Download - Earnings Quality and Earnings Management: An Empirical

Earnings Quality and Earnings Management: An Empirical

Analysis Of The Provision For Credit Loss On Trade

Receivables Amongst FTSE 350 Companies

Jonathon Butler

This dissertation is submitted in partial fulfilment of the requirements for the degree of

Master of Business in Accounting, Waterford Institute of Technology.

Research Supervisor: Mr. John Casey, FCA, MSc (Finance)

August 2012

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ABSTRACT

Amidst persistent global economic uncertainty, an on-going sovereign debt crisis

across Europe and an environment of elevated and deteriorating credit risk, companies

continue to face numerous challenges. One such challenge comprises the risk of credit

loss on trade receivables, which companies provide for through a specific provision.

Prior empirical research has documented extensively the existence of earnings

management through discretionary accruals. This cross-sectional study examines the

existence and determinants of abnormal provision for credit loss on trade receivables

in the context of both earnings quality and earnings management.

While traditional determinants of earnings management including capital market,

contractual, performance, governance and auditor related variables are examined, the

capital market response to instances of extreme abnormal provision for credit loss on

trade receivables is also considered. Consistent with prior earnings management

research, correlation and regression analyses are utilised to determine the extent of

relationships between abnormal provision and these variables.

The mean level of abnormal underprovision of -9.9% provides strong evidence of

provisioning practice at sharp variance with the current credit risk environment, while

the results of regression analyses provide strong evidence supporting the debt

hypothesis of positive accounting theory.

Increasing levels of gearing and increasing gross margins are identified as

significantly explaining abnormal underprovision, while strong evidence supporting

the mitigating effects of robust corporate governance structures on abnormal

underprovision is also identified, where the board of directors is comprised of an

increasing proportion of INEDs.

Further analysis confirms that those companies with extreme abnormal

underprovision experience a significantly inferior post financial year end stock price

performance relative to those companies with extreme abnormal overprovision.

This result suggests that capital markets, in identifying lower quality earnings and

discounting stocks where accounting abnormalities have been identified, may mitigate

the effects of earnings management activity.

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ii

ACKNOWLEDGEMENTS

The completion of this dissertation signifies the end of my formal third level

education. However, its completion and my four years at W.I.T. would not have been

possible without assistance, guidance and inspiration from so many people.

I am truly grateful to Mr. John Casey for his extensive input, forbearance, practical

advice and guidance, without which, completion of this dissertation would not have

been possible.

I extend sincere thanks to PricewaterhouseCoopers (Waterford) for their financial

support at both undergraduate level and in the completion of the MBS in Accounting

programme.

To all of the lecturing and support staff on both the MBS in Accounting and

BA (Hons) in Accounting programmes - I am forever grateful for your support, advice

and assistance in helping me during my four years at WIT.

To all of my fellow classmates – I wish to thank you for a wonderful year.

The experience has left me with great memories and I have been privileged to work

with a group of such talented and determined people.

To all who assisted in reviewing this dissertation – your efforts and support are

sincerely appreciated.

Finally, yet most importantly, I extend heartfelt thanks to my family: to Mum,

Dad and Joanne. You have remained steadfast in supporting me throughout the years,

helping me in all of my achievements to date. I will forever be grateful for your

support, guidance and prayers.

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ETHICAL DECLARATION

Specifically excluded from Turnitin© upload – included in hard copy version.

_____________________________ _____________________________

Jonathon Butler Date

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TABLE OF CONTENTS

Abstract ........................................................................................................................... i

Acknowledgements ........................................................................................................ ii

Ethical Declaration ........................................................................................................ iii

Table of Contents .......................................................................................................... iv

List of Figures ............................................................................................................... ix

List of Tables ................................................................................................................ xi

List of Appendices ...................................................................................................... xiii

List of Abbreviations .................................................................................................. xiv

Chapter One: Introduction ......................................................................................... 1

1.1 Introduction .............................................................................................................. 2

1.2 Research Rationale and Context .............................................................................. 3

1.3 Research Questions and Research Objectives ......................................................... 4

1.4 Research Methodology and Limitations .................................................................. 5

1.5 Dissertation Structure: Diagrammatic Overview ..................................................... 6

Chapter Two: Literature Review ............................................................................... 7

2.1 Introduction .............................................................................................................. 8

2.2 Introduction to Earnings Quality ............................................................................. 9

2.2.1 Earnings Quality as a Concept ......................................................................... 9

2.2.2 Measures of Earnings Quality .......................................................................... 9

2.2.3 Earnings Quality and Earnings Management ................................................ 10

2.3 Introduction to Earnings Management................................................................... 11

2.3.1 Earnings Management as a Concept .............................................................. 11

2.3.2 Differentiation between Earnings Management and Manipulation ............... 11

2.3.3 Accounting Policy Flexibility and Earnings Management ............................ 11

2.3.4 Impact from Earnings Management Activity ................................................ 12

2.3.5 Accrual Accounting, Earnings Management and Accounting Earnings ....... 12

2.4 Earnings Management – Theoretical Perspectives ................................................ 12

2.4.1 Introduction to Theoretical Perspectives ....................................................... 12

2.4.2 Signalling Theory and Higher Quality Firms ................................................ 13

2.4.3 Signalling Theory and Lower Quality Firms ................................................ 13

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Chapter Two: Literature Review (Continued)

2.4.4 Positive Accounting Theory and Earnings Management ............................... 14

2.4.5 Agency Theory and Earnings Management .................................................. 15

2.4.6 Agent Specific Motives for Earnings Management ....................................... 15

2.5 Conclusion ............................................................................................................. 16

Chapter Three: Literature Review........................................................................... 17

3.1 Introduction ............................................................................................................ 18

3.2 Earnings Management and the Financial Condition of a Firm .............................. 19

3.3 The Determinants of Earnings Management ......................................................... 19

3.3.1 Contracting Motives and Earnings Management .......................................... 20

3.3.2 Executive Level Compensation and Earnings Management ........................ 20

3.3.3 Debt Contract Motives and Earnings Management ...................................... 21

3.3.4 Earnings Expectations and Earnings Management ....................................... 22

3.3.5 Regulatory Motives and Earnings Management ........................................... 22

3.3.6 Corporate Governance Structures and Earnings Management ..................... 23

3.3.7 Audit Committee and Earnings Management ............................................... 24

3.3.8 Auditor Type, Auditor Remuneration and Earnings Management ............... 24

3.3.9 Revenue Manipulation, Deferred Revenue and Trade Receivables ............. 25

3.4 The Response of Capital Markets .......................................................................... 26

3.4.1 Earnings Quality and Stock Price Performance ............................................ 26

3.4.2 Equity Offerings, Earnings Management and Stock Price Performance ...... 27

3.4.3 Magnitude of Capital Market Based Earnings Management ........................ 27

3.5 Conclusion ............................................................................................................. 27

Chapter Four: Literature Review ............................................................................ 28

4.1 Introduction ............................................................................................................ 29

4.2 The Provision for Doubtful Receivables: Model Development ........................... 30

4.2.1 The Provision for Doubtful Receivables: McNichols and Wilson (1988) .... 30

4.2.2 The Provision for Doubtful Receivables: Lev and Thiagarajan (1993) ......... 30

4.2.3 Manipulation of Trade Receivables: Ricci (2011) ....................................... 31

4.3 The Provision for Doubtful Receivables – IASB Accounting Guidance ............. 31

4.3.1 IAS 39: Section 58 – 59 ................................................................................ 31

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Chapter Four: Literature Review (Continued)

4.3.2 IFRS 7: Section 7.16 and 7.37 ....................................................................... 31

4.4 Literature Review Conclusion .............................................................................. 32

Chapter Five: Research Methodology ..................................................................... 33

5.1 Introduction ............................................................................................................ 34

5.2 Research Rationale ................................................................................................. 35

5.3 Research Questions ................................................................................................ 35

5.4 Research Objectives ............................................................................................... 36

5.5 Research Hypotheses for Testing ........................................................................... 36

5.5.1 Objective One: Hypothesis ........................................................................... 36

5.5.2 Objective Two: Hypotheses .......................................................................... 37

5.5.3 Objective Three: Hypothesis ......................................................................... 41

5.6 Research Approach ................................................................................................ 41

5.6.1 Dependent Variable: Earnings Quality and Earnings Management ............. 42

5.7 Sample Selection Process ...................................................................................... 42

5.7.1 Sample Selection Context ............................................................................. 42

5.7.2 Sample Selection Refinement ........................................................................ 43

5.7.3 Sample Selection Refinement – Specific Elimination Procedures ................ 43

5.8 Data Sources .......................................................................................................... 44

5.9 Data Measures Utilised .......................................................................................... 46

5.9.1 Primary Dependent Variable ......................................................................... 46

5.9.2 Additional Variables and Measures ............................................................... 47

5.10 Data Validity and Reliability ............................................................................... 47

5.11 Testing Procedures ............................................................................................... 48

5.12 Limitations ........................................................................................................... 48

5.13 Conclusion ........................................................................................................... 49

Chapter Six: Research Findings ............................................................................... 50

6.1 Introduction ............................................................................................................ 51

6.2 Analysis of Identified Outliers ............................................................................... 52

6.3 Magnitude of Abnormal Provision for Credit Loss on Trade Receivables ........... 52

6.3.1 Descriptive Statistics ...................................................................................... 52

6.3.2 Univariate Analysis: Simple Linear Regression Analysis ............................ 53

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Chapter Six: Research Findings (Continued)

6.4 Determinants of Abnormal Provision for Credit Loss on Trade Receivables ....... 54

6.4.1 Compliance with Underlying OLS Regression Analysis Assumptions ......... 54

6.4.2 Descriptive Statistics: Independent Explanatory Variables .......................... 54

6.4.3 Univariate Analysis: Simple Linear Regression Analysis ............................ 56

6.4.4 Multivariate Analysis: Multiple Regression Analysis .................................. 58

6.4.5 H2 to H15: Summary Findings ....................................................................... 64

6.5 Stock Price Performance of Extreme Abnormal Providers .................................. 65

6.5.1 Descriptive Statistics ...................................................................................... 65

6.5.2 Multiple Regression Analysis: Stock Price Performance Significance ........ 66

6.6 Conclusion ............................................................................................................. 67

Chapter Seven: Discussion ........................................................................................ 68

7.1 Introduction ............................................................................................................ 69

7.2 Magnitude of Abnormal Provision for Credit Loss on Trade Receivables ........... 70

7.2.1 Provisioning Activity at Variance with Credit Risk Environment ............... 70

7.2.2 Increased Credit Delinquency: Downside Risk of Elevated Write-Offs ...... 70

7.2.3 Regulatory Considerations ............................................................................ 71

7.3 Determinants of Abnormal Provision for Credit Loss on Trade Receivables ....... 71

7.3.1 Capital Market Variables – Limited Evidence .............................................. 71

7.3.2 Contractual Variables – Significant Evidence ............................................. 72

7.3.3 Performance Variables – Significant Evidence ........................................... 73

7.3.4 Governance Variables – Significant Evidence ............................................. 74

7.3.5 Auditor Variables – Mixed Evidence ........................................................... 75

7.3.6 Non Hypothesised Factors ............................................................................ 75

7.4 Stock Price Performance of Extreme Abnormal Providers .................................. 76

7.4.1 Evidence Supporting the Efficient Market Hypothesis ................................ 76

7.4.2 Capital Markets: A Potential Instrument for Effective Regulation .............. 76

7.4.3 Non Hypothesised Factors ............................................................................ 77

7.5 Conclusion ............................................................................................................. 77

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Chapter Eight: Conclusion ....................................................................................... 78

8.1 Introduction ............................................................................................................ 79

8.2 Research Questions, Research Objectives and Findings ...................................... 80

8.3 Limitations of Research ........................................................................................ 81

8.4 Recommendations for Practitioners ...................................................................... 81

8.4.1 Auditors ......................................................................................................... 81

