income (Umobong & Akani 2015). As per the IASB

observation in 2010 (cited in Vera 2013), the main objective of the financial reporting

is to deliver information which is worthwhile to investors, creditors and other relevant

parties. As per the Vera’s observation in 2013, ‘an empirical research has been

focusing on the relationship between different accounting standards and share prices

or returns, with the purpose of identifying the best accounting policies’.

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In accordance with the Martinez and Ferrero’s observation in 2014 (cited in Pascan

2015), financial reporting quality has been define as the authenticity of the

information occupied by the financial reporting process should ensure that market

participants are fully knowledgeable in order to make well informed choices on

investments and credit and ultimately which requires the companies to willingly

enlarge the scope and quality of the information they report. The higher accounting

quality led to increased investment efficiency (Verdi 2006, cited in Lopes, Cerqueira

& Brandao 2010) and finally it will result to the decreases in financial information

disproportion and by ensuring that earnings are more representative of future cash

flows (Teruel, Solano & Ballesta 2009, cited in Lopes, Cerqueira & Brandao 2010).

Pascan (2015) defines the quality of accounting as follows: In this context, we consider the IFRS Framework as a primary source for defining accounting

quality: The qualitative characteristics of useful financial reporting identify the types of

information are likely to be most useful to users in making decisions about the reporting entity

on the basis of information in its financial report. The qualitative characteristics apply equally

to financial information in general purpose financial reports as well as to financial information

provided in other ways.

The main purpose of the financial reporting is to decrease information asymmetry

between corporate managers and parties contracting like shareholders, lenders,

suppliers, customers, employees and other stakeholders with their firms (Hans et al.

2015).

The characteristics of accounting quality are separated into two; [1] Accounting based

attributes which affected to accruals quality, determination of earnings, and certainty

of earnings and smoothness of earnings, [2] Market based attributes based on the idea

that the function of earnings is to assign cash flows into the accounting periods using

accruals (Verleun et al. 2010, cited in Lin, Cheong & Brandao 2010). There are two

reasons as to why a firm may voluntary adopt IFRS in the process. According to the

Hans et al. (2015, p. 36), ‘[t]he first implies that IFRS has an incremental effect on

accounting quality while the second suggests that it is a manifestation of other

underlying factors’. Furthermore there are well recognized three dimensions of

accounting quality; earning management, timely loss recognition and value relevance

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which is commonly used in recent studies (Hans et al. 2015; Umobong & Akani 2015;

Lin, Cheong & Gould 2012).

DETERMINANTS OF ACCOUNTING QUALITY

According to the Duarte, Amaral and Azevedo’s observations in 2015, there are three

main determinations of accounting quality. Those are legal and political system,

accounting standards and financial information incentives. The interconnections

among those three main determinations of accounting quality are illustrated as follows

(Soderstrom & Sun 2007),

Figure 2-1: Determinants of Accounting Quality

Source: Soderstrom and Sun 2007

Financial Reporting Regulatory Background in Sri Lanka

Country wide philosophy is measured to be an aspect that affects the accounting

system of a country (Houqe et al. 2016). According to the Hofstede’s observation in

1980 (cited in Houqe et al. 2016), there are four basic cultural measurements that can

be used to define the similarities and dissimilarities in cultures, [1] Individualism, [2]

Power distance, [3] Uncertainty avoidance and [4] Masculinity. This has been defined

as four broadly accepted accounting values such as professionalism, uniformity,

conservatism and secrecy (Gray 1988, cited in Houqe et al. 2016). On the other hand

there might be dissimilarities in the influences of IFRS adoption in strong application

versus weak application countries. This basically depends on two factors (Ahmed,

Neel & Wang 2013), ‘[1] [W]hether IFRS are of higher or lower quality than

23

domestic GAAP (e.g. whether they increase or decrease overall managerial discretion)

and [2] [T]he efficacy of enforcement mechanisms.’

Accounting Standards

As per the Soderstrom and Sun’s observation in 2007, the accounting standards are

the first and the most significant factor which determines the accounting quality. The

International Accounting Standards (IAS) was initiated in 1973 by the board of

International Accounting Standards Committee (IASC). In the month of April 2001,

IAS converted as an International Accounting Standard Board (IASB) and it states as

the standard setting body for the European firms (EU). Subsequently, IAS named as

International Financial Reporting Standards (IFRS). By obeying the (Sri Lanka

Accounting Standards 2011) global union of IFRS, the Institute of Chartered

Accountants of Sri Lanka (ICASL) announced Sri Lanka Financial Reporting

Standards (SLFRS) and Sri Lanka Accounting Standards (LKAS) in 2011 with the

approval of the International Accounting Standard Board (IASB).

According to the Sri Lanka Accounting Standards (2011), ‘[t]he Sri Lanka accounting

and auditing standards act, no. 15 of 1995 authorizes ICASL to issue Sri Lanka

accounting standards and requires “specified business enterprises” to prepare and

present their financial statements in compliance with Sri Lanka accounting standards’.

Therefore all listed companies in Sri Lanka should arrange their financial statements

in accordance with the requirements of IFRS with effect to the financial period

beginning on or after 1st of January 2012.

In Sri Lankan context the objectives (Sri Lanka Accounting Standards 2011) of the

Institute of Chartered Accountants of Sri Lanka (ICASL) setting of IFRS are, a) to develop, in the public interest a set of high quality, understandable and enforceable

accounting standards that require high quality, transparent and comparable information in

financial statements and other financial reporting to help users of the information to make

economic decisions.

b) to promote the use and rigorous application of those standards;

c) in fulfilling the objectives associated with (a) and (b), to take account of, as appropriate,

the special needs of small and medium-sized entities; and

d) to bring about convergence of Sri Lanka accounting standards and International Financial

Reporting Standards (IFRS) to produce high quality solutions.

24

The effects of mandatory IFRS adoption on accounting quality unfavorably suspend

due to the fact that, IFRS are of higher or lower quality than domestic GAAP and the

way they affect the effectiveness of implementation instruments (Ahmed, Neel &

Wang 2013). According to the prior research (Ahmed, Neel & Wang 2013) the higher

quality standards means, ‘standard that either reduces managerial discretion over

accounting choices or inherently disallows smoothing or overstatement of earnings’.

Hence if the IFRS recorded less quality than GAAP, firms should expect them to

reduce the quality of accounting (Ahmed, Neel & Wang 2013).

Financial Information Incentives

As per the Soderstrom and Sun’s observation in 2007, the financial information

incentives consists of financial market development, capital structure, ownership and

tax system.

Financial market development is first and the most important financial information

incentive among other incentives (Soderstrom & Sun 2007). According to the

Brown’s study in 2011 (cited in Duarte, Amaral & Azevedo 2015) the modifications

of IFRS resulted, many values in the appraisal of the shares as well as the capital

markets. The IFRS adoption distresses the connection between accounting

information and market value even for a countries categorized by the IFRS

presentation, excellent financial information and strong investors’ safety (Chalmers,

Clinch & Godfrey 2011, cited in Duarte, Amaral & Azevedo 2015).

Capital structure is the second and very important financial information incentive

(Soderstrom & Sun 2007). There are different incentives for financial reporting where

for the firms with dissimilar financial needs (Soderstrom & Sun 2007). According to

the Francis et al. observation in 2008 (cited in Duarte, Amaral & Azevedo 2015),

‘IFRS is a decision that aims to reduce the asymmetry of information of private firms’

adopters and facilitate contracting with external parties’. The capital structure has

indirectly affected by the political and legal systems (Duarte, Amaral & Azevedo

2015).

25

Ownership is third and very essential financial information incentive that affect the

quality of accounting (Soderstrom & Sun 2007). Firms with rigorous ownership and

great variances between cash flow moralities and the right to regulate, have fewer

incentives for reporting the financials (Soderstrom & Sun 2007).

Tax system is the last and the next important financial information incentive that

determines the quality of accounting (Soderstrom & Sun 2007). According to the

Duarte, Amaral and Azevedo’s observation in 2015, there are three ways that the tax

system is influence to the quality of accounting. Those are, [1] There is robust

connection between accounting income and taxable income and the consequences are

affected to the fundamental business, [2] The extraordinary tax rate growths the

incentives to decrease taxable income, [3] Every countries tax authorities have

regulatory power to check the companies’ profits. As per the Soderstrom and Sun’s

observation in 2007 states that, ‘financial information in the common law countries

reduce the asymmetry firm’s information’.

26

MEASURES OF ACCOUNTING QUALITY

The main three dimensions are illustrated in figure 2-2 below (Umobong & Akani

2015). According to this framework IFRS adoption is the independent variable and

the dependent variable is accounting quality.

Figure 2-2: Measures of Accounting Quality

Source: Umobong & Akani 2015

As shown in this framework there are three factors which affected to the dependent

variable. Among those variables the researcher will concentrate on the most important

factor, the earning management.

Alternatively different view about the effects of mandatory IFRS adoption, it is

affected for three group of accounting quality dimensions: [1] Income smoothing, [2]

Reporting aggressiveness and [3] Earning management (Ahmed, Neel and Wang

2013). According to the Ahmed, Neel and Wang’s observation in 2013, eventhough

different researchers represent different measurements for the quality of accounting,

the earning management remains as a commonly cited measurement of accounting

quality.

Although there are various measurements to measure the quality of accounting, the

researcher will follow the most favourable measurements, which were illustrated in

above diagram.

27

Value Relevance Approach

One of the main objectives of IFRS is to distribute information to the users of

financial statements which the value is relevant, means deliver information that are

relevant to the decision making regarding to the investments of creditors and potential

investors (Ames 2013). According to the Ball and Brown’s study in 1968 (cited in

Ames 2013), ‘value relevance has been tested with stock price as an indicator of

reactions to accounting information’. Components on financial statements like

depreciation, amortization, interest or taxes does not affect to the value relevance

considerably. Because IFRS standards are much consider about fair value accounting

other than historical cost method (Ames 2013). As per Umobong and Akani’s

observation in 2015, ‘value relevance is measured as the explanatory power of market

price per share regressed on per share book value and earnings per share’.

Timely Loss Recognition Approach

The second variable issued to measures the quality of accounting is timely loss

recognition. According to the Umobong and Akani’s study in 2015, ‘timely loss

recognition approach refers to the coefficient of interaction obtained by regressing

earnings per share over market price on annual stock return ’. As per the prior studies,

this has be due to the lack of enthusiasm of companies to identify huge losses in a

timely manner (Ball et al. 2003; Lang, Raedy & Yetman 2003; Lang, Raedy &

Wilson 2006; Ball & Shivakumar 2005; Barth et al. 2008; Chua et al. 2012, cited in

Uyar 2013).