8.4.2 International Accounting Standards Board – Standard Setters ..................... 82

8.4.3 Capital Market Participants ........................................................................... 82

8.5 Recommendations for Future Research ................................................................ 82

8.6 Conclusion ............................................................................................................ 83

References ................................................................................................................... 84

Appendices .................................................................................................................. 95

The total word count, excluding both figures and tables, is 16,495

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LIST OF FIGURES

Chapter One:

1.1 Dissertation Structure: Diagrammatic Overview ..................................................... 6

Chapter Two:

2.1 Income Smoothing Hypothesis .............................................................................. 10

2.2 Signalling Theory and Earnings Management ....................................................... 13

2.3 Positive Accounting Theory and Earnings Management ....................................... 14

2.4 Agency Theory and Earnings Management ........................................................... 15

Chapter Three:

3.1 Stock Price Performance: Posting Earnings Manipulation .................................... 26

Chapter Five:

5.1 Capital Market Determinants of Earnings Management ....................................... 37

5.2 Contractual Determinants of Earnings Management ............................................. 37

5.3 Performance Related Determinants of Earnings Management .............................. 38

5.4 Governance Specific Determinants of Earnings Management .............................. 39

5.5 Auditor Related Determinants of Earnings Management ...................................... 40

Chapter Six:

6.1 Simple Linear Regression Equation ...................................................................... 53

6.2 Simple Linear Regression Equation ...................................................................... 56

6.3 Multiple Regression One Equation ....................................................................... 59

6.4 Multiple Regression Two Equation ...................................................................... 60

6.5 Multiple Regression Three Equation .................................................................... 61

6.6 Multiple Regression Four Equation ...................................................................... 63

6.7 Mean Stock Price Performance Post Financial Year End ..................................... 66

Appendix C:

C.1 FTSE 350 Sector Specific Composition Summary ............................................. 109

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Appendix D:

D.1 Alternative Multiple Regression One Equation ................................................. 112

D.2 Stock Price Performance Multiple Regression One Equation ........................... 114

D.3 Stock Price Performance Regression Two Equation ......................................... 115

D.4 Stock Price Performance Multiple Regression Three Equation ......................... 116

Appendix G:

G.1 Frequency Distribution and Bell-Curve (Multiple Regression One) ................. 134

G.2 Scatter Plot of Regression Residuals (Multiple Regression One) ...................... 134

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LIST OF TABLES

Chapter Three:

3.1 Executive Level Compensation and Earnings Management ................................. 21

3.2 Corporate Governance Structures and Earnings Management ............................. 23

3.3 Auditor Type, Auditor Remuneration and Earnings Management ....................... 24

Chapter Five:

5.1 Capital Market Determinants of Earnings Management ...................................... 37

5.2 Contractual Determinants of Earnings Management ............................................ 38

5.3 Performance Related Determinants of Earnings Management ............................. 39

5.4 Governance Specific Determinants of Earnings Management ............................. 40

5.5 Auditor Related Determinants of Earnings Management ..................................... 41

5.6 Capital Market Response to Earnings Management ............................................. 41

5.7 Sample Selection Refinement Summary ............................................................... 44

5.8 Financial Year End Dates Summary Analysis ...................................................... 44

5.9 Data Sources for Data Measures ........................................................................... 45

Chapter Six:

6.1 Six Identified Outliers ........................................................................................... 52

6.2 Descriptive Statistics for Magnitude of Abnormal Provision ............................... 53

6.3 Simple Linear Regression Significance Statistics ................................................. 53

6.4 Descriptive Statistics for Categorical Independent Variables .............................. 55

6.5 Descriptive Statistics for Continuous Independent Variables .............................. 55

6.6 Simple Regression Analysis Results: Full Sample (N=204) ................................ 57

6.7 Simple Regression Analysis Results: Underproviders Only (N=138) .................. 57

6.8 Multiple Regression One Significance Statistics (N=204) ................................... 59

6.9 Multiple Regression One: Fourteen Hypotheses (N=204) ................................... 59

6.10 Multiple Regression Two Significance Statistics (N=204) ................................ 61

6.11 Multiple Regression Two: Seven Hypotheses (N=204) ..................................... 61

6.12 Multiple Regression Three Significance Statistics (N=138) .............................. 62

6.13 Multiple Regression Three: Fourteen Hypotheses (N=138) ............................... 62

6.14 Multiple Regression Four Significance Statistics (N=138) ................................ 63

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Chapter Six: (Continued)

6.15 Multiple Regression Four: Four Hypotheses (N=138) ....................................... 63

6.16 H2 to H15: Summary Findings ............................................................................. 64

6.17 Descriptive Statistics for Stock Price Performance: Underproviders ................. 65

6.18 Descriptive Statistics for Stock Price Performance: Overproviders ................... 65

Appendix D:

D.1 Impact of Outliers upon Preliminary Regression Analysis ................................ 111

D.2 Alternative Multiple Regression One Significance Statistics (N=204) ............. 112

D.3 Alternative Multiple Regression One: Fifteen Hypotheses (N=204) ................. 113

D.4 Stock Price Performance Regression One Sig. Statistics (N=25) ...................... 114

D.5 Regression One: Stock Price Performance Hypotheses (N=25) ........................ 115

D.6 Stock Price Performance Regression Two Sig. Statistics (N=25) ...................... 115

D.7 Regression Two: Stock Price Performance Hypothesis (N=25) ........................ 115

D.8 Stock Price Performance Regression Three Sig. Statistics (N=25) ................... 116

D.9 Regression Three: Stock Price Performance Hypotheses (N=25) ..................... 117

Appendix G:

G.1 Variance Inflation Factor Analysis Results ....................................................... 135

G.2 Test for Multicollinearity: Continuous Independent Variables ......................... 136

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LIST OF APPENDICES

Appendix A: Personal Reflection .................................................................. 95

Appendix B: IFRS 7 and IAS 39 ................................................................... 98

Appendix C: Details of Final Sample Population ....................................... 102

Appendix D: Multiple Regression Analysis Data ....................................... 110

Appendix E: Disclosure Notes: Extracts from Annual Reports .................. 118

Appendix F: Dependent Variable Dataset .................................................. 120

Appendix G: Methodology Continued: OLS Regression Assumptions ...... 128

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LIST OF ABBREVIATIONS

CRSP - Centre for Research in Security Prices

EPS - Earnings Per Share

EQ - Earnings Quality

FTSE 100 - FTSE 100 Index on the London Stock Exchange

FTSE 350 - FTSE 350 Index on the London Stock Exchange

IFRS - International Financial Reporting Standards

INEDs - Independent, Non-Executive Directors

OLS - Ordinary Least Squares Regression

U.S. - United States of America

U.S. GAAP - United States Generally Accepted Accounting Principles

U.S. GAO - United States Government Accountability Office

U.S. SEC - United States Securities and Exchange Commission

VAT - Value Added Tax

VIF - Variance Inflation Factor

Chapter 1

Introduction

Chapter 1 Introduction

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CHAPTER ONE

INTRODUCTION

1.1 Introduction

This chapter outlines the justification for this study, explores its underlying rationale

and establishes its context. Key themes and concepts that underpin this study are

discussed, while its relevance to the current business environment is also considered.

The research approach and contribution of this study are also explored, while the

chapter concludes with a diagrammatic overview of the structure of the dissertation.


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1.2 Research Rationale and Context

The importance of trade credit as a method of corporate financing is widely

documented, with empirical evidence suggesting that trade receivables account for

between 5 to 30 per cent of the total assets of European companies (Van Der Wijst

and Hol, 2002). Post the 2008 financial crisis, both private and corporate credit risk

remain elevated. With the intensification of the sovereign debt crisis across Europe

during 2011 and 2012, both macroeconomic uncertainty and credit risk have increased

further. EFMA (2012) states that 79 per cent of credit risk manager respondents

anticipate a renewed recession across Europe during 2012, while restrictions in trade

credit and sharp increases in credit delinquencies are also anticipated.

Companies provide for anticipated losses on trade receivables through a specific

provision for credit loss on trade receivables (IAS 39: S.58-59), commonly referred to

as the provision for doubtful receivables or provision for bad debts. In an environment

of such elevated credit risk, any reduction in this provision or failure to augment the

provision relative to an increase in total gross trade receivables is suspect. Indeed, any

reduction or failure to augment this provision generally serves to inflate overall

earnings. This study examines such abnormal provisioning activity in the context of

both earnings quality and earnings management.

Prior empirical studies, ranging from Healy (1985), Jones (1991) to Dechow et al

(2011) have examined in great detail the existence, frequency and magnitude of

earnings management activity, primarily in the context of discretionary accruals.

While a limited number of recent studies including Chen (2006) examine earnings

management in an international context, the majority of prior research has been

conducted in a U.S. or U.S. GAAP compliant financial reporting context. McNichols

and Wilson (1988) identify the need for further research with regard to earnings

management through singular accrual measures such as the provision for bad debts.

However, excepting the research of Lev and Thiagarajan (1993) and Ricci (2011), the

researcher is not aware of additional extensive research that has examined the

manipulation of the provision for credit loss on trade receivables from an earnings

management perspective, in a European or IFRS compliant financial reporting

context.


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This study complements existing earnings management research in adopting

methodology employed in prior studies, while also adding to existing earnings

management literature. In considering the response of capital markets to instances of

extreme abnormal provision for credit loss on trade receivables, this study also

examines the link between theoretical perspectives and the real world business

environment. Finally, the results provide a series of useful information for

practitioners including auditors, accounting standard setters and capital market

participants, with regard to the magnitude and determinants of abnormal provision for

credit loss on trade receivables.

1.3 Research Questions and Research Objectives

The research questions to be addressed in this study are:

RQ 1 – What is the magnitude and what are the determinants of abnormal provision

for credit loss on trade receivables amongst FTSE 350 companies?

RQ 2 – What is the capital (stock) market response to instances of extreme abnormal

provision for credit loss on trade receivables amongst FTSE 350 companies?

Utilising abnormal provision for credit loss on trade receivables as a measure of

earnings quality and as a proxy for earnings management activity, the following

research objectives support the investigation of the research questions:

1. To quantify the existence, direction and magnitude of abnormal provision for

credit loss on trade receivables amongst FTSE 350 companies.

2. To develop a multivariate OLS regression model that examines the

applicability of previously identified and alternative determinants of earnings

management activity, including capital market, contractual, performance,

governance and auditor related variables to abnormal provision for credit loss

on trade receivables amongst FTSE 350 companies.

3. To examine the individual and aggregate stock price performance of the most

extreme abnormal providers for credit loss on trade receivables (both under

and over providers) over a specified post financial year end period.


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1.4 Research Methodology and Limitations

Consistent with prior earnings management research, including Frankel et al (2002)

and Chen (2006), this study identifies a core measure of earnings quality and proxy

for earnings management, subsequently utilising descriptive statistics, correlation and

regression analyses to complete the varying tests that examine the research questions.

All data is gathered directly from both the latest available annual reports and the

Thomson One Banker database. The final sample population consists of 204 FTSE

350 companies, while the primary measure of earnings quality and proxy for earnings

management comprises the relative change in the provision for credit loss on trade

receivables after controlling for the relative change in total gross trade receivables.

Limitations of this study relate to the potential omission of earnings management

through alternative abnormal provisioning or discretionary accruals. The attachment

of a single earnings inflation motive to abnormal underprovision also disregards the

possibility that overprovision may represent earnings deflation activity. This study

also assumes that the prior year provision for credit loss on trade receivables is

representative of steady state, un-managed, normal provisioning. It is possible that

provisioning activity was elevated during 2008 and 2009, amidst the core of the

financial crisis, with companies now reducing their provision for credit loss on trade

receivables in subsequent years.


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1.5 Dissertation Structure: Diagrammatic Overview

This dissertation consists of eight chapters in total. The overall structure of the

dissertation and composition of each chapter is detailed in Figure 1.1 below.