Earnings Management Approach

The IFRS are principles based and therefore they could provide a great opportunity

for earning management (Barth, Landsman & Lang 2008, cited in Horton, Serafeim &

Serafeim unpub.) As per the Cohan’s study in 2003 (cited in Ames 2013) the earning

quality is defined as how far accounting figures accurately represent the fundamental

health of that a firm and the degree of they the result in future operating cash flows.

On the other hand, the earning quality can be defined as the extent to which operating

fundamentals are taken by reported earnings (Chan et al. 2004, cited in Ames 2013).

Fischer and Rosenzewig’s observation in 1995 (cited in Uyar 2013), defined earning

management as follows,

28

[E]arnings management as a form of behavior applied only to increase or decrease the

reported earnings in the current period for personal interest, without an increase or decrease in

the long-term profitability of the companies owned by the managers.

According to the study of Teets in 2002 (cited in Houqe et al. 2016), quality of

earning is a multi-dimensional perception which is affected by three kind of decisions;

[1] Decisions made by standard setters, [2] Selections made by management on

methods of accounting and, [3] Judgments and estimates made by management when

selecting the above methods.

According to the Yee’s observation in 2006 (cited in Ames 2013) earning

management consist with two components; [1] As a fundamental attribute of the firm

and [2] As a financial attribute. Ames (2013, pp. 156-157) conclude that the

‘[e]arning quality is the extent to which reported earning match the fundamental or

true earning, of the firm’. According to the Umobong and Akani (2015, pp. 61-77),

earning management is defined as a condition where the managers who are

responsible for the preparation and presentation of the financial statements and this

may lead to an imperfect market where the stakeholders do not have all correct and

necessary information based on time by creating information asymmetry between

managers and stakeholders. As per Hoogendoorn’s observation in 2004 (cited in

Umobong & Akani 2015), there are five forms of earning management; [1] Loss

maximization/ big bath accounting, [2] Loss minimization, [3] Profit maximization,

[4] Profit minimization and, [5] Income smoothing which combines the above four

forms of earning management together.

Earning management come up in a situation where the managerial person who is

accountable for the preparation and presentation of financial statements working

towards their own remunerations instead of stakeholders benefits (Umobong & Akani

2015). According to the Chalmers, Clinch and Godfrey’s observation in 2008, this

sort of condition generates information unevenness between managers and

stakeholders can entertain their own wealth by acting on behalf of their self-interests

and those judgments may impact financial reporting information positively and

negatively which ultimately resulted to the agency problem. Eventhough some

authors define earnings management as destruction to the information of financial

29

statements, some authors argues that earnings management is not only supported to

manipulate earnings but also aimed to signaling and informing outsiders to the

changing business (Palepu et al. 2007, cited in Umobong & Akani 2015). Earning

management has resulted for an undesirable influence on decision making and also it

has reduced the convenience and relevance of financial reporting information of users

as well as the company. Hence manipulation of financial statement information

misleads the stakeholders’ decision making process (Umobong & Akani 2015).

According to the Houqe et al. study in 2016, ‘[t]he investor protection regime is also a

factor influencing earnings quality because low earnings quality is less likely to occur

in countries with stronger investor protection’.

According to the Barth et al. study in 2008 (cited in Hans et al. 2015) earning

management can use in two ways; [1] Earning smoothing including variability of

changes in earnings, variability of changing earnings relative to the variability of

changes in cash flows and negative correlation between accruals and cash flows, [2]

Managing towards small positive earnings. Barth et al. (2008, p. 18, cited in Hans et

al. 2015) also stated that; [T]hat managers who prefer smooth earning will discretionally apply accruals to reduce the

variance, a high variance is also consistent with managers applying their discretion to take

‘big baths’ or with errors in accruals, both of which are associated with low-quality

accounting.

In a broader sense, three measurements can be applied to measure the earnings

management, [1] Discretionary accruals (DA), [2] Small positive earnings (SPOS)

and [3] Earnings smoothing (Uyar 2013). Discretionary accruals assessed by spending

the cross sectional model in Kothari et al. 2005 and Jones et al. 2008 (cited in Uyar

2013).

Previous studies advocate that, ‘firms are likely to set a positive earnings level as a

target and use the frequency of small positive net income as a metric of earning

management’ (Barth et al. 2008; Burgstaher et al. 2006; Chen et al. 2010 cited in Uyar

2013). As per the Barth et al. study in 2008 (cited in Uyar 2013), voluntary adoption

of IFRS revealed a low degree of handling earnings towards a target after guiding for

prospective incentives for voluntary IFRS adoption. The motives behind to the

management treat in income smoothing are existing to maintain a steady risk outline

30

for the companies which have an intention to decrease the asymmetry in companies

share prices (Verleun et al. 2011, cited in Umobong & Akani 2015).

EMPIRICAL FINDINGS

The results of the Lops, Cerqueira and Brandao’s observation (2010, p. 20) show that,

after IFRS become mandatory the quality of accounting impacted negatively on firms

in European Union (EU). But it is important to note that the firms who do not hold the

membership of EU have positively affected by increasing accounting quality. This

concludes that mandatory IFRS application and flexibility does not improve the

quality of accounting just because of the adoption of IFRS is mandatory (Lops,

Cerqueira & Brandao 2010). The Umobong and Akani’s results (2015, pp. 61-77)

indicate decrease in accounting quality by using independent variables of earning

management, value relevance and timely loss recognition and book value of equity

are less value relevant and timely loss recognition is less in post IFRS compared to

pre IFRS adoption period.

According to the Epstein’s observation in 2009, IFRS adopters repeatedly ended up

with adverse net income and normally sophisticated value relevance. IFRS adoption

caused enhancement in quality of accounting, because IFRS adopters usually

demonstrated greater accounting quality in the post adoption period than the pre

adoption period (Barth, Landsman & Lang 2007, cited in Epstein 2009). According to

the Bryce, Ali and Mather’s study in 2015, quality of accounting is not significantly

increased after the IFRS adoption, in the case of Australia. On the other hand it says

that audit committee operates more effectively under IFRS than previously adopted

Australian GAAP (AGAAP).

But some researchers stated that (Horton, Serafeim & Serafeim unpub.) ‘[m]andatory

IFRS adoption has proved the quality of information intermediation in capital markets

and as a result firms’ information environment by increasing both information quality

and accounting comparability’. In the case of Ames’s observation in 2013, stated that

the earning management does not significantly improved by the post adoption, but by

the value relevance of major balance sheet components changes post adoption. As

result of Hans et al. observation in 2015, indicates that reporting incentives control

31

accounting standards in defining quality of accounting. It also specifies that ‘it is

unwarranted to infer from evidence on accounting quality changes around voluntary

adoption that IFRS per improves accounting quality’. As according to the case of

South Africa, the post adoption period bought better results due to the fast

development of the country and the economy than pre adoption period (Ames 2013).

As per the Ames’s suggestions (2013, p. 159), there are many positive years in post

adoption period such as 2005, 2006 and 2007 as well as more or less unfortunate

years for the South African economy such as 2008 and 2009 due to the “financial

crisis” or the “great recession”. According to the Uyar’s study in 2013, the mandatory

IFRS adoption in Turkey affected earning management as follows, [T]he earnings management practices were observed to decrease as compared with the pre-

IFRS period and the timely loss recognition and value-relevance values were observed to

increase, which constitute the dimensions of accounting quality. It was also concluded that by

the switch from domestic accounting standards to International Accounting Standards (IAS),

the quality of accounting in the country was improved and the market became more active

than it was before.

In the case of Uyar’s study (2013, p. 477), it proves that the enhancement of the

quality of accounting by reduction of faults correlated to earnings forecasts is

extremely substantial indicator. United Kingdom (UK) demonstrates an increase in

accounting quality due to adoption of IFRS because, UK is a country with robust

administration system and own accounting standard system of UK GAAP which

closer to IFRS (Milova 2014). On the other hand, a country like France discloses no

improvements in earning management or accounting quality after the adoption of

IFRS due to usual characteristic of the country law institution and weak investor

safeguard (Milova 2014). According to the findings presented by prior researchers, no

benefits have been offered by IFRS in common law countries like Australia and UK

and enlarged extensiveness of earning management in France (Jeanjean et al. 2008,

cited in Milova 2014). The main consequence identified in relevant to the adoption of

IFRS is, it resulted to the reduction in cost of information, mainly for cross border

companies, as a result it carrying benefits for investors (Leuz 2003, Corveig et al.

2007, cited in Milova 2014). Commonly, adoption of IFRS should improve the

earning quality due to incremental of transparency of financial reporting (Houqe et al.

2016). According to the Doupnik and Perera’s study in 2009 (cited in Houqe et al.

32

2016), ‘financial statement comparability helps investors to evaluate potential

investment in foreign capital market more easily and, therefore, the risk is reduced’.

In case of value relevance (Goodwin et al. 2008; Jeanjean & Stolowy 2008, cited in

Bryce, Ali & Mather 2015), the quality of accounting leftover steady even after the

mandatory IFRS adoption in Australia. But in the Chalmers et al. study in 2011 (cited

in Bryce, Ali & Mather 2015), the quality of accounting improves after the mandatory

adoption of IFRS. According to the Leuz and Verrecchia’s observation in 2000 (cited

in Citter, Tarca & Wee 2012), ‘the voluntary adoption of IFRS reduces the cost of

equity capital’. On the other hand, IFRS adoption on market liquidity and the

economic consequences of mandatory adoption is uncertain. Countries with high level

of secrecy influence the low level of earning quality with reference to the abnormal

accruals (Houqe et al. 2016). Eventhough the empirical results shows improvement in

earning quality in all countries due to the mandatory adoption of IFRS, it is indicate

that the countries with stronger secrecy resulted the higher improvement of the quality

of earnings than other countries (Houqe et al. 2016).

CHAPTER SUMMARY

Having understood the literature review done in relation to the effects of mandatory

adoption on earning management towards the accounting quality in above, the study

moves to highlight the process used to collect information and data for the purpose of

conducting research.

The next chapter contains the methodology and it will describe the conceptual

framework, the sample selection and data collection method, data management, data

analysis strategies, hypothesis and models used to calculate the discretionary accruals

and small positive earnings.

33

CHAPTER 03: METHODOLOGY

OVERVIEW

Annual reports and other publicly available data used as a main research method to

gather secondary data regarding the effects of mandatory IFRS adoption on earning

management towards the earning quality. Among the total population of the firms listed

at Colombo Stock Exchange (CSE) in Sri Lanka, 46 companies were selected as the final

sample of the study. The sample selection method is discussed in the following

section. Quantitative research techniques were used for data analysis using Statistical

Package for Social Sciences (SPSS) 20.0 packages. The analyses were conducted by

using statistical methods which include descriptive statistics methods and inferential

statistics methods such as correlation, paired sample t test and regression analysis.