Figure 1.1 – Dissertation Structure: Diagrammatic Overview

Introduction

Chapter 1:

An overview of the context and rationale

underlying the study. The research questions

and research objectives are also introduced.

Literature

Review

Chapter 2 – 4:

An extensive examination of theory and

prior empirical research underlying earnings

quality and earnings management activity.

Methodology

Chapter 5:

A thorough overview of the data collection

and analysis procedures, with a detailed

consideration of the limitations of the study.

Findings

Chapter 6:

Descriptive statistics and the results of

correlation and regression analyses

undertaken are presented sequentially.

Discussion

Chapter 7:

The implications of the findings are

analysed relative to regulation, theory and

prior empirical research.

Conclusion

Chapter 8:

The major findings of the dissertation are

presented, along with recommendations for

both practitioners and future research.

Chapter 2

Literature Review:

Earnings Quality and Earnings Management:

Introduction and Theoretical Perspectives

Chapter 2 Literature Review

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CHAPTER TWO

LITERATURE REVIEW

Earnings Quality and Earnings Management:

Introduction and Theoretical Perspectives

2.1 Introduction

The purpose of this chapter is to provide an introduction to the concepts of earnings

quality and earnings management. Initially discussing prior and current literature on

these concepts, the chapter then examines both accounting and economic theory

underlying earnings management practice, including positive accounting theory,

signalling theory and modern theory of the firm.


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2.2 Introduction to Earnings Quality

2.2.1 Earnings Quality as a Concept

The concept of earnings quality is documented extensively in prior literature, with

Ayres (1994) stating that earnings quality was examined as early as the 1930’s,

whereby the true or underlying value of a security could be determined through

careful analysis of an entity’s financial statements to indicate whether a company

should be trading in excess of or below its current market value. According to Ayres

(1994), a focus on the degree of permanence in reported earnings became a principal

measure of earnings quality during the early 1970’s.

Bricker et al (1995) posit that reported earnings are of the highest quality when they

are most reflective of underlying events and conditions. Moreover, Duncan (2002)

asserts that management must often undertake subjective estimates with regard to

losses on loans or trade receivables that directly impact earnings quality and that if

managers smooth or manage earnings through estimates that are either too liberal or

conservative, there is a significant risk that such earnings may be viewed as lower

quality earnings by financial statement users. In supporting Duncan (2002), Schipper

et al (2003) determine that investors consistently attach higher price multiples to

earnings patterns that are supported by high quality earnings and that the magnitude

of any earnings management activity directly impacts the quality of earnings.

2.2.2 Measures of Earnings Quality

Schipper and Vincent (2003) state that earnings quality may be measured through

indicators that include the ratio of cash from operations to income, changes in total

accruals or the direct estimation of discretionary accruals through accounting

fundamentals. Palliam and Shalhoub (2003) define earnings quality as a measure of

the predictability of future earnings while Schipper et al (2003) also posit that higher

quality earnings have a signalling effect that indicates the sustainability of an earnings

pattern. Bellovary et al (2005), in identifying the provision for doubtful receivables as

a singular measure of earnings quality, also contend that earnings quality refers to the

stability, persistence and lack of variability in reported earnings while Mohammady

(2010) supports these assertions in stating that persistence, predictive value, feedback

value and earnings smoothness can all be employed as indicators of earnings quality.


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Figure 2.1 below, adapted from Ayres (1994) outlines the traditional income

smoothing hypothesis, where long run reported and managed or manipulated earnings

are smooth, relative to underlying, real earnings.

Figure 2.1 – Income Smoothing Hypothesis

2.2.3 Earnings Quality and Earnings Management

As a result of the relationship between earnings management activity and earnings

quality, the detection of earnings management has been the focus of multiple

empirical studies to date, with the work of Healy (1985), DeAngelo (1986), and Jones

(1991) in the development of specific models to test for the existence, frequency and

magnitude of earnings management. Subsequent empirical studies, including those of

Sweeney (1994), Dechow et al (1995), Dechow and Dichev (2002), and Dechow et al

(2011) have attempted to refine prior models in the detection of earnings management

and to identify the primary determinants of earnings management practice generally.


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2.3. Introduction to Earnings Management

2.3.1 Earnings Management as a Concept

References to earnings management are not always explicit and are often described as

earnings manipulation, big bath accounting, income smoothing or creative accounting

(Stlowy and Breton, 2004). Dechow and Skinner (2000) suggest that there is a

somewhat limited degree of empirical evidence from academic studies to suggest that

earnings management has a material impact upon average reported earnings. Dechow

and Skinner (2000) also acknowledge that there is significant disparity between

academic and practitioner perspectives, whereby academics focus primarily upon

earnings management activity driven by contractual agreements, yet practitioners are

primarily concerned with capital market determinants of earnings management.

2.3.2 Differentiation between Earnings Management and Manipulation

Earnings management comprises income smoothing behaviour but also refers to the

intentional structuring of disclosure or investment decisions with the bottom line

impact in mind (Ayres, 1994). In defining earnings management, an important

distinction must be made between earnings management and earnings manipulation

more specifically. Dechow and Skinner (2000) suggest that while underprovision for

bad debts constitutes aggressive earnings management, it is fictitious inventory and

revenue inflation that constitute fraudulent earnings manipulation. Moreover, Chen

(2006) posits that expense recognition deferral, erroneous revenue recognition and

measurement abuse constitute outright earnings manipulation.

2.3.3 Accounting Policy Flexibility and Earnings Management

While acknowledging that accounting flexibility is a primary mechanism through

which earnings management takes place, Dechow and Skinner (2000) suggest that the

elimination of all accounting flexibility would render earnings useless as a

measurement of economic performance. Colson et al (2010) suggest that firms may

utilise such flexibility to provide a clearer indication of their financial performance,

rather than to mislead investors. Additionally, Srivastava (2008) determines that firms

utilise flexibility in revenue recognition rules in order to convey value relevant

information to investors and not to engage in earnings management.


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2.3.4 Impact from Earnings Management Activity

Despite an apparent lack of conclusive empirical evidence within academic literature

(Healy and Wahlen, 1998), the regular occurrence of corporate earnings scandals

including those of Enron, Tyco and Global Crossing (Bitner, 2005) provides

supporting evidence that earnings management occurs in extreme forms with

significant adverse impacts on firms and their respective stakeholder groups.

Additionally, Healy and Wahlen (1998) state that there is evidence of significantly

negative stock market responses to allegations of earnings management, with a

corresponding risk of an adverse impact on resource allocation in the wider economy.

2.3.5 Accrual Accounting, Earnings Management and Accounting Earnings

Accrual accounting, which is utilised to disrupt cashflow patterns in order to

compensate for issues of both timing and recognition (Dechow and Dichev, 2002),

is most contentious in the area of earnings management. Although accounting driven

accruals are often identified as a primary mechanism through which earnings

management may take place (Dechow et al, 1995), it is argued that the use of accrual

accounting in the determination of earnings results in long run earnings patterns that

are closely correlated with returns (Degeorge et al 1999). Healy and Wahlen (1998)

also contend that current earnings, which are indicative of management judgement,

are value relevant and are better indicators of future cash flow performance than

current cash flows.

2.4. Earnings Management – Theoretical Perspectives

2.4.1 Introduction to Theoretical Perspectives

The determinants of earnings management activity can be viewed primarily within the

confines of the signalling, agency and positive accounting theoretical frameworks.

While several sources of literature contend that there are significant differences

between agency theory and signalling theory, Morris (1987) concludes that both

theories are consistent, with considerable overlap in many instances.


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13

Figure 2.2 – Signalling Theory and Earnings Management

2.4.2 Signalling Theory and Higher Quality Firms

Spence (1973) suggests that where two parties are engaged in a transaction and there

exists the problem of asymmetric information, one party may send a signal to the

other, in order to convey value relevant information, with resultant positive

implications for the party sending the signal from a valuation perspective. Accounting

standard guidance prescribes a lower bound or minimum information disclosure

requirement level according to Morris (1987), who posits that higher quality firms

will utilise accounting information disclosure to indicate to shareholders that they are

not utilising accounting flexibility to their detriment, or that they are not utilising such

flexibility to the same extent as other firms.

2.4.3 Signalling Theory and Lower Quality Firms

Conversely, Morris (1987) also argues that lower quality firms who are determined

that accounting standard disclosure requirements do not provide fine information

signals; will engage in corporate lobbying to ensure that standards of this kind are

introduced. Morris (1987) further posits that, in the context of accounting policy

choice, higher quality firms will chose more optimal accounting policies that reveal

their superior quality when compared with lower quality firms, who will utilise

accounting methods that conceal their inferior quality. The clear inference from these

assertions is that those firms who engage in earnings management are predisposed

towards those signalling motives of inferior quality firms.

Signalling

Theory

Optimal Accounting

Policies Revealing

Superior Quality

Higher Quality Firms

Lower Quality Firms Accounting Methods

Concealing Inferior

Quality


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14

Figure 2.3 – Positive Accounting Theory and Earnings Management

2.4.4 Positive Accounting Theory and Earnings Management

Developed primarily by Watts and Zimmerman (1978), positive accounting theory

provides significant grounding for the determinants of earnings management practice.

Watts and Zimmerman (1986) posit that, in the absence of manipulation by

management, earnings otherwise follow a particular process and in order to reduce the

variance of that process, management adopt or alter specific accounting procedures.

In analysing the relationship between earnings and stock prices, Watts and

Zimmerman (1986) also suggest that the method of generating reported earnings has

an important bearing upon the income smoothing hypothesis, whereby, all else being

equal, managers will smooth earnings.

Watts and Zimmerman (1986) further determine, from prior empirical evidence, that

although there is no definitive evidence of a relationship between the capital intensity

of a firm, political costs and earnings deflation, there is consistent evidence that key

variables including size, the debt to equity ratio and the existence of a management

level compensation plan impact the propensity towards earnings management within a

firm. Watts and Zimmerman (1986) also posit that there is a positive relationship

between an increasing debt to equity ratio, the existence of a management level

compensation plan and the likelihood of the adoption of earnings inflation procedures

specifically. Consequently, these variables have been subject to widespread testing in

earnings management research.

Increased Propensity

Towards Earnings

Inflation Activity

Management

Compensation

Hypothesis

Debt Hypothesis Positive Relationship

Between Debt and

Earnings Inflation

Positive

Accounting

Theory


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15

Figure 2.4 – Agency Theory and Earnings Management

2.4.5 Agency Theory and Earnings Management

Conceptualised primarily by Jensen and Meckling (1976), agency theory defines the

relationship between the principal and agent, owner and manager of a firm. Palliam

and Shalhoub (2003) state that the risk differential between principals and agents

creates a problem in the principal - agent relationship. While the responsibility for the

management of earnings rests with the agents of a firm, the methods undertaken to

manage earnings are not equally desirable from a principal’s perspective (Palliam and

Shalhoub, 2003). The principal can limit divergence by the agent from their desired

perspectives through both incentives and monitoring costs. However, where both

parties strive for utility maximisation, divergence remains highly likely (Jensen and

Meckling, 1976).

2.4.6 Agent Specific Motives for Earnings Management

In examining revenue recognition practice in the context of an agency setting, Dutta

and Zhang (2000) determine that no performance measure based upon current

accounting information will result in optimal agent specific incentives where mark to

market accounting is utilised. Moreover, in order to comply with consensus earnings

forecasts, the desires of the principal or to project a smooth earnings path, Palliam and

Shalhoub (2003) posit that agents will manage earnings through the acceleration or

deferral of either revenue or expenses or through accounting operations.

Conflicting From

Agent and Principal

Perspectives

Methods of Earnings

Management

Agent Motives Compliance with

Expectations of

Principal & Markets

Agency

Theory


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16

2.5 Conclusion

Clearly, the concepts of earnings quality and earnings management have been the

subject of extensive focus over several decades from both empirical and theoretical

perspectives. There is considerable cross literature consensus with regard to earnings

quality measures ranging from persistence to smoothness, along with the use of

singular measures of earnings quality relating to trade receivables as outlined by

Bellovary et al (2005). Additionally, accounting and economic theory provides strong

support for the existence and determinants of earnings management activity, with

many of these determinants examined extensively in the following chapter.