CONCEPTUAL DIAGRAM

International Financial Reporting Standards (IFRS) is the main related conceptual

requirement of this study. But this research mainly referred for the Sri Lankan

context, hence this is based on Sri Lanka Financial Reporting Standards (SLFRS) and

Lanka Accounting Standards (LKAS) which are published by the Institute of

Chartered Accountants of Sri Lanka (ICASL) with the permission of the International

Accounting Standard Board (IASB).

According to Hans et al. (2015, p. 35), the benefits documented around voluntary

IFRS adoption can be attributed to the change in accounting standards and the early

IAS. Because, the voluntary adaptors obeyed with prior to mandatory IFRS adoption

was the settlement between the assignments from 14 countries (Hans et al. 2015).

Figure 3-1 illustrates the conceptual framework of this research by describing

measurement patterns of the accounting quality. This shows that accounting

standards, legal and political systems, and incentives of financial reporting affect the

accounting quality. According to Soderstrom and Jialin (2007, p. 688), the conversion

to IFRS not only affect the financial reporting, but also to the other dominants that

fluctuate time to time across countries. Therefore, it is probable that quality of

accounting will fluctuate across countries by following IFRS adoption. The quality of

34

accounting depends on the legal and political system of the countries which contains

incentives of financial reporting and accounting standards (Soderstrom & Jialin

2007). This research is conducted to understand the degree of influence by mandatory

adoption of accounting standards.

Figure 3-1: Conceptual Framework

SAMPLING AND DATA COLLECTION

The total population contains all the firms listed at Colombo Stock Exchange (CSE)

in Sri Lanka for the, year ending 31st March 2016. To gather data, a sample of 68

companies listed in Colombo Stock Exchange (CSE) was selected from above

mentioned population.

The researcher attends to select three sectors as per the study sample instead of using

all sectors, as the three sectors collectively account to the highest market

capitalization among others. The sectors selected for the study are beverage, food and

tobacco sector, manufacturing sector and chemical and pharmaceutical sector. Table

3-1 describes the sample selection process in detail. The final study sample consists of

46 companies. The sample excludes the firms that did not prepare their financial

statements for the period ended 31st March. Nine companies were excluded from the

sample with regard to this. Secondly, four companies were excluded due to the reason

of non-availability of annual reports. Finally, nine companies were excluded due to

Accounting Quality

Value Relevance

Timely Loss Recognition

Earnings Management

Discretionary Accruals (DA)

Small positive earnings (SPOS)

35

quoted or listed in Colombo Stock Exchange (CSE) after the financial year ending

2008/09. Table 3-1: Sample Selection Method

Beverage, Food & Tobacco

Manufacturing

Chemical & Pharmaceutical

Manufacturing related sectors for 31st March 2016

(-) Non- March Financial Statements

Beverage, Food & Tobacco

Manufacturing

Chemical & Pharmaceutical

(-) Non Availability of Annual Reports

Beverage, Food & Tobacco

Manufacturing

Chemical & Pharmaceutical

(-) Foreign Currency Denominated Annual Reports

Beverage, Food & Tobacco

Manufacturing

Chemical & Pharmaceutical

(-) Quoted After 2008-09 Financial Year

Beverage, Food & Tobacco

Manufacturing

Chemical & Pharmaceutical

Study Sample

.

.

.

.

.

3

5

1

.

2

1

1

.

0

0

0

.

2

6

1

21

37

10

68

.

.

.

(9)

.

.

.

(4)

.

.

.

(0)

.

.

.

(9)

46

The follwing pie charts illustrated that, how many companies are included in the study

sample and how many companies are excluded from the study sample in each and

every sector.

36

The figure 3-2 shows the above combination of the beverage, food and tobacco sector,

Figure43-2: Beverages, Food and Tobacco Sector Sample

From the 21 companies in the beverage, food and tobacco sector, seven companies

were excluded due to non-presentation of their financial statements for the period

ended 31st March, foreign currency denominated annual reports, non-availability of

annual reports and quoted or listed in Colombo Stock Exchange (CSE) after the

financial year ending 2008/09.

The figure 3-3 shows the above combination of the manufacturing sector,

Figure53- 1: Manufacturing Sector Sample

30%

70%

Beverages, Food & Tobacco Sector

Excluded from the study sample

Included in theStudy sample

30%

70%

Manufacturing Sector

Excluded from the study sample

Included in theStudy sample

37

From the 37 companies in the manufacturing sector, twelve companies were excluded

due non-presentation of their financial statements for the period ended 31st March,

foreign currency denominated annual reports, non-availability of annual reports and

quoted or listed in Colombo Stock Exchange (CSE) after the financial year ending

2008/09.

The figure 3-2 shows the above combination of the chemical and phamaceutical sector,

Figure63- 2: Chemical and Pharmaceutical Sector Sample

From the 10 companies of the manufacturing sector, three companies were excluded

due to non-presentation of their financial statements for the period ended 31st March,

foreign currency denominated annual reports, non-availability of annual reports and

quoted or listed in Colombo Stock Exchange (CSE) after the financial year ending

2008/09.

According to the above table and figures illustrated, the total sample distribution of

manufacturing industry among selected sectors can be illustrate as follows,

30%

70%

Chemical and Pharmaceutical Sector

Excluded from the study sample

Included in theStudy sample

38

Figure73- 3: Sector-wise Distribution

The secondary data collection techniques have been used for the purpose of collecting

data. For each of these firms, the accounting data such as turnover, net profit, total

assets, property plant and equipment, trade receivable, total liabilities, common stock,

equity book value and cash flow from operating activities were collected manually by

referring annual reports. Other publicly available information including share market

information were collected using the, Colombo Stock Exchange (CSE) website. All

relevant annual reports have been downloaded from the official web site of CSE in Sri

Lanka.

DATA MANAGEMENT

To analyze collected data researchers can use various types of data analytical tools.

Among them, the researcher used Statistical Package for Social Sciences (SPSS) 20.0

as the data management technique. This is a Windows based program that can be used

to perform data entry and analysis and to create tables and graphs. Information

obtained through annual reports and other publicly available data corded to the SPSS

and prepared strong and complete data for the analysis purposes.

31%

54%

15%

Sector-wise DistributionFood & Beverages Manufacturing Chemical & Pharmaceutical

39

DATA ANALYSIS STRATEGIES

While conducting the study, researcher used various number of analysis methods to

identify whether the mandatory IFRS adoption impacts on earning management of the

firms listed in Colombo Stock Exchange (CSE) in Sri Lanka. The analyses were

conducted by using statistical methods which include descriptive statistics methods

and inferential statistics methods such as correlation, paired sample t test and

regression analysis.

Descriptive statistics methods were used to describe the basic features of the data in a

study. Together with simple graphics analysis, it forms the basis of virtually every

quantitative analysis of data. Inferential statistics were used to make judgments of the

probability that an observed difference between groups is a dependable one or one

that might have happened by chance in this study. First of all, under inferential

statistics the researcher conducted Pearson correlation to test the association between

two variables. And then carry out paired sample t test to determine whether the mean

difference between two sets of observations is zero. Finally the researcher conducted

regression analysis to estimating the relationships among variables and to develop a

suitable model for the measurements.

HYPOTHESIS DEVELOPMENT

In order to achieve the objectives of the study, the main hypothesis and conditional

alternative hypothesis can be constructed as follows.

Main Hypothesis:

H0: The mandatory IFRS adoption impacts the earning management of the firms listed

in Colombo Stock Exchange (CSE) in Sri Lanka.

40

MODELS

Under the guidance of previous studies, earnings management, timely loss

recognition, and value relevance were used as variables that constituted accounting

quality (Lang et al. 2006; Barth et al. 2008; Christensen et al. 2008; Paananen & Lin

2009; Chen et al. 2010; Chua, Cheong & Gould 2012, cited in Uyar 2013). According

to the Fischer and Zweig’s study in 1995 (cited in Uyar 2013), earnings management

defined as, form of behavior applied only to increase or decrease the reported earnings

in the current period for personal interest, without an increase or decrease in the long-

term profitability of the companies owned by the managers. In representing earnings

management, prior literature uses, discretionary accruals (DAs), small positive

earnings (SPOS), and earnings smoothing. But for purpose of this research, the

researcher only uses the discretionary accruals (DAs) and small positive earnings

(SPOS).

Discretionary Accruals

As per the Dechow, Sloan and Sweeney’s observation in 1995, there are various

models that can be used to measure the discretionary accruals such as the Healy

model, the DeAngelo model, the Jones model, the modified Jones model and the

industry model. This study used the modified Jones model, the modified version of

the Jones model in the empirical analysis. In the Jones model the discretionary

accruals were measured with error when discretion is exercised over revenues. The

modification is designed to eliminate the imagined tendency. Therefore in the

modified Johns model, non-discretionary accruals are estimated during the event

period (Dechow, Sloan & Sweeney 1995). The total accruals of the modified Jones

model are estimated as follows:

TACCit = β0 + β1 (1/Assetsit-1) + β2 (ΔREVit – ΔRECit) + β3PPEit + εit

(1)

Where,

TACCit is the total accrual for company i in year t, calculated as the difference

between net income and operating cash flow. The Assets it-1 is prior year (in year t-1)

total asset for company i,

ΔREVit is the change in revenues for company i between year t and t –1,

41

ΔRECit is the change in trade receivable for company i between year t and t –1,

PPEit is the gross property, plant, and equipment for company i in year t.

All of the variables are deflated by lagged total assets. The non-discretionary accruals

of the modified Jones model are estimated as follows:

NDACCit = β0 + β1 (1/Assetsit-1) + β2 (ΔREVit – ΔRECit) + β3PPEit + εit

(2)

Finally, the discretionary accruals is acquired by calculating the difference between

total accruals and estimated non-discretionary accruals as follows,

DACCit= TACCit- NDACCit

(3)

According to the Chen et al. study in 2010 (cited in Uyar 2013), the multiple

regression model was created as equation (4) to discover the connection between the

effects of IFRS adoption and discretionary accruals.

| DACCit| = β0 + β1PIFRSit + β2CSIZEit + β3GROWTHit + β4AUDit + β5LEVit +

β6CFOit + β7LOSSit + β8SECit

(4)

Where,

DACCit is the total discretionary accruals for company i in year t. The absolute value

of DACC is used for the calculation, since the main objective is to measure the extent

of earnings management regardless of whether it is done to increase or decrease

income,

PIFRSit is a dummy variable that equals to one for the post-IFRS adoption period (i.e.

2012/13) and zero if otherwise for firm i in year t,

GROWTHit is the annual revenue percentage change for firm i in year t,

LEVit is at the end of the year total liabilities divided by end of the year equity book

value for firm i in year t,

CFOit is the annual net cash flow from operations scaled by lagged total assets for

firm i in year t,

42

CSIZEit is the natural logarithm 1of market value of equity for firm i in year t. Due to

limitations of having market value of equity the researcher used natural logarithm of

total assets instead,

AUDit is a dummy variable 2 that equals one if a big 4 audit firms 3 are hired and zero

otherwise for firm i in year t,

LOSSit is a dummy variable that equals one for observations of firms with annual net

income less than zero, and zero if otherwise for firm i in year t,

SECit is also a dummy variable that equals one for observation of firms represent the

food and beverage sector and zero if otherwise for firm i in year t.

Small Positive Earnings

The second way of representing the earnings management is small positive earnings.

Prior researchers suggested that (Barth et al. 2008; Burgstahler et al. 2006; Chen et al.

2010, cited in Uyer 2013), firms are expected to set a positive earnings level as a

target and use the occurrence of small positive net income as a metric of earnings

management aimed at achieving such a target. By following those researchers, the

following equation had been taken in to consideration.

SPOSit= β0 + β1PIFRSit + β2CSIZEit + β3GROWTHit + β4AUDit + β5LEVit + β6CFOit

+ β7TURNit + β8DISSUEit + β9EISSUEit +β10SECit

(5)

Where,

SPOSit is a dummy variable that assumes a value of one if net income scaled by total

assets is between 0 and 0.01 and zero if otherwise for firm i in year t,

TURNit is the revenue divided by lagged total assets for firm i in year t,

DISSUEit is a percentage change in total liabilities for firm i in year t, 1 The natural logarithm of a number is its logarithm to the base of the mathematical constant e, where e is an irrational and transcendental number approximately equal to 2.718281828459 2 A dummy variable is one that takes the value 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome 3 Big 4 audit firms are Ernst & Young (EY) since 1989, Price Waterhouse Coopers (PWC) since 1998, Deloitte Touche Tohmatsu since 1989 and KPMG since 1987.

43

EISSUEit is the percentage change in common stock for firm i in year t.

All other variables are the same variables used in the discreionay accruals equaltion

(4) above.

CHAPTER SUMMARY

This chapter consists of conceptual framework, population, sample, data collection,

data management, analysis strategies, hypothesis and essentially the models used to

measure the discretionary accruals and small positive earnings. Having understood the

methodology of the study in relation to effects of mandatory adoption on earning

management towards the accounting quality, the study moves to highlight the process

data analysis used to inspect, clean, transform and remodel data with a view to reach

to a certain conclusion in this research.

The next chapter contains the analysis part of the research and contains descriptive

and inferential statistic part including correlation, univariate analysis and multivariate

analysis to interpret the discretionary accruals and small positive earnings.

44

CHAPTER 04: ANALYSIS

OVERVIEW

In this chapter the researcher is intended to conduct a statistical analysis by using two

basic approaches, [1] Descriptive analysis and, [2] Inferential analysis. Mean and the

standard deviation of each and every continuous variable were considered under the

descriptive analysis. And also under inferential statistics analysis part paired sample t

test, Pearson correlation coefficient and regression analysis were done accordingly to

make inference over the population.

Extreme values of the data set were eliminated prior to the analysis. For that purpose,

the variables which were used to tests on |DACC| and SPOS are subject to

winsorization. And other variables used for the calculation of the cross sectional

absolute DACC are not subject to winsorization. All the variables that used to tests

|DACC| and SPOS are representing in the table below,

Table24-1: Winsorization Process

Variable Winsorization

|DACC|

PIFRS

CSize

Growth

AUD

Leverage

CFO

Loss_Dummy

Sector_Dummy

EISSUE

DISSUE

Turn_Over_Ratio

Yes

No

No

Yes

No

Yes

Yes

No

No

Yes

Yes

Yes

The above variables were winsorized under 95% percentile and 5% percentile. The

upper values of the above winsorized variables were replaced by the 95% quartile

level and the lower values of the variables were replaced by the 5% quartile level. The

values used to winsorized are as follows,

45

Table34-2: Winsorization Values

Variable

Percentile Value

5% 95%

|DACC|

Growth

Leverage

CFO

EISSUE

DISSUE

Turn_Over_Ratio

0.0079

-0.3204

0.0711

-0.2290

0.0000

-0.4424

0.1385

0.4346

0.7812

2.4565

0.2222

0.9141

1.5125

2.1919

After doing the winsorization procedure the data set was export to the Statistical

Package for Social Sciences (SPSS) 20.0 and conducted the following statistical

analysis accordingly.

DESCRIPTIVE STATISTICS

Table 4-3 represents the descriptive statistics related to the period before and after the

change of accounting standard pertaining to the test variables and control variables.

When the parameters pertaining to the period before change of mandatory IFRS

adoption and the period after change of mandatory IFRS adoption are compared, a

change is found in terms of averages (mean) and standard deviations (SD). The

summary result of descriptive analysis of |DACC| variables are as follows,

46

Table 4-3: Descriptive Statistics

Test Variable Pre- IFRS ( n= 138) Post- IFRS ( n= 138)

Mean Difference Mean S.D Mean S.D

|DACC|

CSize

Growth

Leverage

CFO

0.1491

14.1943

0.1544

0.7739

0.0383

0.1254

1.3101

0.2620

0.5995

0.1223

0.0997

14.6246

0.0921

0.7402

0.0339

0.0930

1.3301

0.2280

0.6355

0.1015

0.04937

-0.43025

0.06230

0.03373

0.00441

While |DACC| is observed as the earnings management measure, the average in the

pre-IFRS period is 0.1491. According to the prior research observations and

theoretical framework of the study, in order to detect the expected positive change in

accounting quality in the post-IFRS period, the |DACC| is required to be smaller. As

per the results of descriptive analysis, it is evident that this expectation level has been

met. Therefore, the |DACC| reports an average of 0.0997 in the post-IFRS period.

Similarly, the standard deviation of the |DACC| has decreased in post-IFRS period

than pre-IFRS period. When considering continuous independent variables which

affected to the |DACC|, only the average of CSIZE was increased in post-IFRS period

than pre-IFRS period. The averages of the other continuous independent variables like

GROWTH, LEV and CFO has been decreases in post-IFRS period then pre-IFRS

period with parallel to the |DACC|.

The summary result of descriptive analysis of variables used in the calculation of the

cross sectional absolute DACC are as follows,

Table54-4: Descriptive Statistics of Cross Sectional |DACC|

Test

Variable

Pre- IFRS ( n= 138) Post- IFRS ( n= 138) Mean

Difference Mean S.D Mean S.D

1/Assets

ΔRev

ΔRec

PPE

TACC

0.0000022

69.0162

5.2484

0.5407

0.0465

0.0000049

215.8594

46.4215

0.3035

0.2825

0.0000011

41.3061

8.0345

0.5283

0.0318

0.0000016

205.2883

41.9849

0.3281

0.1106

0.0000011

27.7101

-2.7861

0.0125

0.0147

47

When considering continuous independent variables which affected to the calculation

of the cross sectional absolute DACC, only the average of ΔREV was increased in

post-IFRS period then pre-IFRS period. The averages of the other continuous

independent variables such as 1/ASSETS, ΔREC, PPE and TACC have been decreased

in post-IFRS period than pre-IFRS period with parallel to the |DACC|. This decline

may ultimately result the decrease in average of |DACC| from 0.1491 to 0.0997 in

post-IFRS period.

The summary result of descriptive analysis of SPOS variables are as follows,

Table64-5: Descriptive Statistics of Variables Used to Estimate SPOS

Test Variable Pre- IFRS ( n= 138) Post- IFRS ( n= 138)

Mean Difference Mean S.D Mean S.D

CSize

Growth

Leverage

CFO

EISSUE

DISSUE

Turn_Over_Ratio

14.1943

0.1544

0.7739

0.0383

0.0974

0.1712

0.9758

1.3101

0.2620

0.5995

0.1223

0.2736

0.4515

0.6007

14.6246

0.0921

0.7402

0.0339

0.0264

0.1581

0.8720

1.3301

0.2280

0.6355

0.1015

0.1400

0.4526

0.5291

-0.43025

0.06230

0.03373

0.00441

0.07107

0.01312

0.10377

The average values of continuous variables of the SPOS measures have been recorded

in the above table. As per the prior research observations and theoretical framework

of the study, in order to detect the expected positive change in accounting quality in

the post-IFRS period, the SPOS is required to be smaller. When considering

continuous independent variables which affected to the SPOS, only the average of

CSIZE was increased in post-IFRS period than pre-IFRS period. The averages of the

other continuous independent variables like GROWTH, LEV, CFO, EISSUE, DISSUE

and TOR has been decreased in post-IFRS period than pre-IFRS period with parallel

to the SPOS.

48

CORRELATION

Following tables represents the Pearson correlation coefficients of each research

variable. In the correlation matrix, comprehensive findings are presented accordingly.

The purpose of this analysis is to understand the relationships among the variable with

another variable. In this matrix, the analysis of the relationship between the earnings

management measurement variables (|DACC| and SPOS) in pre-IFRS period and

post-IFRS period indicates the results. The test statistic value and P value under the

confidence level considered for each and every variable helps to determine where the

correlation amount is significant or not. Table 4-6 shows the Pearson correlation

results under the confidence level of 95% of the |DACC| pre-IFRS explanatory

variables and table 4-7 shows the Pearson correlation results under the confidence

level of the |DACC| post-IFRS explanatory variables. These analyses show that

change of accounting standards reduce earning management practice.

In accordance with the table 4-6, Pearson correlation value with * indicates the

significant at the 99% confident interval and Pearson correlation value with **

indicates the significant at the 95% confidence level. As per the table below, there is a

relationship to the negative direction between |DACC| and CSIZE, LEV, CFO and

LOSS variable. But only |DACC| and CFO shows the significant relationship where

all of others are not significant. On the other hand, there is a relationship to the

positive direction between |DACC| and GROWTH and AUD variable. But both of

those relationships are not significant according to the test results.

Other than the above mentioned relationships, some of the independent variables have

moderately strong relationships. Generally CSIZE is having moderately positive

relationship with AUD, LEV and CFO (0.400, 0.241 and 0.260 respectively) variables.

On the other hand CSIZE is having moderately negative relationship (-0.299) with

LOSS variable. All of those moderately negative and positive relationships are

significant at stated confident interval as per the table above.