Chapter 3

Literature Review:

The Determinants of Earnings Management:

Varying Perspectives and the Response of

Capital Markets


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18

CHAPTER THREE

LITERATURE REVIEW

The Determinants of Earnings Management:

Varying Perspectives and the Response of Capital Markets

3.1 Introduction

The purpose of this chapter is to discuss extensively the determinants of earnings

management practice as identified in prior empirical studies, while simultaneously

aiming to demonstrate a clear link between the prior mentioned theory and extant

earnings management practice. Where multiple authors’ findings relating to the

determinants of earnings management are discussed; summary tables are presented.

The chapter then concludes with a focus on the response of capital markets to

earnings management activity.


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19

3.2 Earnings Management and the Financial Condition of a Firm

While earnings management more frequently takes place within firms that are

experiencing financial distress (Chen, 2006), healthy firms that have not experienced

multi-period accumulated losses also engage in earnings management

(Peltier-Rivest and Swirsky, 2000). However, the determinants of earnings

management within healthy firms are not equivalent to those of distressed firms, as

such traditional determinants, including executive level motives, would not yield

sufficient benefits in order to influence managers’ accounting choice (Peltier-Rivest

and Swirsky, 2000).

Jeffrey et al (2008) determine that where a firm has suffered significant prior period

operating losses or negative cash flows, it will be motivated to manipulate revenues

specifically rather than earnings generally, as capital market participants tend to value

such firms on the basis of the level and growth in revenue rather than earnings and

cash flows. Management within healthy firms that are engaged in union or labour

negotiations are more likely to make income decreasing total accruals that depress

total earnings (Peltier-Rivest and Swirsky, 2000). Moreover, within healthy firms,

there is limited evidence of strong earnings management incentives driven through

top level executive change or government lobbying – that is, when a firm is subject to

governmental investigation (Peltier-Rivest and Swirsky, 2000).

3.3 The Determinants of Earnings Management

In establishing the determinants of earnings management, a primary consideration

includes the extent to which such activity is driven by the exercise of managerial

judgement (Healy and Wahlen, 1998). Bitner (2005) contends that asset quality, sales

growth and depreciation contain key indicators highlighting some of the contributing

factors to earnings management, while also contending that, inter alia, the following

components contribute to earnings management activity:

Sub optimal decision making resulting from prolonged periods of prosperity with

warning signals being disregarded and contradictory evidence being rationalised.

Fear on the part of lower level executives who act in their own self-interest in the

non-disclosure of negative information to their superiors.

A deficit in longer term planning, operational viability and leadership.


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20

3.3.1 Contracting Motives and Earnings Management

Contracting motives for earnings management refer to those determinants within the

confines of the agency relationship, where covenants or provisions are utilised to

mitigate traditional agency problems (Jensen and Meckling, 1976). Healy and Wahlen

(1998) state that contracting motives arise where management compensation contracts

are utilised to align external stakeholder and management incentives or where lending

contracts are utilised to prevent against managerial level engagement in activity to the

detriment of a firm’s creditors.

3.3.2 Executive Level Compensation and Earnings Management

According to Healy and Wahlen (1998), the balance of empirical evidence suggests

that managers will utilise accounting judgement to inflate earnings where bonus plans

and contractual compensation incentives are indexed to earnings performance.

Watts and Zimmerman (1986) suggest that managers with contractual bonus plans are

more likely to adopt accounting policies that lead to the premature recognition of

future period earnings in the current accounting period. Moreover, Healy (1985)

determines that firms who specify a limit on their bonus award schemes are more

likely to report accruals resulting in the deferral of income when the bonus limit is

reached, indicating that there is an incentive to report earnings that will result in

receipt of the maximum bonus level, but not beyond such a level.

Dechow and Sloan (1991) determine that chief executive officers reduce research and

development spending during their final years in office, possibly to report more

positive short run earnings, with their final compensation contracts linked to these

earnings upon departure. However, Healy and Wahlen (1998) contend that such

changes in research and development expenditure may arise as a result of changes in

general investment policy rather than earnings management specifically.

Siagian (2002) finds no evidence of an abnormally high association between the

bonus of the chief executive officer and annual earnings amongst firms have been

subject to enforcement actions by the U.S. S.E.C. However, Balsam (1998)

determines that management engage in accounting choice in order to enhance their

level of compensation, determining that the association between the compensation of

the chief executive officer and the reported income of a firm increases with the level

of discretionary accruals.


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21

Gaver and Gaver (1998) support the findings of Balsam (1998), determining that

managers are rewarded for undertaking accounting choice that positively impacts

income. Chen (2006) also asserts and finds, in a Taiwanese context, that those firms

engaged in earnings manipulation have a stronger intention to avoid reporting net

losses or depressed earnings in order to secure high levels of bonus payments, when

compared with a sample of non-manipulating firms. Clearly, the majority evidence

from these studies (Table 3.1 below) suggests that executive level compensation is a

primary determinant of earnings management activity.

Table 3.1 – Executive Level Compensation and Earnings Management

Author Subject Earnings Management R

Healy (1985) Bonus Plans Limited Earnings Mgmt. +

Healy and Wahlen (1998) Bonus Plans Earnings Inflation +

Dechow and Sloan (1991) R&D Expenditure Earnings Inflation -

Balsam (1998) Mgmt. Compensation Earnings Inflation +

Gaver and Gaver (1998) Mgmt. Compensation Earnings Inflation +

Chen (2006) Bonus Payments Earnings Inflation +

Where R indicates the relationship between the subject and earnings management activity.

3.3.3 Debt Contract Motives and Earnings Management

Prior research has also investigated the relationship between an increasing risk of

breaching debt covenants or lending contracts and earnings management activity.

Chen (2006) suggests that firms are more likely to be successful in loan or funding

applications where they have higher net incomes and gearing ratios that are well

below the industry accepted threshold of 50 per cent. Dechow et al (1996) state that a

primary determinant of earnings management is the desire to raise external financing

at a low cost and to avoid any debt covenant restrictions. In examining firms that have

violated lending contracts, DeFond and Jimbalvo (1994) determine that firms

accelerated their earnings, one year prior to the breach of debt covenants.

Additionally, Sweeney (1994) determines that debt covenant violators typically

engage in income increasing accounting policy changes; however this engagement is

generally post debt covenant violation.


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22

3.3.4 Earnings Expectations and Earnings Management

Additional research has examined whether earnings management occurs in order to

meet or exceed the expectations of institutional investors, analysts and other capital

market participants. Payne and Robb (1997) determine that firms manage their

earnings in order to meet or comply with analysts’ forecasts. Kasznik (1999) provides

evidence consistent with the findings of Payne and Robb (1997), determining that

managers utilise positive discretionary accruals to inflate earnings when earnings

would otherwise, in the absence of inflation, fall below prior management forecasts.

Habib and Hansen (2008) state that the importance placed upon meeting analysts’

forecast benchmarks has increased in recent years. Lopez and Rees (2002) determine

via empirical analysis that 65 per cent of sample firms met or exceeded analysts’

forecasts during the years post 1992. Lopez and Rees (2002) also find that the

negative response of stock market participants to not meeting forecasts is significantly

greater, in absolute terms, than the response to beating forecasts and that meeting

analysts’ forecasts is a more powerful variable in the explanation of returns than the

annual profit or loss performance of a firm.

3.3.5 Regulatory Motives and Earnings Management

Regulatory motives refer to those within the context of either governmental regulation

(Healy and Wahlen, 1998) or self-regulation in the form of effective or defective

corporate governance mechanisms (Jouber and Fakhfakh, 2011). Jones (1991)

determines that U.S. firms seeking import duty relief generally depress earnings in the

year of application for such relief. Within the regulated U.S. banking sector and in

the context of the provision for credit loss, Healy and Wahlen (1998) state that there is

considerable evidence of excess loan loss provisioning and a subsequent

understatement of loan loss impairments to facilitate the recognition of abnormal

unrealised gains. This assertion is supported by Collins et al (1995) who determine

that profitable banks decrease their loan loss provisions when their earnings are

relatively low and increase such provisions when earnings are relatively high - this

being a clear indicator of pro-cyclical earnings management. Yun and Kim (2011)

infer that regulation can limit earnings management activity, finding that there has

been a significant decline in the proportion of discretionary accruals amongst sample

firms post implementation of the Sarbanes Oxley Act, 2002.


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23

3.3.6 Corporate Governance Structures and Earnings Management

Empirical research has also identified a strong link between effective corporate

governance structures and a reduced level of earnings management activity,

as summarised in Table 3.2 below. Dechow et al (1996) find that poor oversight

through weak governance structures is an important determinant of earnings

manipulation. Chen (2006) determines that Taiwanese firms who are engaged in

earnings manipulation have a lower concentration of INEDs on their boards of

directors and supervisory boards, when compared with non-manipulating firms.

Peasnell et al (2005) posit that, as earnings management imposes costs upon

stockholders and capital market participants, effective corporate boards should work

towards preventing such manipulation, while also finding that the incidence of income

increasing earnings management activity decreases as the concentration of external

board members to the total board increases. Sebahattin and Harlan (2009) determine

that effective or strong corporate governance mechanisms within U.S. manufacturing

firms reduce the incidence of earnings management amongst mid-range firms. In light

of these findings, Sebahattin and Harlan (2009) also assert that creditors and equity

investors should apply greater scrutiny to the reported accruals of firms, being

mindful that robust corporate governance structures may represent an intervening

variable with regard to abnormal accruals.

Table 3.2 – Corporate Governance Structures and Earnings Management


Dechow et al (1996) Weak Governance Increased Manipulation +

Beasley (1996) (+) INEDs to Board Reduction in Fraud -

Peasnell et al (2005) (+) INEDs to Board Reduced Earnings Inflation -

Sebahattin and Harlan (2009) Strong Governance Reduced Earnings Mgmt. -


Additionally, Beasley (1996) determines that the proportion of external director

concentration to the board of directors for firms experiencing financial statement

fraud is lower when compared with non-fraud firms and that the composition of the

board, rather than audit committee presence, is a more important mechanism for

reducing the likelihood of financial statement manipulation.


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24

3.3.7 Audit Committee and Earnings Management

The audit committee is a sub-component of the overall governance structure within a

firm. While Lin et al (2006) posit that there is a significantly negative association

between the independence of the audit committee and the incidence of earnings

restatement, along with a significantly negative association between the number of

audit committee meetings and the incidence of earnings restatement, neither of these

hypotheses is supported when subject to testing.

3.3.8 Auditor Type, Auditor Remuneration and Earnings Management

Francis and Krishnan (1999) determine that large audit firms provide higher quality

audits, exhibit reporting conservatism and are more aggressive in constraining the

earnings management activity of their clients. Krishnan (2003) determines that clients

of non-specialist auditors exhibit elevated levels of discretionary accruals when

compared with clients of specialist auditors while also suggesting that the use of a

Big 61 auditor with industry specialist knowledge can enhance the credibility of

accounting information.

Jordan et al (2010) determine that managers of companies audited by small audit

firms manipulate their earnings to generate EPS values consistent with user reference

points and that Big 4 audit firms are more likely to be effective in constraining the

efforts of their clients in earnings manipulation. In addition, Lin et al (2006) suggest

that higher audit fees from either audit specific or non-audit services reduce auditor

independence and therefore impair overall audit quality. In contrast, Frankel et al

(2002) find that there is a negative association between audit specific fees and

earnings management indicators. Table 3.3 below summarises these findings.