49

Table74-6: Correlation Analysis of Pre-IFRS explanatory variables (|DACC|)

|DACC| CSize Growth AUD Lev CFO LOSS

|DACC| Pearson Correlation Sig. (2-tailed) N

1 .

138

-.018 .119 .012 .892 138

-.063 .461 138

-.398** .000 138

-.082 .337 138

.835 138

.165 138

CSize Pearson Correlation Sig. (2-tailed) N

-.018 .835 138

1 .

138

-.009 .915 138

.400** .000 138

.241** .004 138

.260** .002 138

-.299** .000 138

Growth Pearson Correlation Sig. (2-tailed) N

.119

.165 138

-.009 .915 138

1 .

138

-.102 .232 138

-.033 .702 138

-.198* .020 138

-.038 .658 138

AUD Pearson Correlation Sig. (2-tailed) N

.012

.892 138

.400** .000 138

-.102 .232 138

1 .

138

.023

.792 138

.091

.290 138

-.153 .073 138

Lev Pearson Correlation Sig. (2-tailed) N

-.063 .461 138

.241** .004 138

-.033 .702 138

.023

.792 138

1 .

138

.028

.747 138

-.009 .915 138

CFO Pearson Correlation Sig. (2-tailed) N

-.398** .000 138

.260** .002 138

-.198* .020 138

.091

.290 138

.028

.747 138

1 .

138

-.236** .005 138

LOSS Pearson Correlation Sig. (2-tailed) N

-.082 .337 138

-.299** .000 138

-.038 .658 138

-.153 .073 138

-.009 .915 138

-.236** .005 138

1 .

138

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

50

Table84-7: Correlation Analysis of Post-IFRS explanatory variables (|DACC|)

|DACC| CSize Growth AUD Lev CFO LOSS

|DACC| Pearson Correlation Sig. (2-tailed) N

1 .

138

-.006 .940 138

.286** .001 138

.092

.281 138

.020

.818 138

-.376** .000 138

-.157 .066 138

CSize Pearson Correlation Sig. (2-tailed) N

-.006 .940 138

1 .

138

-.096 .264 138

.317** .000 138

.275** .001 138

.221** .009 138

-.235** .006 138

Growth Pearson Correlation Sig. (2-tailed) N

.286** .001 138

-.096 .264 138

1 .

138

.001

.990 138

.010

.905 138

-.020 .812 138

-.065 .451 138

AUD Pearson Correlation Sig. (2-tailed) N

.092

.281 138

.317** .000 138

.001

.990 138

1 .

138

.030

.728 138

-.161 .058 138

-.017 .839 138

Lev Pearson Correlation Sig. (2-tailed) N

.020

.818 138

.275** .001 138

.010

.905 138

.030

.728 138

1 .

138

-.118 .167 138

.178* .037 138

CFO Pearson Correlation Sig. (2-tailed) N

-.376** .000 138

.221** .009 138

-.020 .812 138

-.161 .058 138

-.118 .167 138

1 .

138

-.327** .000 138

LOSS Pearson Correlation Sig. (2-tailed) N

-.157 .066 138

-.235** .006 138

-.065 .451 138

-.017 .839 138

.178* .037 138

-.327** .000 138

1 .

138

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

51

In the above table Pearson correlation value with * indicates the significant at the 99%

confident interval and Pearson correlation value with ** indicates the significant at the 95%

confident interval. According to the table 4-6, there is a relationship to the negative direction

between |DACC| and CSIZE, CFO and LOSS variable. But only |DACC| and CFO shows the

significant relationship where all of others are not significant. On the other hand, there is a

relationship to the positive direction between |DACC| and GROWTH, AUD and LEV variable.

But only |DACC| and GROWTH shows the significant relationship where all others are not

significant.

Other than the above mentioned relationships, some of the other independent variables have

moderately strong relationships. Generally CSIZE is having moderately positive relationship

with AUD, LEV and CFO (0.317, 0.275 and 0.221 respectively) variables. All those

relationships are significant at stated confident interval as per the table above. On the other

hand CSIZE is having moderately negative relationship (-0.235) with LOSS variable. Other

than that CFO is having moderately negative relationship with LOSS variable (-0.327). Both

of those moderately negative relationships are significant at stated confident interval as per

the table above.

In an overall view, between |DACC| and CSIZE, LOSS is having negative relationship in both

per-IFRS and post-IFRS periods, which is not significant at stated confident interval as per

the table above. On the other hand, between |DACC| and CFO is having negative relationship

in both per-IFRS and post-IFRS periods, which is significant as well. And also, between

|DACC| and AUD is having positive relationship in both per-IFRS and post-IFRS periods,

which is not significant at stated confident interval as per the table above. The relationship

between |DACC| and LEV becomes positive in post-IFRS period, where in pre-IFRS period it

was recorded as negative relationship. Finally, |DACC| and GROWTH shows a positive

relationship in both pre-IFRS and post-IFRS periods, which is not significant in pre-IFRS

adoption period but significant in the post-IFRS period.

Table 4-8 shows the Pearson correlation results and is confidence level of the SPOS pre-IFRS

explanatory variables and table 4-9 shows the Pearson correlation results of SPOS post-IFRS

explanatory variables.

52

Table94-8: Correlation Analysis of Pre-IFRS explanatory variables (SPOS)

SPOS CSize Growth AUD Lev CFO TOR Dissue Eissue

SPOS Pearson Correlation Sig. (2-tailed) N

1 .

138

-.107 .213 138

.006

.944 138

-.181* .034 138

.122

.155 138

-.171* .045 138

-.050 .561 138

-.071 .407 138

.102

.232 138

CSize Pearson Correlation Sig. (2-tailed) N

-.107 .213 138

1 .

138

-.009 .915 138

.400** .000 138

.241** .004 138

.260** .002 138

.051

.550 138

.174* .042 138

.019

.827 138

Growth Pearson Correlation Sig. (2-tailed) N

.006

.944 138

-.009 .915 138

1 .

138

-.102 .232 138

-.033 .702 138

-.198* .020 138

-.003 .974 138

.178* .037 138

.099

.249 138

AUD Pearson Correlation Sig. (2-tailed) N

-.181* .034 138

.400** .000 138

-.102 .232 138

1 .

138

.023

.792 138

.091

.290 138

-.094 .274 138

.097

.256 138

-.021 .805 138

Lev Pearson Correlation Sig. (2-tailed) N

.122

.155 138

.241** .004 138

-.033 .702 138

.023

.792 138

1 .

138

.028

.747 138

.421** .000 138

.140

.101 138

.008

.922 138

CFO Pearson Correlation Sig. (2-tailed) N

-.171* .045 138

.260** .002 138

-.198* .020 138

.091

.290 138

.028

.747 138

1 .

138

.208* .014 138

-.121 .158 138

-.114 .182 138

TOR Pearson Correlation Sig. (2-tailed) N

-.050 .561 138

.051

.550 138

-.003 .974 138

-.094 .274 138

.421** .000 138

.208* .014 138

1 .

138

-.077 .368 138

-.126 .141 138

Dissue Pearson Correlation Sig. (2-tailed) N

-.071 .407 138

.174* .042 138

.178* .037 138

.097

.256 138

.140

.101 138

-.121 .158 138

-.077 .368 138

1 .

138

-.123 .151 138

53

Eissue Pearson Correlation Sig. (2-tailed) N

.102

.232 138

.019

.827 138

.099

.249 138

-.021 .805 138

.008

.922 138

-.114 .182 138

-.126 .141 138

-.123 .151 138

1 .

138

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

54

In the above table Pearson correlation value with * indicates the significant at the 99%

confident interval and Pearson correlation value with ** indicates the significant at the 95%

confident interval. As per the table 4-8, there is a relationship to the negative direction

between SPOS and CSIZE, AUD, CFO, TOR, DISSUE and EISSUE variable. But between

SPOS and AUD, CFO shows a significant relationship where all others are not significant. On

the other hand, there is a relationship to the positive direction between SPOS and GROWTH

and LEV variable. But both of those relationships are not significant according to the test

results.

Other than the above mentioned relationships, some of the independent variables have

moderately strong relationships among themselves. Generally CSIZE is having moderately

positive relationship with AUD, LEV and CFO (0.400, 0.241 and 0.260 respectively)

variables. On the other hand, LEV is having moderately positive relationship (.421) with TOR

variable. All of those moderately negative and positive relationships are significant at stated

confident interval as per the table above.

55

Table104-9: Analysis of Post-IFRS explanatory variables (SPOS)

SPOS CSize Growth AUD Lev CFO TOR Dissue Eissue

SPOS Pearson Correlation Sig. (2-tailed) N

1 .

138

.083

.332 138

-.144 .092 138

.101

.240 138

-.103 .229 138

-.154 .071 138

-.177* .038 138

-.063 .464 138

-.044 .611 138

CSize Pearson Correlation Sig. (2-tailed) N

.083

.332 138

1 .

138

-.096 .264 138

.317** .000 138

.275** .001 138

.221** .009 138

-.011 .897 138

-.082 .339 138

-.058 .497 138

Growth Pearson Correlation Sig. (2-tailed) N

-.144 .092 138

-.096 .264 138

1 .

138

.001

.990 138

.010

.905 138

-.020 .812 138

.172* .043 138

.206* .015 138

-.072 .402 138

AUD Pearson Correlation Sig. (2-tailed) N

.101

.240 138

.317** .000 138

.001

.990 138

1 .

138

.030

.728 138

-.161 .058 138

-.161 .059 138

-.132 .123 138

.003

.969 138

Lev Pearson Correlation Sig. (2-tailed) N

-.103 .229 138

.275** .001 138

.010

.905 138

.030

.728 138

1 .

138

-.118 .167 138

.316** .000 138

.113

.186 138

-.051 .554 138

CFO Pearson Correlation Sig. (2-tailed) N

-.154 .071 138

.221** .009 138

-.020 .812 138

-.161 .058 138

-.118 .167 138

1 .

138

.328** .000 138

-.214* .012 138

-.044 .609 138

TOR Pearson Correlation Sig. (2-tailed) N

-.177* .038 138

-.011 .897 138

.172* .043 138

-.161 .059 138

.316** .000 138

.328** .000 138

1 .

138

-.041 .637 138

-.044 .604 138

Dissue Pearson Correlation Sig. (2-tailed) N

-.063 .464 138

-.082 .339 138

.206* .015 138

-.132 .123 138

.113

.186 138

-.214* .012 138

-.041 .637 138

1 .

138

-.076 .374 138

56

Eissue Pearson Correlation Sig. (2-tailed) N

-.044 .611 138

-.058 .497 138

-.072 .402 138

.003

.969 138

-.051 .554 138

-.044 .609 138

-.044 .604 138

-.076 .374 138

1 .

138

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

57

In the above table Pearson correlation value with * indicates the significant at the 99%

confident interval and Pearson correlation value with ** indicates the significant at the 95%

confident interval. As per the table 4-9, there is a relationship to the negative direction

between SPOS and GROWTH, LEV, CFO, TOR, DISSUE and EISSUE variable. But only

SPOS and TOR shows the significant relationship where all others are not significant. On the

other hand, there is a relationship to the positive direction between SPOS and CSIZE, AUD

variable. But both of those relationships are not significant according to the test results.

Other than the above mentioned relationships, some of the independent variables have

moderately strong relationships among themselves. Generally CSIZE is having moderately

positive relationship with AUD, LEV and CFO (0.317, 0.275 and 0.221 respectively)

variables. On the other hand LEV is having moderately positive relationship (.316) with TOR

variable. And also GRWOTH variable is having moderately positive relationship 0.206) with

DISSUE. Other than that CFO is having moderately negative relationship with DISSUE

variable (-0.214) and moderately positive relationship with TOR variable (0.328). Both of

those moderately negative relationships are significant at stated confidence level as per the

table above.

In an overall manner, between SPOS and DISSUE, EISSUE is having negative relationship in

both per-IFRS and post-IFRS periods, which is not significant at stated confident levels as

per the table above. The relationship between SPOS and GROWTH, LEV becomes positive in

pre-IFRS period, where in post-IFRS period it was recorded as negative relationship in-

between. But this is not significant at stated confident interval as per the table above. Mean

while the relationship between SPOS and CSIZE, AUD becomes negative in pre-IFRS period,

where in post-IFRS period it was recorded as positive relationship in-between. But those

variables are also not significant at stated confident levels except the relationship between

SPOS and AUD in pre-IFRS period. Then, SPOS and CFO states negative relationship in both

pre-IFRS and post-IFRS period, which is significant in pre-IFRS adoption period but become

not significant in the post-IFRS period. Finally, SPOS and TOR indicates a negative

relationship among both pre-IFRS and post-IFRS period, which is not significant in pre-IFRS

adoption period but become significant in the post-IFRS period.

58

UNIVARIATE ANALYSIS

The following tables show the univariate results of the mandatory IFRS adoption on earning

management. Paired sample t-test was used to carry out the analysis. This test is used to

determine whether or not there is a statistical significance related to the average values of the

same sample group for different time periods. Table 4-10 represents the univariate results of

the |DACC| and their independent variables.

Table114-10: Univariate Analysis of |DACC|

Measure N

F T-test Sig. Pre-IFRS Post-IFRS

|DACC|

CSize

Growth

LEV

CFO

138

138

138

138

138

138

138

138

138

138

17.048

0.343

2.828

0.257

5.351

3.714

-2.707

2.107

0.454

0.326

0.000

0.007

0.036

0.650

0.745

When the parameters of the |DACC| variable for the periods before and after the change of

accounting standard are compared, a scientifically significant result was not achieved (t=

3.714). The expected result of the test run is that the switch from domestic accounting

standards to IFRS should have led to an increase in accounting quality by reducing

discretionary accruals (DAs). But the test result indicates an increase in the earnings

management by increasing the DAs. The reason behind this is identified as the decreased

CSIZE (-2.707) after the change of accounting standard. All other variables were increased

after the change of accounting standards, such as GROWTH, LEV and CFO resulted 2.107,

0.257 and 5.351 respectively.

Table 4-11 represents the univariate results of variables used in the calculation of the cross

sectional |DACC|.

59

Table124-11: Univariate Analysis of Cross Sectional |DACC|

Measure N

F T-test Sig. Pre-IFRS Post-IFRS

1/Assets

ΔRev

ΔRec

PPE

TACC

138

138

138

138

138

138

138

138

138

138

13.123

1.991

0.023

1.204

10.247

2.566

1.093

-0.523

0.327

0.568

0.011

0.275

0.601

0.744

0.571

The variables used in the calculation of the cross sectional |DACC| also increased after the

change of accounting standard except ΔREC. Variables 1/ASSETS, ΔREV , PPE and TACC

increased after the change of accounting standard by presenting t value of 2.566, 1.093, 0.327

and 0.568 respectively, where only ΔREC was decreased (t = -0.523) after the change of

accounting standard.

Table 4-12 represents the univariate results of the SPOS and their independent variables.

Table134-12: Univariate Analysis of SPOS

Measure N

F T-test Sig. Pre-IFRS Post-IFRS

SPOS

CSize

Growth

Leverage

CFO

EISSUE

DISSUE

Turn_Over_Ratio

138

138

138

138

138

138

138

138

138

138

138

138

138

138

138

138

1.059

0.343

2.828

0.257

5.351

32.015

0.033

2.482

0.514

-2.707

2.107

0.454

0.326

2.716

0.241

1.523

0.608

0.007

0.036

0.650

0.745

0.007

0.810

0.129

When the parameters of the SPOS variable for the periods before and after the change of

accounting standard are compared, a scientifically significant result was not achieved (t =

0.514). The expected result of the test run is that the switch from domestic accounting

standards to IFRS should have led to an increase in accounting quality by reducing SPOS.

But the test result indicates an increase in the earnings management by increasing the SPOS.

The reason behind this is only CSIZE was decreased (-2.707) after the change of accounting

60

standard. All other variables increased after the change of accounting standards, such

GROWTH, LEV, CFO, EISSUE, DISSUE and TOR resulted 2.107, 0.257, 5.351, 2.716, 0.241

and 1.523 respectively.

The parametric findings of the |DACC| indicate an increase in the earnings management by

increasing the |DACC| after the change of accounting standard at 95% significant level. When

the parameters of the SPOS variable led to an increase in accounting quality by reducing

SPOS after the change of accounting standard, but this is not significant at 95% confidence

interval.

MULTIVARIATE ANALYSIS

The liner regression technique has been used to fit the model which is discussed under table

4-13. The essential four assumptions of the regression have been achieved under model 01

(Refer Annexure 01). In the model 01, the predictors/ independent variables are PIFRS, LEV,

AUD, CFO, GROWTH, CSIZE, LOSS, SEC_1 and SEC_2. Hence the dependent variable is

|DACC|. The following table represents the summary of model 01.

Table14-13: Summary of Model 01

R R Square Adjusted R Square Std. Error of the

estimate Durbin- Watson

0.508 0.258 0.236 0.09874 1.563

The R square represents the goodness of the fit of the model, since the adjusted R square is

23.6%. The following table represents the coefficients of the model 01.

61

Table154-14: Coefficients of Model 01

Model

Unstandardised

coefficients

Standardized

coefficients t Sig.

Collinearity

Statistics

B Std.

Error Beta Tolerance VIF

(Constant)

PIFRS

CSIZE

GROWTH

AUD

LEV

CFO

LOSS

Sec_2

0.110

-0.051

0.004

0.054

0.002

-0.007

-0.439

-0.070

-0.002

0.073

0.012

0.005

0.025

0.017

0.010

0.058

0.019

0.013

-

-0.224

0.051

0.118

0.006

-0.037

-0.436

-0.207

-0.007

1.522

-4.139

0.801

2.183

0.102

-0.671

-7.628

-3.623

-0.126

0.129

0.000

0.424

0.030

0.919

0.503

0.000

0.000

0.900

-

0.947

0.684

0.951

0.849

0.895

0.852

0.853

0.982

-

1.056

1.463

1.052

1.178

1.118

1.174

1.172

1.018

According to the above table the constant test statistic value is 1.522, but it is not significant

at 95% confidence interval. PIFRS, CFO and LOSS variables recorded negative test statistic

values as -4.139, -7.628 and -3.623 respectively and negative standardized coefficient beta as

-0.224, -0.436 and -0.207 respectively. Since the significance value is 0.000, these variables

are significant at 95% confidence level. Even though LEV and Sec_2 represents negative test

statistic values and negative standardized coefficient beta values, those are not significant at

95% confidence level. GROWTH variable represents the strong positive test statistic value

and strong positive standardized coefficient beta, which is significant at 95% confidence

level. CSIZE and AUD represent positive test statistic values and positive standardized

coefficient beta values, but those values are not significant at 95% confidence level.

Collinearity Statistics, VIF factor is the measure of the amount of multicollinearity within the

set of independent variables can cause a number of problems in the understanding the

significant of independent variables in the regression model. As each and every VIF factor of

model 01 lies below the 5 (VIF < 5), there is no multicollinearity within the set.

The following table represents the excluded variables of the model 01 while the fitting the

model.

62

Table164-15: Exclude Variables of Model 01

Model Beta t Sig Partial

Correlation

Collinearity Statistics

Tolerance VIF Minimum

Tolerance

Sec_1 . . . . 0.000 . 0.000

If the above excluded variable reject from the model and run the regression the result will be

the same as above. Therefore the researcher decided to exclude the SEC_1 from the model,

since it is not fitted as expected.

The model 2 has been developed to fit the SPOS equation. The essential four assumptions of

the regression have been satisfied under this model (Refer Annexure 02). In the model 02, the

predictors/ independent variables are PIFRS, LEV, AUD, CFO, GROWTH, CSIZE, TOR,

DISSUE, EISSUE, SEC_1 and SEC_2. Hence the dependent variable is SPOS. The following

table represents the summary of the measurements of model 02.

Table174-16: Summary of Model 02

R R Square Adjusted R Square Std. Error of the

estimate Durbin- Watson

0.233 0.054 0.018 0.232 1.869

The R square represents the goodness of the fit of the model, since the adjusted R square is

0.018. The following table represents the coefficients of the model 02

63

Table184-17: Coefficients of Model 02

Model

Unstandardised

coefficients

Standardized

coefficients t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

(Constant)

PIFRS

CSIZE

GROWTH

AUD

LEV

CFO

TOR

DISSUE

EISSUE

Sec_2

0.035

-0.024

0.009

-0.057

-0.056

0.014

-0.365

-0.035

-0.50

0.025

-0.001

0.170

0.029

0.012

0.059

0.041

0.026

0.141

0.029

0.033

0.066

0.031

-

-0.052

0.052

-0.060

-0.090

0.038

-0.175

-0.086

-0.095

0.024

-0.003

0.206

-0.839

0.731

-0.962

-1.379

0.543

-2.596

-1.228

-1.514

0.382

-0.046

0.837

0.402

0.466

0.337

0.169

0.588

0.010

0.221

0.131

0.703

0.963

-

0.919

0.705

0.909

.840

0.739

0.785

0.730

0.898

0.939

0.982

-

1.089

1.418

1.100

1.190

1.353

1.274

1.371

1.114

1.065

1.018

According to the above table the constant test statistic value is 0.206, since it is not

significant at 95% confidence interval. CFO recorded negative test statistic values and

respectively and negative standardized coefficient beta. Since the significance value is 0.000,

these variables are significant at 95% confidence level. Even though PIFRS, GROWTH,

AUD, TOR, DISSUE and SEC_2 represents negative test statistic values and negative

standardized coefficient beta values, those are not significant at 95% confidence level.