Table 3.3 – Auditor Type, Auditor Remuneration and Earnings Management


Francis and Krishnan (1999) Large Audit Firms Reduced Earnings Mgmt. -

Frankel et al (2002) (+) Audit Fees Reduced Earnings Mgmt. -

Lin et al (2006) (+) Audit Fees Increased Earnings Mgmt. +

Jordan et al (2010) Small Audit Firms Increased Earnings Mgmt. +


1 Big 6 audit firms in 2003 have subsequently reduced to Big 4 audit firms at the time of this study.


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25

3.3.9 Revenue Manipulation, Deferred Revenue and Trade Receivables

The determinants of both revenue and trade receivables manipulation are discussed in

this section, given the importance of trade receivables throughout this study. Investors

and capital market participants place significant emphasis upon reported revenue

according to Anderson and Yohn (2002), who conclude that when there are

irregularities with a firm’s financial statements, investors are more concerned with

revenue recognition than alternative reporting issues, with revenue restatements

resulting in significantly more adverse stock returns compared with alternative

accounting restatements.

Caylor (2009) determines that managers engage in accelerated revenue recognition

using the short term deferred revenue and gross trade receivable accounts where

pre-managed earnings fall slightly below analyst benchmarks. Caylor (2009) also

determines that managers prefer to exercise revenue recognition in deferred revenue

rather than trade receivables in order to avoid negative earnings surprises.

Revenues form a unique role in valuations and it is preferable for managers to

manipulate revenues when compared with alternative earnings management methods,

as alternative earnings management methods are not equivalent in monetary outcomes

according to Zhang (2006); who also determines that firms with the following

characteristics are more likely to manage or manipulate revenues:

Higher growth perspectives

Higher operating margins

Outstanding analyst sales forecasts

Higher accounting policy flexibility in revenue recognition

Jeffrey et al (2008) determine that the greater a firm’s historical operating losses or

past and expected negative operating cash flows, the more likely it is to overstate

revenues and accounts receivable in order to induce a higher market valuation. Jeffrey

et al (2008) also determine that there is a positive relationship between the likelihood

of revenue manipulation and increasing leverage and inventory to total asset ratios.


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26

3.4 The Response of Capital Markets

3.4.1 Earnings Quality and Stock Price Performance

While there is broad cross literature agreement that capital markets respond

negatively to low quality earnings, the available empirical evidence remains

somewhat conflicting. Dechow et al (2007) document a negative raw stock price

performance amongst manipulating firms during the years directly post earnings

manipulation. As indicated in Figure 3.1 below, there is a pronounced decline in stock

price performance post earnings manipulation, with only a slow recovery thereafter,

highlighting the longer term capital market effects arising from such activity.

Figure 3.1 – Stock Price Performance: Post Earnings Manipulation

Annual Raw Returns Surrounding the Earnings Manipulation Years

Year Relative to Manipulation Years

Note: Adapted directly from Dechow et al (2007).

Chan et al (2001) determine that there is a reliable negative association between

elevated levels of accruals, categorised as low quality earnings, and future stock

returns; also noting that changes in accounts receivable have strong predictive power.

However, Sloan (1996), in finding that the persistence of earnings performance is

dependent on the magnitude of the cash and accrual components of earnings,

determines that stock price results are inconsistent with the traditional efficient market

hypothesis that stock prices fully reflect all publicly available information.


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27

3.4.2 Equity Offerings, Earnings Management and Stock Price Performance

Teoh et al (1998) determine that discretionary current accruals, which are subject to

managerial judgement, are artificially high around the period of an initial public

offering when compared with non-issuers. Teoh et al (1998) also determine that

issuers with an abnormally high level of discretionary accruals experience inferior

stock returns in the three years post initial public offering, with a firm in the most

aggressive category of initial public offering earnings managers experiencing, on

average, a 15 to 30 per cent worse three year stock price performance than those firms

classified as being within the most conservative range. This result is consistent with

the findings of Holthausen et al (1995) who determine that future stock returns are

negative for firms whose current earnings include large accrual components and

conversely, that future stock returns are positive for those firms with low accrual

components to their earnings.

3.4.3 Magnitude of Capital Market Based Earnings Management

While only limited prior research has measured the significance of capital market

based earnings management, Teoh et al (1994) determine, from a sample of firms

undertaking initial public offerings; that the median level of unexpected accruals

ranges from 4 – 5 per cent of total assets. Erickson and Wang (1999) find that

accruals for firms are measured at 2 per cent of total assets during the quarter of a

stock acquisition. While quantification of these findings in the absence of a

comparison with other non-issuing firms remains difficult, Teoh et al (1994)

determine that 62 per cent of firms undertaking initial public offerings exhibit

abnormally high levels of unexpected accruals when compared with a sample of

control firms, indicating an identifiable level of earnings management activity.

3.5 Conclusion

The numerous determinants of earnings management discussed throughout this

chapter provide a clear link between the previously cited accounting and economic

theory and extant earnings management practice, particularly with regard to the

opportunistic perspectives of positive accounting theory. Subsequently, varying

models have been developed to analyse the determinants of earnings management

activity. These models are considered in detail in the following chapter.

Chapter 4

Literature Review:

Testing For Earnings Management:

Model Development and Relevant

Accounting Standard Guidance


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29

CHAPTER FOUR

LITERATURE REVIEW

Testing for Earnings Management: Model Development and

Relevant Accounting Standard Guidance

4.1 Introduction

The purpose of this chapter is to provide a chronological overview of the development

of models and measures utilised in testing for earnings quality and earnings

management, focusing on the provision for credit loss on trade receivables

specifically. Moreover, the chapter also details the current IFRS accounting standard

guidance and disclosure requirements with regard to the provision for credit loss on

trade receivables.


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30

4.2 The Provision for Doubtful Receivables: Model Development

4.2.1 The Provision for Doubtful Receivables: McNichols and Wilson (1988)

There is a significant literature gap with regard to empirical studies that examine the

level of provision for credit loss on trade receivables, amongst IFRS compliant firms,

in the context of earnings management. This is possibly explained as a result of prior

research utilising a portfolio or combination approach in examining the combined

level of total discretionary accruals. In utilising the provision for doubtful debts as a

proxy for earnings management activity and predicting the provision for doubtful

debts in the absence of earnings management, McNichols and Wilson (1988)

determine that firms manage their earnings through the choice of income decreasing

accruals when income is extreme.

Additionally, McNichols and Wilson (1988) also determine that discretion in the

provision for bad debts ranges from 1 – 4 per cent of income for firms with extreme

income and that exercising discretion over the provision for bad debts, combined with

alternative discretionary accrual measures, can facilitate the achievement of target

income where annual earnings targets are within a 10 to 15 per cent growth range.

4.2.2 The Provision for Doubtful Receivables: Lev and Thiagarajan (1993)

In developing a multivariable earnings signal framework, Lev and Thiagarajan (1993)

identify disproportionate annual changes in trade receivables relative to revenue and

disproportionate annual changes in the provision for doubtful receivables relative to

trade receivables as fundamental indicators of earnings quality, suggesting that firms

with inadequate provisions for doubtful receivables are expected to experience future

depressed earnings as a result of increased provisions.

Lev and Thiagarajan (1993) also determine that while the aforementioned receivables

and doubtful receivables signals are relatively weak in unconditioned analysis, both

signals are statistically significant and value relevant during high inflation years,

indicating the importance of contextual or conditioned analysis. Lev and Thiagarajan

(1993) also suggest that there are adverse implications arising from inadequate bad

debt provisioning for both the persistence and growth of earnings.


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31

4.2.3 Manipulation of Trade Receivables: Ricci (2011)

Ricci (2011) compares the receivables and receivables related accounts of companies

subject to U.S. SEC enforcement actions against those of a positive control group.

Utilising the Wilcoxon Signed Ranks Test, manipulating companies are paired against

non-manipulating companies within the same industry grouping. In determining that

trade receivables manipulation varies by industry type, Ricci (2011) finds that

receivables are inflated via the provision for doubtful receivables specifically; in the

Information Technology sector.

4.3 The Provision for Doubtful Receivables – IASB Accounting Guidance

4.3.1 IAS 39: Section 58 – 59

Section 58 of IAS 39 prescribes that an entity should reduce the carrying amount of a

financial asset either directly or through the use of an allowance account where

objective evidence of impairment exists at the reporting period end. Section 59 of IAS

39 also states that, inter alia, the following constitute objective evidence of

impairment:

“A breach of contract, such as a default or delinquency in interest or principal

payments”.

“National or local economic conditions that correlate with defaults on the assets

in a group”.

4.3.2 IFRS 7: Section 7.16 and 7.37

Section 7.16 of IFRS 7 stipulates that an entity must disclose a reconciliation of the

annual changes in the allowance account for each class of financial asset. Section 7.37

of IFRS 7 also stipulates that an entity must disclose:

“An analysis of the age of financial assets that are past due as at the end of the

reporting period but not impaired’’ and “An analysis of financial assets that are

individually determined to be impaired as at the end of the reporting period”.

These disclosures facilitate the determination of increasing or decreasing credit risk

arising on trade receivables from the examination of a firm’s financial statements.


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32

4.4 Literature Review Conclusion

Capital market participants continue to place a significant emphasis upon the reported

earnings of firms (Schipper et al, 2003). As detailed in chapter two, the quality of

earnings is fundamental, as they represent a primary mechanism by which investors

can determine the most appropriate price of a security and attach a value to a firm.

However, there have been numerous corporate earnings scandals, arising from

earnings management practice, with resultant adverse effects for corporate

stakeholders (Bitner, 2005).

Accounting and economic theory provides strong support for the existence and

determinants of earnings management activity, while empirical research provides a

clear link between theory and extant earnings management practice. The majority of

these determinants have, however, been established in the context of earnings

management practice within the United States or U.S. GAAP compliant financial

reporting, as detailed in both chapters three and four. To this extent, there is a

significant literature gap with regard to empirical research in an IFRS compliant

financial reporting context. This gap is addressed in detail in the following chapter.

Chapter 5

Research

Methodology

Chapter 5 Research Methodology

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34

CHAPTER FIVE

RESEARCH METHODOLOGY

5.1 Introduction

The purpose of this chapter is to discuss the research rationale, research objectives

and research methodology underlying this study. The chapter firstly discusses the

research rationale for this study along with the research questions, before examining

the research objectives and literature driven research hypotheses. Thereafter, an

in-depth overview of the research methods undertaken in this study is provided, with

significant emphasis upon sample selection, data collection methods and the rationale

for the selection of various data measures. The chapter then concludes with an

overview of several limitations associated with this study.


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35

5.2 Research Rationale

Prior empirical studies, ranging from Healy (1985), Jones (1991) to Dechow et al

(2011) have examined in great detail the existence, frequency and magnitude of

earnings management activity, primarily in the context of discretionary accruals.

While Chen (2006) examines earnings management in an international context, the

majority of prior research has been conducted in a U.S. or U.S. GAAP compliant

financial reporting context. McNichols and Wilson (1988) identify the need for

further research with regard to earnings management through singular accrual

measures such as the provision for bad debts. However, excepting the research of Lev

and Thiagarajan (1993) and Ricci (2011), no extensive research has examined the

manipulation of the provision for credit loss on trade receivables in a European or

IFRS compliant financial reporting context.

Persistent macroeconomic uncertainty, particularly across Europe, combined with an

elevated level of credit risk relative to previous years (EFMA, 2012), renders this

study possible, timely and most appropriate. Undertaken in an IFRS compliant

financial reporting context, this study complements existing earnings management

research in adopting methodology employed in prior studies, while also adding to

existing literature. Moreover, this study provides a series of useful information for

various practitioners with regard to the magnitude and determinants of abnormal

provision for credit loss on trade receivables.

5.3 Research Questions

The research questions to be addressed in this study are:





The research objectives that support the investigation of the research questions are

outlined overleaf.


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36

5.4 Research Objectives

Utilising abnormal change in the provision for credit loss on trade receivables as a

measure of earnings quality and as a proxy for earnings management activity:

1. To quantify the existence, direction and magnitude of abnormal provision for

credit loss on trade receivables amongst FTSE 350 companies.

2. To develop a multivariate OLS regression model that examines the


management activity, including capital market, contractual, performance,

governance and auditor related variables to abnormal provision for credit loss

on trade receivables amongst FTSE 350 companies.

3. To examine the individual and aggregate stock price performance of the most

extreme abnormal providers for credit loss on trade receivables (both under

and over providers) over a specified post financial year end period.

5.5 Research Hypotheses for Testing

This study, similar to previous earnings management studies, including Teoh et al

(1998), Frankel et al (2002) and Chen (2006) examines a large number of independent

explanatory variables. Research hypotheses relating to each research objective, along

with the associated independent variables and supporting literature references, where

applicable, are presented throughout.

5.5.1 – Objective One: Hypothesis

Maintaining the assertion that FTSE 350 companies are managing their earnings

through the provision for credit loss on trade receivables:

H1 – Ceteris paribus, the annual relative change in gross trade receivables does not

explain a significant extent of the variation in the annual relative change in the


Literature Source: Lev and Thiagarajan (1993) – Lit. Review – Section 4.2.2


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37

5.5.2 – Objective Two: Hypotheses

The following hypotheses are conceptualised in the context of earnings inflation

activity, with abnormal underprovision for credit loss on trade receivables in an

environment of elevated credit risk representing earnings inflation activity.

Figure 5.1 – Capital Market Determinants of Earnings Management

H2 – Ceteris paribus, there is a negative association between analyst consensus EPS

growth forecasts and the abnormal change in provision for credit loss on trade

receivables.

H3 – Ceteris paribus, there is a negative association between a company’s earnings

(EPS) surprise and the abnormal change in provision for credit loss on trade

receivables.

Where: companies abnormally underprovide, to inflate earnings, in order to comply

with such capital market determinants.

Table 5.1 – Capital Market Determinants of Earnings Management

Literature Source: Literature Review: Section 3.3.4.

Payne and Robb (1997) - Kasznik (1999)

Lopez and Rees (2002) - Habib and Hansen (2008).

Figure 5.2 – Contractual Determinants of Earnings Management

Capital Market

Variables

Consensus EPS

Growth %

Earnings – EPS

Surprise %

Abnormal Change in

Provision for Credit

Loss on T-Receivables

Contractual

Variables

Existence of

Bonus Plan

Exec. Incentive

Remuneration

Abnormal Change in



Change in

Gearing


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38

H4 – Ceteris paribus, there is a negative association between the existence of a bonus

plan and the abnormal change in provision for credit loss on trade receivables.

H5 – Ceteris paribus, there is a negative association between the proportion of

incentive (bonus) specific executive level remuneration and the abnormal change in


H6 – Ceteris paribus, there is a negative association between the change in the level of

gearing of a firm and the abnormal change in provision for credit loss on trade

receivables.

Where: companies abnormally underprovide, to inflate earnings, given the existence

of such contractual incentives.

Table 5.2 – Contractual Determinants of Earnings Management

Literature Source: Literature Review: Sections 3.3.1 - 3.3.2 - 3.3.3.

Healy (1985) - Watts and Zimmerman (1986)

Dechow and Sloan (1991) - DeFond and Jimbalvo (1994)

Dechow et al (1996) - Balsam (1998) - Chen (2006).

Figure 5.3 – Performance Related Determinants of Earnings Management

H7 – Ceteris paribus, there is a negative association between the change in the gross

margin of a firm and the abnormal change in provision for credit loss on trade

receivables.

H8 – Ceteris paribus, there is a negative association between the change in the net

margin of a firm and the abnormal change in provision for credit loss on trade

receivables.

Performance

Variables

Change in

Gross Margin

Change in Net

Margin

Abnormal Change in



Change in

T/Rec Days


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39

H9 – Ceteris paribus, there is a negative association between the change in the

average trade receivables collection period of a firm and the abnormal change in


Where: companies abnormally underprovide, to inflate earnings, in order to maintain

a positive top line performance through to the final earnings performance or to detract

attention from an increasing average trade receivables collection period.

Table 5.3 – Performance Related Determinants of Earnings Management


Zhang (2006) - Jeffrey et al (2008).

Figure 5.4 – Governance Specific Determinants of Earnings Management

The final No. Of Gov. Non Compliance Issues variable has been introduced to

enhance the robustness of this study. Grant Thornton (2011) determines that only half

of all FTSE 350 companies were fully compliant with the Combined Code during

their 2011 review. As a result, the No. of Gov. Non Compliance Issues variable is

selected as a proxy for overall governance best practice within a FTSE 350 company.

Moreover, it is hypothesised that previously robust variables such as the proportion of

INEDs to the Audit Committee may no longer be robust, given the emergence of

corporate governance best practice in recent years.

Governance

Variables

INEDSs to

Total Board

INEDs to Audit

Committee

Abnormal Change in



No. Of Audit

Committee

Meetings

No. Gov. Non

Compliance

Issues


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40

H10 – Ceteris paribus, there is a positive association between the proportion of INEDs

to the total board of directors and the abnormal change in provision for credit loss on

trade receivables.

H11 – Ceteris paribus, there is a positive association between the proportion of INEDs

to the total audit committee and the abnormal change in provision for credit loss on

trade receivables.

H12 – Ceteris paribus, there is a positive association between the number of audit

committee meetings held during the financial year and the abnormal change in


H13 – Ceteris paribus, there is a negative association between the number of firm

specific governance non-compliance issues and the abnormal change in provision for


Where: robust corporate governance structures mitigate the propensity towards

earnings inflation activity, resulting in abnormal overprovision, categorised as prudent

activity throughout this study.

Table 5.4 – Governance Specific Determinants of Earnings Management

Literature Source: Literature Review: Sections 3.3.6 – 3.3.7.

Dechow et al (1996) - Beasley (1996)

Peasnell et al (2005) - Chen (2006) - Lin et al (2006).

Figure 5.5 – Auditor Related Determinants of Earnings Management

H14 – Ceteris paribus, there is a positive association between auditor type and the

abnormal change in provision for credit loss on trade receivables.

H15 – Ceteris paribus, there is a positive association between audit specific fees and

the abnormal change in provision for credit loss on trade receivables.

Auditor

Variables

Auditor Type

Audit Specific

Fee(s)

Abnormal Change in




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41

Where: Big 4 auditors are more effective in constraining the abnormal underprovision

activity of their clients and where higher audit specific fees mitigate earning inflation

activity, resulting in abnormal overprovision.

Table 5.5 – Auditor Related Determinants of Earnings Management


Francis and Krishnan (1999) - Frankel et al (2002)

Krishnan (2003) - Lin et al (2006) - Jordan et al (2010)

5.5.3 – Objective Three: Hypothesis

In developing the following hypothesis, extreme abnormal underprovision for credit

loss on trade receivables is defined as earnings inflation activity. While extreme

abnormal overprovision may also be defined as earnings deflation activity, such

overprovision is categorised as being prudent rather than representing earnings

deflation activity in an environment of elevated credit risk.

H16 – Ceteris paribus, FTSE 350 companies with extreme abnormal underprovision

for credit loss on trade receivables (and as a result lower quality earnings) experience

an inferior stock price performance, post financial year end, relative to FTSE 350

companies with extreme abnormal overprovision for credit loss on trade receivables.

Table 5.6 – Capital Market Response to Earnings Management

Literature Source: Literature Review: Sections 3.4.1 – 3.4.3.

Holthausen et al (1995) - Teoh et al (1998)

Sloan (1996) - Chan et al (2001).

5.6 Research Approach

The research approach of this study is relatively consistent with prior earnings

management research, with the use of correlation and regression quantitative

techniques to analyse earnings management activity. Webster (1995, p.621) states that

while: “Regression determines if X and Y exhibit a positive relationship, or if the

relationship is negative in that they move in opposite directions, correlation measures

how strong the relationship is between X and Y” .


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42

The breadth of this study is, however, more extensive than prior research, in

considering the magnitude and determinants of earnings management activity, along

with the response of capital markets. Prior research has generally only examined two

of these facets simultaneously. However, such studies have often had access to

evidence of earnings management, primarily through the U.S. GAO accounting

restatement database (Jeffrey et al, 2008), eliminating the need to determine the

existence of earnings management.

5.6.1 Dependent Variable: Earnings Quality and Earnings Management

Where such evidence is not available, the proxy measure utilised for earnings quality

or evidence of earnings management generally comprises a measure of accounting

choice, including discretionary accruals or a measure of earnings irregularity relative

to analyst consensus expectations. Prior research, including Ricci (2011) has

examined firms specifically subject to U.S. SEC2 enforcement actions. As no earnings

restatement database is available in an IFRS compliant financial reporting context,

this study firstly determines the existence of abnormal provision for credit loss on

trade receivables, utilising this measure as an indicator of earnings quality and as a

proxy for earnings management activity. In conducting univariate and multivariate

regression analyses, the primary dependent variable comprises the relative change in

the provision for credit loss on trade receivables after controlling for the relative

change in gross trade receivables (Section 5.9.1) (Lev and Thiagarajan, 1993).

5.7 Sample Selection Process

5.7.1 Sample Selection Context

This study is undertaken to examine earnings quality and earnings management in a

European, IFRS compliant financial reporting context. As a result, the FTSE 350

Index, a market capitalisation weighted index incorporating all FTSE 100 and FTSE

250 companies is chosen as the initial sample population. This ensures consistency

with previous studies including Beasley (1996), Zhang (2006) and Jeffrey et al (2008)

which all contain large sample populations of greater than 100 firms.

2 U.S. SEC refers to the United States Securities and Exchange Commission.


___________________________________________________________________________

43

5.7.2 Sample Selection Refinement

The initial total sample of 350 companies relates to the composition of the FTSE 350

Index on 07 June 2012. Since this study has been undertaken, the researcher is aware

of only one company, Supergroup PLC; that has been demoted from the FTSE 350

Index. Company sector classification has been undertaken in accordance with the

FTSE 350 sector classifications of the London Stock Exchange (2012). Consistent

with prior studies, including Burgstahler and Dichev (1997), banks, financial and

financial related institutions are specifically excluded, given the significant variance

in their capital structures. Moreover, Collins et al (1995) already document the

existence of earnings management through abnormal provisioning activity within

banks, further supporting their exclusion from this study.

5.7.3 Sample Selection Refinement – Specific Elimination Procedures

Companies within sectors classified as Banks, Equity Investment Instruments,

Financial Services, Life Insurance, Non-Equity Investment Instruments, Nonlife

Insurance, Real Estate Investment and Services and Real Estate Investment Trusts

were therefore specifically excluded. Ruspetro PLC, admitted to the FTSE 350 Index

during early 2012, had not published any financial statements as at 07 June 2012 and

was therefore excluded from the study. After exclusion of these companies, the total

sample consisted of 241 companies.

A preliminary examination of their financial statements resulted in the exclusion of a

further 31 companies, due to the omission of information with regard to the provision

for credit loss on trade receivables or where summary receivables were presented, that

comprised significantly of VAT and other non-trade components. Of the remaining

210 companies, a further six were excluded where the extent of the relative change in

their provision for credit loss on trade receivables, after controlling for the relative

change in gross trade receivables, was extreme relative to the total sample population.

The characteristics of these six companies are discussed in the Research Findings

chapter. Full details surrounding the final sample population of 204 companies are

contained in Appendix C, while a summary of sample selection refinement is

contained in Table 5.7 overleaf.


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44

Table 5.7 – Sample Selection Refinement Summary

Total

Initial FTSE 350 Population 350

Exclusion of Financial Firms -108

Annual Report Unavailable -1

Insufficient Information – (Abnormal) -31

Outlier Firms -6

Final Sample 204

Information regarding the sector specific composition of the final sample of 204

companies is also contained in Appendix C.

5.8 Data Sources

Previous earnings management studies, including Zhang (2006) and Jeffrey et al

(2008) utilise the Compustat and CRSP3 databases for complete data collection. While

the Thomson One Banker database is used extensively throughout this study, in-depth

analysis of individual annual reports and disclosure notes is also necessary to collect

firm specific data relating to trade receivables, the provision for credit loss on trade

receivables, along with contractual, governance and auditor related variables.