CSIZE, LEV and EISSUE represent positive test statistic values and positive standardized

coefficient beta values, but those values are not significant at 95% confidence level.

Collinearity Statistics, VIF factor is the measure of the multicollinearity within the set of

independent variables which can cause a number of problems in the understanding the

significant of individual variables in the regression model. Each and every VIF factor of

model 01 lying below the 5 (VIF < 5), indicates the model is free from multicollinearity.

The following table represents the excluded variables of the model 02 while the fitting the

model.

64

Table194-18: Exclude Variables of Model 02

Model Beta t Sig Partial

Correlation

Collinearity Statistics

Tolerance VIF Minimum

Tolerance

Sec_1 . . . . 0.000 . 0.000

If the above excluded variable reject from the model and run the regression the result will be

the same as above. Therefore the researcher decided to exclude the SEC_1 from the model,

since it is not fitted as expected.

Since some regression results are not significant at model 01 and model 02, the researcher

conducted a stepwise regression, which is a semi-automated process of building a model by

successively adding or removing variables based solely on the test statistic of their estimated

coefficients.

Under the stepwise regression there have been developed four models for the |DACC|. Model

summary and coefficient of each and every model is represents. Table 4-19 shows the

summary of four models separately.

Table204-19: Summary of |DACC| Models

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Durbin-

Watson

1

2

3

4

.376a

.438b

.492c

.505d

.141

.192

.242

.255

.138

.186

.234

.244

.10488

.10189

.09887

.09820

.

.

.

1.565

a. Predictors: (Constant), CFO

b. Predictors: (Constant), CFO, PIFRS

c. Predictors: (Constant), CFO, PIFRS, LOSS

d. Predictors: (Constant), CFO, PIFRS, LOSS, Growth

e. Dependent Variable: |DACC|

The R square represents the goodness of the fit of the model, the adjusted R square of the

model 01 to model 04 are 0.138, 0.186, 0.234 and 0.244 respectively. The following table

represents the coefficients of the four models.

65

Table214-20: Coefficients of |DACC| Models

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.

Error Beta

Tolera-

nce VIF

1 (Constant)

CFO

.138

-.378

.007

.056

.

-.376

20.808

-6.706

.000

.000

.

1.000

.

1.000

2 (Constant)

CFO

PIFRS

.164

-.383

-.051

.009

.055

.012

.

-.380

-.226

18.346

-6.984

-4.161

.000

.000

.000

.

1.000

1.000

.

1.000

1.000

3 (Constant)

CFO

PIFRS

LOSS

.176

-.447

-.052

-.079

.009

.055

.012

.019

.

-.444

-.230

-.232

19.245

-8.084

-4.359

-4.230

.000

.000

.000

.000

.

.924

.999

.924

.

1.083

1.001

1.082

4 (Constant)

CFO

PIFRS

LOSS

Growth

.167

-.430

-.048

-.075

.053

.010

.055

.012

.019

.024

.

-.427

-.215

-.222

.117

16.600

-7.745

-4.065

-4.050

2.180

.000

.000

.000

.000

.030

.

.905

.982

.917

.962

.

1.105

1.018

1.091

1.040

According to the above table each and every variable of model 01, model 02 and model 03

recorded the negative test statistic values and negative standardized coefficient beta. Since

the significance value is 0.000, those variables are significant at 95% confidence level. In

model 04 GROWTH represents positive test statistic values and positive standardized

coefficient beta while all other variables represents negative test statistic values and negative

standardized coefficient beta and all other variables are significant at 95% confidence level.

Collinearity Statistics, VIF factor is the measure of the amount of multicollinearity within the

set of independent variables can cause a number of problems in the understanding the

significant of individual variables in the regression model. As each and every VIF factor of

four models lying below the 5 (VIF < 5), it indicates that the model is free from

multicollinearity issue.

The following table represents the excluded variables of the four models while the fitting

stepwise regression.

66

Table224-21: Excluded Variables of |DACC| Models

Model Beta

In T Sig.

Partial

Correlatio

n

Collinearity Statistics

Tolera

nce VIF

Minimum

Tolerance

1 PIFRS

CSize

Growth

AUD

LEV

LOSS

Sec_1

Sec_2

-.226a

.043a

.163a

.029a

-.035a

-.228a

.032a

-.032a

-4.161

.742

2.929

.525

-.617

-4.026

.564

-.564

.000

.459

.004

.600

.538

.000

.573

.573

-.244

.045

.175

.032

-.037

-.237

.034

-.034

1.000

.945

.986

1.000

.998

.924

.995

.995

1.000

1.058

1.015

1.000

1.002

1.082

1.005

1.005

1.000

.945

.986

1.000

.998

.924

.995

.995

2 CSize

Growth

AUD

LEV

LOSS

Sec_1

Sec_2

.085b

.136b

.036b

-.041b

-.232b

.031b

-.031b

1.500

2.478

.660

-.753

-4.230

.575

-.575

.135

.014

.510

.452

.000

.566

.566

.091

.149

.040

-.046

-.248

.035

-.035

.917

.969

.999

.998

.924

.995

.995

1.090

1.032

1.001

1.002

1.082

1.005

1.005

.917

.969

.999

.998

.924

.995

.995

3 CSize

Growth

AUD

LEV

Sec_1

Sec_2

.037c

.117c

.014c

-.024c

.018c

-.018c

.649

2.180

.270

-.449

.337

-.337

.517

.030

.787

.654

.736

.736

.039

.131

.016

-.027

.020

-.020

.875

.962

.989

.992

.992

.992

1.143

1.040

1.011

1.008

1.008

1.008

.875

.905

.915

.918

.917

.917

4 CSize

AUD

LEV

Sec_1

Sec_2

.042d

.022d

-.023d

.009d

-.009d

.743

.420

-.429

.170

-.170

.458

.675

.668

.865

.865

.045

.026

-.026

.010

-.010

.874

.985

.991

.986

.986

1.145

1.015

1.009

1.015

1.015

.873

.902

.904

.900

.900

a. Predictors in the Model: (Constant), CFO

b. Predictors in the Model: (Constant), CFO, PIFRS

c. Predictors in the Model: (Constant), CFO, PIFRS, LOSS

d. Predictors in the Model: (Constant), CFO, PIFRS, LOSS, Growth

e. Dependent Variable: |DACC|

The variables stated in the table 4-21 above, are excluded from the four models, because the

variables are not significant at 95% confidence interval.

67

According to the stepwise regression model the researcher attempt to choose the model 04,

hence it consist of relatively more number of variables with test statistic values and

standardized coefficient beta. Therefore the new model can be illustrated as follows,

|DACCit| = β0 + β1PIFRSit + β3GROWTHit + β6CFOit + β7LOSSit

Under the stepwise regression there have been developed only one model for the SPOS.

Model summary and coefficient of the model is represents separately. Table 4-22 shows the

summary of the model.

Table234-22: Summary of SPOS Model

Model R R Square Adjusted R

Square

Std. Error of the

Estimate Durbin-Watson

1 .163a .027 .023 .231 1.837

a. Predictors: (Constant), CFO

b. Dependent Variable: SPOS

The R square represents the goodness of the fit of the model, since the adjusted R square of

the model is 0.023. The following table represents the coefficients of the selected model.

Table244-23: Coefficients of SPOS Models

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

Collinearity

Statistics

B Std.

Error Beta

Toleran

-ce VIF

1 (Constant)

CFO .070

-.341

.015

.124

.

-.163

4.802

-2.739

.000

.007

.

1.000

.

1.000

According to the above table, each and every variable of model recorded negative test

statistic values and negative standardized coefficient beta. Since the significant value is

0.007, it is significant at 95% confidence level. Collinearity Statistics, VIF factor is the

measure of the amount of multicollinearity within the set of independent variables can cause

a number of problems in the understanding the significant of individual variables in the

68

regression model. As each and every VIF factor of four models lying below the 5 (VIF < 5),

the model is free from multicollinearity issue.

The following table represents the excluded variables of the four models while the fitting

stepwise regression.

Table254-24: Excluded Variables of SPOS Models

Model Beta In T Sig. Partial

Correlation

Collinearity Statistics

Tolera

nce VIF

Minimum

Tolerance

1 PIFRS

CSize

Growth

AUD

LEV

TOR

DISSUE

EISSUE

Sec_1

Sec_2

-.034a

.018a

-.076a

-.056a

.007a

-.064a

-.096a

.045a

-.007a

.007a

-.574

.292

-1.261

-.940

.117

-1.040

-1.587

.758

-.115

.115

.567

.771

.208

.348

.907

.299

.114

.449

.908

.908

-.035

.018

-.076

-.057

.007

-.063

-.096

.046

-.007

.007

1.000

.945

.986

1.000

.998

.933

.974

.992

.995

.995

1.000

1.058

1.015

1.000

1.002

1.072

1.027

1.008

1.005

1.005

1.000

.945

.986

1.000

.998

.933

.974

.992

.995

.995

The above variables are excluded from the model, because, the variables are not significant at

95% confidence interval. Therefore the new model can be illustrated as follows,

SPOSit= β0 + β6CFOit

According to the stepwise regression model the researcher attempt to choose that model,

hence it consists of relatively significant variables with test statistic values and standardized

coefficient beta.

69

CHAPTER 05: CONCLUSION & RECOMMENDATION

FINDINGS AND CONCLUTION

Number of researches have already conducted regarding to the effects of mandatory IFRS

adoption and their determinations in every country. But only few effective studies have been

conducted in relation to the effects of mandatory IFRS adoption in the Sri Lankan context. In

this research, the focus is to carry out an explanatory study to identify whether the mandatory

IFRS adoption impacts the earning management of the firms listed in Colombo Stock

Exchange (CSE) in Sri Lanka. Therefore the research expects to investigate the impact on

earning management through the mandatory adoption of IFRS, affected towards the quality

of earnings.