The latest available annual reports for all companies were downloaded from the

Investor Relations sections of companies’ websites on 07 June 2012. A summary

analysis of the financial year end dates of the final sample of 204 companies is

contained in Table 5.8 below.

Table 5.8 – Financial Year End Dates Summary Analysis

Total

Financial Year End Date - 2010 1



Total Sample 204

3 CRSP refers to the Center for Research in Security Prices.


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45

While these financial year end dates are spread across a total of some 15 months

(31 December 2010 – 31 March 2012), the researcher deems this to be appropriate for

three reasons. Firstly, the datasets in previous studies, including Frankel et al (2002)

and Chen (2006) are multiannual in nature, with earnings management occurring in

alternating periods. Secondly, the exclusion of companies with a latest available

annual report for the year ended during 2011 or 2012 would have significantly

reduced the total sample size. Through the selection of the latest available annual

report for each firm, a degree of consistency is ensured. Thirdly, this study is

undertaken in an environment of elevated credit risk, which has remained elevated

post the 2008 financial crisis. Indeed, credit risk across Europe has further

deteriorated between 2011 and 2012 (EFMA, 2012). This inherent control ensures

that all data within the above range is gathered in the context of elevated credit risk.

Initial explanatory data was downloaded from the Thomson One Banker database on

15 June 2012. Data relating to the stock price performance of the 50 most extreme

abnormal providers for credit loss on trade receivables was downloaded on 26 June

2012. The source for data underlying each significant measure used in this study is

detailed in Table 5.9 below.

Table 5.9 – Data Sources for Data Measures

Data Measure Source Data Measure Source

Gross Trade Receivables AR Prop. Of INEDS to Total Board AR

Provision For Credit Loss On T/Rec AR Prop. Of INEDS to Audit Comm. AR

Consensus EPS Growth (%) TO # Of Gov. Non Compliance Issues AR

Earnings (EPS) Surprise (%) TO # Of Audit Committee Meetings AR

Existence of Bonus Plan AR Auditor Type AR

Exec. Incentive Remuneration AR Revenue AR

Change in Gross Margin (%) TO Audit Specific Fee(s) AR

Change in Net Margin (%) TO Stock Price Performance TO

Change in Gearing (%) TO Stock Beta Value TO

Change in Trade Rec. Days TO Total Assets TO

In Table 5.9 above, AR refers to the annual report of a company while TO refers to

the Thomson One Banker database.


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46

5.9 Data Measures Utilised

5.9.1 Primary Dependent Variable

As previously outlined, the primary dependent variable utilised in this study

comprises the relative change in the provision for credit loss on trade receivables after

controlling for the relative change in total gross trade receivables, which is calculated

as follows:

Measure of Abnormal Provision – Proxy for Earnings Management Activity

{ }-{ }: Where: EQ = Earnings quality.

Prov. (t) = Provision for credit loss on trade receivables in latest financial period.

Prov. (t-1) = Provision for credit loss on trade receivables in previous financial period.

GTR (t) = Gross trade receivables in latest financial period.

GTR (t-1) = Gross trade receivables in previous financial period.

Utilisation of this measure is directly consistent with Lev and Thiagarajan (1993).

However, this study reverses the direction of the measure to assist in classification of

the direction of abnormal provision, where underprovision results in a negative value

and overprovision results in a positive value. In doing so, this study does not deviate

from the underlying measure of Lev and Thiagarajan (1993), in measuring the relative

change in, and difference between, both variables.

Consistent with Lev and Thiagarajan (1993), this study attaches a single interpretation

to this measure, where abnormal underprovision is defined as earnings inflation

activity, in an environment of elevated credit risk. Throughout this study, this measure

is employed as the primary measure of earnings quality and proxy for earnings

management activity.

Prov. (t) - Prov. (t-1)

Prov. (t-1)

X 100

GTR (t) - GTR (t-1)

GTR (t-1)

X 100 EQ


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47

5.9.2 Additional Variables and Measures

The following is an overview of the dependent variable utilised in determining

whether the extent of abnormal provision for credit loss on trade receivables

significantly explains subsequent stock price performance. Full explanatory detail

relating to all additional measures employed in this study is contained in Appendix G.

Stock Price Performance %

{ } Where: Price Close (t) = Stock price close on 07

June 2012.

Price Close (t-1) = Stock price close on date of latest available financial year end.

While this stock price performance measure results in a variation in the period of

stock price performance analysis, it ensures a degree of consistency where the

response of capital markets to the latest available annual financial information of a

firm is considered. The 07 June 2012 is selected as the performance period cut-off

point, given that the study sample population was gathered on this date. This approach

is consistent with Dechow et al (2007), in analysing the raw stock price performance

of companies surrounding earnings manipulation years.

5.10 Data Validity and Reliability

Howell (2009, p.53) states that outliers deserve special attention as they may represent

erroneous measurement or recording procedures. While the six outliers identified in

this study were confirmed as being legitimate values, they were specifically excluded

and subject to individual analysis, in order to maintain the assumption of normal data

distribution and to enhance the reliability of regression analyses. The final dataset

relating to the magnitude and determinants of earnings management was complete,

without any missing variables. However, stock price performance data was

unavailable for two companies, therefore the subsequent most extreme abnormal

(under and over) providers for credit loss on trade receivables replaced these

companies, to ensure that the stock price performance sample population consisted of

an equal number of extreme abnormal under and over providers.

Price Close (t) – Price Close (t-1)

Price Close (t -1)

X 100


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48

5.11 Testing Procedures

The Microsoft Excel 2010 Data Analysis Toolpak is utilised to undertake univariate

and multivariate analyses to examine H1 through H16 inclusive. Consistent with Chen

(2006), multivariate OLS regression analyses comprises the majority of statistical

analysis undertaken, while descriptive statistics relating to each variable are also

generated. Although the multivariate analyses conducted is consistent with best

practice, it is subject to both inherent limitations and assumptions (Webster, 1995,

p.638). Extensive testing procedures that ensure compliance with these assumptions

are contained in Appendix G.

5.12 Limitations

In utilising abnormal provision for credit loss on trade receivables as the sole

indicator of earnings quality and proxy for earnings management activity, it is

possible that instances of earnings management through alternative abnormal

provisioning or discretionary accruals remain undetected. Equally, while companies

may have engaged in significant abnormal provision for credit loss on trade

receivables, they may not be engaged in earnings management across other variables,

with earnings management therefore insignificant in an overall context. While a single

earnings inflation motive is attached to abnormal underprovision for credit loss on

trade receivables, consistent with previous research, this approach disregards the

possibility that abnormal overprovision may represent earnings deflation activity.

Additionally, underprovision may well be justified, where the credit risk attaching to

specific customers has declined significantly.

These limitations are, however, considerably moderated, given that this study is

undertaken in an environment of elevated credit risk, rendering any underprovision

suspect. Moreover, given the pressure on firm specific revenues in a difficult

macroeconomic environment combined with capital market earnings expectations, it

is more unlikely that companies would engage in earnings deflation activity at the

present time. This study also assumes that the prior year provision for credit loss on

trade receivables is representative of steady state, un-managed, normal provisioning.

It is possible that provisioning activity was elevated during 2008 and 2009, amidst the

core of the financial crisis, with companies now reducing their provision for credit

loss on trade receivables in subsequent years.


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49

5.13 Conclusion

This chapter has provided an overview of the methodology underlying this study,

along with extensive detail regarding the research questions, objectives and

hypotheses that are subject to testing. The rationale for the sample selection

refinement procedures adopted and data measures selected has been discussed,

providing a firm basis for the research findings discussed in the following chapter.

Chapter 6

Research Findings

“Competency in mathematics, both in numerical manipulations and in

understanding its conceptual foundations, enhances a person’s ability to

handle the more ambiguous and qualitative relationships that dominate

our day-to-day financial decision-making”.

Alan Greenspan (2007)

Chapter 6 Research Findings

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51

CHAPTER SIX

RESEARCH FINDINGS

6.1 Introduction

The findings of the statistical analyses undertaken are presented sequentially in this

chapter, consistent with the order of the research objectives. Starting with an analysis

of the identified outliers; results relating to the three primary research objectives are

then presented and discussed. The discussion and presentation of findings relating to

each research objective begins with a presentation of descriptive statistics, followed

with the results of univariate and multivariate regression analyses before concluding

with acceptance or rejection of the research hypotheses.


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52

6.2 Analysis of Identified Outliers

Outliers in this study are identified with reference to the primary dependent variable.

Six companies, summarised in Table 6.1 below, were identified as having extreme

abnormal provision for credit loss on trade receivables relative to the total sample,

which resulted in their exclusion from all subsequent analysis and reduced the final

sample population to 204 companies.

Table 6.1 – Six Identified Outliers

Company Name Abnormal Provision For Credit Loss

HERITAGE OIL PLC -2262.5%

SOCO INTERNATIONAL PLC -821.9%

KENMARE RESOURCES PLC -294.1%

FILTRONA PLC 156.3%

RANK GROUP PLC 181.0%

INTL CON. AIRLINES GROUP PLC 199.4%

The extent of abnormal underprovision was considerably more extreme than the

extent of abnormal overprovision amongst these six outliers. 33 per cent of outliers

comprised companies in the Oil and Gas sector - Heritage Oil PLC and SOCO

International PLC. A further 33 per cent comprised companies in the Travel and

Leisure sector - Intl Consolidated Airlines Group PLC and Rank Group PLC.

However, the moderately excessive overprovision in the case of Intl Consolidated

Airlines Group PLC arose through business combination activities. The remaining

two outliers - Kenmare Resources PLC and Filtrona PLC, comprised companies in the

Mining and Support Services sectors respectively. All six outliers were subsequently

excluded from further analysis, while Appendix D details the impact of these outliers

upon preliminary testing.

6.3 Magnitude of Abnormal Provision for Credit Loss on Trade Receivables

6.3.1 Descriptive Statistics

During the latest period, the mean relative increase in gross trade receivables is

measured at 12.0 per cent, with a corresponding mean relative increase in the

provision for credit loss on trade receivables of only 2.1 per cent. The mean level of

abnormal provision is therefore measured at -9.9 per cent (Table 6.2 overleaf).


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53

Table 6.2 – Descriptive Statistics for Magnitude of Abnormal Provision

Magnitude of Abnormal Provision Mean Std

Dev Min Max Skew

Abnormal Provision for Credit Loss (%)* -9.90 0.40 -141.60 135.30 -0.10

Change in Gross Trade Receivables (%)* 12.00 0.30 -85.20 163.56 1.80

Change in Provision for Credit Loss (%)* 2.10 0.40 -73.28 191.20 2.10

All Percentages (Indicated*) are converted to Percentage Point Scores through Multiplication – (X 100)

The mean level of abnormal provision indicates that there was an average

underprovision for credit loss on trade receivables of 9.9 per cent, with a standard

deviation value of 0.4 suggesting that the majority of values lie close to this mean.

Of the total sample of 204 FTSE 350 companies, 138 or 67.7 per cent exhibited

abnormal underprovision during the period. Consistent with the range of abnormal

provision for credit loss on trade receivables, both the relative change in gross trade

receivables and the relative change in the provision for credit loss on trade receivables

exhibit wide ranges of -85.2 per cent to 163.6 per cent and -73.28 per cent to 191.2

per cent respectively. The skewness value of -0.1 also indicates that the abnormal

provision for credit loss on trade receivables variable is normally distributed.

6.3.2 Univariate Analysis: Simple Linear Regression Analysis

OLS simple regression analysis is utilised to determine the extent to which the

relative change in gross trade receivables explains the relative change in the provision

for credit loss on trade receivables, as denoted in Figure 6.1 below.