It is clear based on prior researches (Baig & Khan 2016), that conflicting outcomes have been

reported in this area. Some of them discovered that after IFRS become mandatory the quality

of accounting affected negatively and others come to a conclusion that the mandatory IFRS

adoption has proved the quality of accounting by increasing both information quality and

accounting comparability (Ames 2013) . Therefore most of the studies have identified that

earning management is not significantly improved by the post adoption, but the timely loss

recognition and the value relevance of major balance sheet components changes post

adoption.

When analysing the descriptive statistics of |DACC|, a decrease is seen in the post-IFRS

period than pre- IFRS adoption period. Similarly, the standard deviation has also been

decreased in post-IFRS period than pre- IFRS adoption period. These results detected the

expected positive change in accounting quality in the post-IFRS period. Because, variables

which affected to the calculation of the cross sectional absolute DACC has been ultimately

affected to that result. The averages of the continuous independent variables used in the

calculation of the cross sectional absolute DACC such as 1/ASSETS, ΔREC, PPE and TACC

has also been decreased in post-IFRS period than pre-IFRS period, similar to the |DACC|.

When analysing the descriptive statistics in accordance with SPOS variables, it is conclude

that the mean and the standard deviation has been decreased in post-IFRS period than pre-

IFRS adoption period. These results detected the expected positive change in accounting

quality in the post-IFRS period. According to the Uyar (2013), expected positive change in

accounting quality in the post- IFRS period is indicated when the |DACC| and SPOS

70

variables become smaller and therefore it can be concluded that the expected positive change

in accounting quality in the post-IFRS period has been achieved.

According to the Pearson correlation, relationship between |DACC| and other independent

variables in post-IFRS period is lower than the relationship between |DACC| and other

independent variables in the pre-IFRS period. These results also detected expected positive

change in accounting quality in post-IFRS period. This may conclude that expected positive

change in accounting quality in post-IFRS period has been achieved accordingly. Most of the

independent variables of the |DACC| model are negatively related with the dependent

variable. Also, Most of the independent variables of the SPOS model are also negatively

related with the dependent variable. This implies that SPOS of the post-IFRS period is lower

than the |DACC| of the pre-IFRS period. Since this result is not significant, the researcher is

unable to conclude on the results as above. According to the Uyar observation (2013, p. 470),

the spearmen correlation resulted relationships in the negative direction between PIFRS and

|DACC| and the same relationship orientation is observed between PIFRS and SPOS.

Paired sample t-test was used to conduct a univariate analysis. When analysing the periods of

before and after the change of accounting standards of the |DACC| variables, a statistically

significant result was not achieved. The test result indicates an increase in the earnings

management by increasing the discretionary accruals (DAs). But the expected result of the

test run is that the switch from domestic accounting standards to IFRS should have led to an

increase in accounting quality by reducing DAs. When analysing the periods before and after

the change of accounting standards of the SPOS variables, a statistically significant result was

not achieved. The test result indicates an increase in the earnings management by increasing

the SPOS. But the expected result of the test run is that the switch from domestic accounting

standards to IFRS should lead to an increase in accounting quality by reducing SPOS. Uyar

observation in 2013 states that, ‘when the parameters of the |DACC| variable for the periods

before and after the change of accounting standard are compared, a scientifically significant

result was achieved’. Furthermore, when analysing the periods before and after the change of

accounting standards of the SPOS variables, a scientifically significant result was achieved

which indicates an increase in the earnings management by increasing the SPOS (Uyar 2013).

71

Regression analysis was used to carry out the multivariate analysis. According to the |DACC|

regression model (Model 01) only four variables had a significant relationship; PIFRS,

GROWTH, CFO and LOSS. While PIFRS, CFO and LOSS recorded negative standardized

coefficient beta and test value, Growth recorded positive standardized coefficient beta and

test value. On the other hand in the SPOS regression model (Model 02) only one variable

recorded the significant relationship, which was CFO. All other variables were not significant

at 95% confident level. The CFO recorded negative standardized coefficient beta and test

value. Most of the variables recorded the significant, negative standardized coefficient beta

and test value in both |DACC| and SPOS models (Uyar 2013).

According to the Uyar observation in 2013, this kind of situation leads that with the switch to

IFRS, the companies ongoing to perform in a more responsive and appropriate way in terms

of the rules and principles of the international markets. As per the Uyar (2013) states, ‘the

post-IFRS period significant results were obtained in the value of relevance in terms of both

price model and return model’. The impact of the improvement of accounting quality on the

decrease of errors related to earnings predictions is a highly significant indicator. Hence, as

per the descriptive analysis, correlation analysis, univariate analysis and multivariate analysis

done by the researcher, it is concluded that earning management has been decreased due to

the mandatory IFRS adoption, in the case of Sri Lanka.

LIMITATIONS AND RECOMMENDATONS

Several limitations have been identified by the researcher according to the objective of this

study, which is to determine effect of mandatory IFRS adoption on earning management

towards the earning quality.

The first identified limitation is lack of sufficient time series of data to achieve the effect of

IFRS (SLFRS) adoption. Hence, the study conducted by obtaining data from 2008/09,

2009/10, 2011/12 as pre-IFRS adoption period and 2012/13, 2013/14, 2014/15 as post-IFRS

adoption period. But the pre-IFRS adoption period lies before 2008/09. Post-IFRS adoption

time period is significant, because the firms may need more time to organize, formulate and

implement IFRS.

72

As per Scott’s observation in 2009 (cited in Udayakumara & Weerathunga, unpub.) the

managers may be attractive in smoothing earnings to provide a true indication of a firm’s

future cash flows. The second limitation of this study is that the procedures which were

expected to conduct, were not revealed the exact performance of the firm or do not provide a

superior signal of future cash flows towards the smoothness in earning management.

Additionally, the sample of the study only included firms listed at Colombo Stock Exchange

(CSE) which are the publicly listed corporate bodies, and did not include government

corporations, private companies and etc.

The final limitation is that the data collected by using the annual reports and other publicly

available data did not prove the realistic situation of the company as management accounting

information do. To overcome this limitation the researcher had to have access to the internal

data of the firms which is impossible to attain practically.

Along with these results it is recommended that an assessment of IFRS in terms of adoption

and bringing together was also needed. To overcome the first limitation stated above the

future researchers have to consider the sufficient time series of data to achieve the effect of

IFRS. To overcome the second limitation stated above the future researchers have to refer the

management accounts parallel to the general purpose financial statements. To overcome the

third limitation stated above the future researchers have to consider publicly listed corporate

bodies as well as the government corporations, private companies and etc.

73

REFERENCES

74

Ahmed, AS, Neel, M & Wang, D 2013, 'Does mandatory adoption of IFRS improve accounting

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APPENDIX

79

APPENDIX 01

The liner regression technique has been used to fit the model which is discussed under figure

3-1 in the methodology chapter. There are essential four assumptions of the regression should

have achieved when fitting the model. Those four assumptions are as follows.

1. The residuals are normally distributed.

2. The variance should equal to zero.

3. There is no auto correlation value.

4. Randomness of errors.

Here the researcher matched the model 01 with those four assumptions. In the model 01, the

predictors/ independent variables are PIFRS, LEV, AUD, CFO, GROWTH, CSIZE, LOSS,

SEC_1 and SEC_2. Hence the dependent variable is |DACC|.

1. The residuals are normally distributed.

The liner regression analysis requires all variables to be multivariate normal. In the |DACC|

regression model the normal P-P plot can be illustrate as follows,

Figure8A-1: Normal P-P plot of |DACC|

80

According to the figure A-1 above the residuals are lies closer to the line therefore the

residuals are normally distributed. Anderson Darlin test value is sufficiently large (More than

5%) reject H0.

H0; population is normal

H1; Population is not normal

Since the first assumption of the regression is proved.

2. The variance should equal to zero (Expectation of residuals).

Expectation of residuals should be zero. Here the model recorded the residual value of 0.003.

Approximately it is 0. Therefore the assumption proved.

3. There is no auto correlation value.

Thirdly, linear regression analysis requires that there is little or no correlation in the data.

Autocorrelation occurs when the residuals are not independent from each other. For the

purpose of testing autocorrelation Durbin Watson statistic was used. This model recorded the

value of 1.563 for Durbin Watson which is illustrated below,

Table26A-1: Durbin Watson statistic of |DACC|

Adjusted R Square Std. Error of the estimate Durbin- Watson

0.236 0.09874 1.563

In the model 01, the predictors/ independent variables are PIFRS, LEV, AUD, CFO,

GROWTH, CSIZE, LOSS, SEC_1 and SEC_2. Since SEC_1 was excluded from the model,

there are only eight independent variables (K=8) are considered while checking the Durbin

Watson table. Since the value is lies between DU and 4-DU, Accept the H0; there is no

autocorrelation.

4. Randomness of errors.

Randomness of errors means discrepancy or uncontrolled variation between an observed

value and the value and the value predicted by a specification, standard or model. In this

model it states equal variances, means randomness of the residuals.

81

Figure9A-2: Randomness of errors of |DACC|

APPENDIX 02

Here the researcher matched the model 02 regression model with those four assumptions. In

the model 02, the predictors/ independent variables are PIFRS, LEV, AUD, CFO, GROWTH,

CSIZE, TOR, DISSUE, EISSUE, SEC_1 and SEC_2. Hence the dependent variable is SPOS.

1. The residuals are normally distributed.

In the SPOS regression model the normal P-P plot can be illustrate as follows,

260240220200180160140120100806040201

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

Observation Order

Resi

dual

Versus Order(response is |DACC|)

82

Figure10A-3: Normal P-P plot of SPOS

According to the figure A-3 above the residuals are lies closer to the line therefore the

residuals are normally distributed. Since the first assumption of the regression can be proved.

2. The variance should equal to zero.

Expectation of residuals should be zero. Here the model recorded the residual value of

0.0000. Almost it is 0. Therefore the assumption proved.

3. There is no auto correlation value.

This model recorded the value of 1.869 for Durbin Watson which is illustrated below,

Table27A-2: Durbin Watson statistic of |DACC|

Adjusted R Square Std. Error of the estimate Durbin- Watson

0.018 0.232 1.869

In the model 02, the predictors/ independent variables are PIFRS, LEV, AUD, CFO,

GROWTH, CSIZE, TOR, DISSUE, EISSUE, SEC_1 and SEC_2. Since SEC_1 was

83

excluded from the model, there are only ten independent variables (K=10) are considered

while checking the Durbin Watson table. Since the value is lies between DU and 4-DU,

Accept the H0; there is no autocorrelation.

4. Randomness of errors.

In this model it states no equal variances, means there is no randomness of the residuals. Figure11A-4: Randomness of errors of SPOS

260240220200180160140120100806040201

1.00

0.75

0.50

0.25

0.00

Observation Order

Resi

dual

Versus Order(response is SPOS (Dum))