Figure 6.1 – Simple Linear Regression Equation

Table 6.3 - Simple Linear Regression Significance Statistics

β0 Constant R Square 0.103

Coefficient -0.020 Adj. R Square 0.099

T-Stat -0.816 F Stat 23.229

P-Value 0.415 P-Value F Stat 0.000

∆% in Prov. for Credit Loss on T/Rec = β0 + β1∆% in Gross Trade Receivables + 𝜀


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54

Table 6.3 - Simple Linear Regression Significance Statistics Continued

Detail β1 ∆% in GTR

Predicted Sign +

Coefficient 0.340

T-Stat 4.820

P-Value 0.000

As anticipated, the coefficient of β1 (0.340) is positive, with a P-Value of 0.000

indicating significance at the 1% level, confirming that there is a significant positive

relationship between the relative change in gross trade receivables and the relative

change in the provision for credit loss on trade receivables. The regression

significance P-Value of 0.000 indicates that the model has explanatory power at all

levels of significance. While both variables exhibit a positive association, the Adj. R

Square value of 0.099 indicates that only 9.9 per cent of the variation in the relative

change in provision for credit loss on trade receivables is explained by the relative

change in gross trade receivables. H1 is therefore accepted.

6.4 Determinants of Abnormal Provision for Credit Loss on Trade Receivables

6.4.1 Compliance with Underlying OLS Regression Analysis Assumptions

The utilisation of univariate and multivariate regression analyses requires compliance

with the underlying assumptions of OLS regression analysis, including correct model

specification, normal distribution of the error observations (𝜀i) and the absence of

multicollinearity between the independent explanatory variables. As the results of all

procedures contained in Appendix G verify compliance with these underlying

assumptions, the researcher deems all findings relating to both univariate and

multivariate analyses conducted to be reliable.

6.4.2 Descriptive Statistics: Independent Explanatory Variables

Explanatory detail relating to all independent variables is detailed in Appendix G.

Of the 14 independent explanatory variables utilised in multivariate regression

analysis, 12 are continuous in nature, while two are categorical in nature (Table 6.4

overleaf).


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55

Table 6.4 – Descriptive Statistics for Categorical Independent Variables

FREQ 1 FREQ 0

β3 Existence of Bonus Plan 202 2

β13 Auditor Type 198 6

As indicated in Table 6.4 above, 202 or 99 per cent of the 204 FTSE 350 companies

operated bonus or incentive remuneration related plans during the period, while 198

or 97 per cent of the 204 FTSE 350 companies were audited by a Big 4 auditor.

Table 6.5 – Descriptive Statistics for Continuous Independent Variables

Determinants of Earnings Mgmt. Mean Std

Dev Min Max Skew

β1 Consensus EPS Growth (%)* 34.91 1.56 -63.48 1760.00 8.72

β2 Earnings (EPS) Surprise (%)* 0.33 0.21 -185.00 97.00 -4.24

β4 Executive Incentive Remuneration 0.36 0.18 0.00 0.89 -0.41

β5 Change in Gearing (%)* 0.09 5.77 -23.44 21.00 0.21

β6 Change in Gross Margin (%)* 1.03 6.63 -18.39 51.89 3.73

β7 Change in Net Margin (%)* 0.04 10.31 -72.95 41.28 -2.20

β8 Change in Trade Rec. Days 0.88 20.41 -74.93 239.65 7.66

β9 Proportion of INEDs to the Total Board* 53.95 0.10 28.57 80.00 0.08

β10 Proportion of INEDs to Audit Comm.* 98.00 0.07 50.00 100.00 -5.48

β11 Of Governance Non Compliance Issues 0.74 1.09 0.00 8.00 2.68

β12 Of Audit Committee Meetings 4.32 1.58 2.00 14.00 2.39

β14 Audit Specific Fee(s) 0.08 0.06 0.01 0.44 1.85


Of the 12 continuous independent variables, both the Earnings (EPS) Surprise (%)

and Proportion of INEDs to Audit Committee variables are significantly negatively

skewed (Table 6.5 above), while the Consensus EPS Growth (%) variable exhibits

significantly positive skew, indicating abnormal distribution in these instances.

The skewness values of the Executive Incentive Remuneration, Change in Gearing

and Proportion of INEDs to the Total Board variables indicate normal distribution.


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56

The mean of the Consensus EPS Growth (%) variable (34.91%) indicates that analyst

expectations were for significant EPS and earnings growth during the latest period,

with an extreme EPS growth rate expectation of 1760% in one instance. The mean

values of the Proportion of INEDs to the Total Board (53.95%), Proportion of INEDs

to Audit Committee (98.00%) and No. of Governance Non Compliance Issues (0.74)

variables indicate generally strong compliance with corporate governance best

practice and the Combined Code amongst the 204 FTSE 350 companies.

Finally, the mean increase of 0.88 days in the average trade receivables collection

period (Change in Trade Rec. Days) indicates that there was very limited extension of

trade credit during the period amongst the 204 FTSE 350 companies.

6.4.3 Univariate Analysis: Simple Linear Regression Analysis

OLS simple regression analysis was conducted to examine the extent of univariate

relationships between the primary dependent variable: Earnings Quality (proxy for

earnings management activity) and each of the previously identified 14 independent

variables. Initially undertaken with the full sample of 204 companies, simple

regression analysis was subsequently restricted to the 138 identified underproviders,

as the hypotheses in this study are conceptualised in the context of earnings inflation

activity.

Figure 6.2 – Simple Linear Regression Equation

Where:

Earnings Quality = Abnormal provision for credit loss on trade receivables and proxy

for earnings management. βXi = Each of the 14 independent explanatory variables.

β0 = Intercept and 𝜀 = Regression error term.

The results of simple linear regression analysis are contained in Table 6.6 overleaf.

Earnings Quality = β0 + βXi + 𝜀


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Table 6.6 – Simple Regression Analysis Results: Full Sample (N=204)

Hypo. Variable Pred.

Sign Coefficient P-Value

2 β1 Consensus EPS Growth (%) - -0.023 0.181

3 β2 Earnings (EPS) Surprise (%) - 0.188 0.151

4 β3 Existence of Bonus Plan - 0.046 0.866

5 β4 Executive Incentive Remuneration - -0.048 0.866

6 β5 Change in Gearing (%) - -0.011 0.022

7 β6 Change in Gross Margin (%) - 0.004 0.369

8 β7 Change in Net Margin (%) - 0.003 0.203

9 β8 Change in Trade Rec. Days - -0.001 0.669

10 β9 Proportion of INEDs to the Total Board + 0.543 0.036

11 β10 Proportion of INEDs to Audit Committee + 0.246 0.524

12 β11 # Of Governance Non Compliance Issues - -0.008 0.748

13 β12 # Of Audit Committee Meetings + 0.008 0.655

14 β13 Auditor Type + 0.010 0.524

15 β14 Audit Specific Fee(s) + -0.270 0.555

Bolded P-Values in the above Table indicate statistical significance at the 5% level.

As indicated in Table 6.6 above, 12 of the 14 independent variables exhibit no

significant influence on the direction of abnormal provision for credit loss on trade

receivables. However, the bolded P-Values, significant at the 5% level, indicate that

both the Change in Gearing and Proportion of INEDs to the Total Board variables

have a significant influence on the direction of abnormal provision for credit loss on

trade receivables, with the signs of the coefficients consistent with those hypothesised

in both instances.

Table 6.7 – Simple Regression Analysis Results: Underproviders Only (N=138)





4 β3 Existence of Bonus Plan - -0.134 0.508

5 β4 Executive Incentive Remuneration - 0.038 0.485


7 β6 Change in Gross Margin (%) - -0.009 0.039


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Table 6.7 – Simple Regression Analysis: Underproviders Only (N=138)




9 β8 Change in Trade Rec. Days - 0.001 0.587


11 β10 Proportion of INEDs to Audit Committee + -0.143 0.641



14 β13 Auditor Type + 0.377 0.022*

15 β14 Audit Specific Fee(s) + -0.189 0.628

Bolded P-Values in the above Table indicate statistical significance at the 5% or 10% level.

Table 6.7 above indicates that amongst the restricted sample of 138 underproviders,

the Change in Gross Margin and Proportion of INEDs to the Total Board variables

significantly influence the direction of abnormal provision for credit loss on trade

receivables at the 5% level. The Change in Gearing also exhibits significance at the

10% level. The signs of the coefficients of all significant variables are also consistent

with those hypothesised. While the Auditor Type variable exhibits an apparent

significant relationship (0.022*), this result contrasts sharply with the previous

univariate analysis and is both deemed to be skewed and disregarded, given that this

variable is not normally distributed, where only three of the 138 abnormal

underproviders were audited by a non-Big 4 auditor.

6.4.4 Multivariate Analysis: Multiple Regression Analysis

OLS multiple regression analysis was conducted to examine the extent of multivariate

relationships between the primary dependent variable: Earnings Quality (proxy for

earnings management activity) and each of the previously identified 14 independent

variables. The analysis was also further refined with the exclusion of insignificant

variables, restriction of the analysis to the 138 identified underproviders, along with

multiple regression analysis undertaken with the secondary dependent variable as

detailed in Appendix D.

Regression One: Fourteen Hypotheses Regression (N=204)

Regression one consists of the full sample of 204 FTSE 350 companies and tests all

fourteen hypotheses as indicated in Figure 6.3 overleaf.


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Figure 6.3 – Multiple Regression One Equation

The title of each variable in the equation is shortened in the interest of brevity.

Table 6.8 - Multiple Regression One Significance Statistics (N=204)



T-Stat -0.921 F Stat 1.215


The results contained in Table 6.8 above indicate limited explanatory power, with a

regression significance P-Value of 0.266 indicating insignificance at all levels. The

Adj. R Square value of 0.016 indicates that the model only explains 1.6 per cent of the

variation in the direction of abnormal provision for credit loss on trade receivables.

The results for all 14 hypotheses tested are contained in Table 6.9 below-overleaf.

Table 6.9 - Multiple Regression One: Fourteen Hypotheses (N=204)





4 β3 Existence of Bonus Plan - 0.010 0.971






Earnings Quality = β0 + β1 EPS GROWTH+ β2 EPS SURPRISE + β3 BONUS

+ β4 REMUN + β5 GEARING + β6 GROSS MARGIN

+ β7 NET MARGIN + β8 T/REC DAYS + β9 INEDS BOARD

+ β10 INEDS AUDIT + β11 GOV NON COMP

+ β12 AUDIT MEET + β13 AUDITOR + β14 AUDIT FEES + 𝜀


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Table 6.9 - Multiple Regression One: Fourteen Hypotheses Continued



9 β8 Change in Trade Rec. Days - -0.001 0.589


11 β10 Proportion of INEDs to Audit Committee + 0.206 0.645

12 β11 # Of Governance Non Compliance Issues - 0.014 0.646

13 β12 # Of Audit Committee Meetings + -0.001 0.960

14 β13 Auditor Type + -0.061 0.709

15 β14 Audit Specific Fee(s) + 0.221 0.650


As outlined in Table 6.9, only three variables exhibit a statistically significant

relationship with the direction of abnormal provision for credit loss on trade

receivables: the Change in Net Margin (10% level), Change in Gearing (10% level),

and Proportion of INEDs to the Total Board (5% level). Moreover, the positive

coefficient of the Change in Net Margin variable is contrary to that hypothesised,

while the P-Values of several variables highlight their insignificance.

Regression Two: Seven Hypotheses Regression (N=204)

Regression two consists of the full sample of 204 FTSE 350 companies and tests

seven hypotheses, having eliminated the most insignificant variables, as indicated in

Figure 6.4 below.

Figure 6.4 – Multiple Regression Two Equation


The results contained in Table 6.10 overleaf indicate a marked improvement in the

explanatory power of the model, with a regression significance P-Value of 0.023 and

an Adj. R Square value of 0.046 indicating that the model explains 4.6 per cent of the

variation in the direction of abnormal provision for credit loss on trade receivables.

Earnings Quality = β0 + β1 EPS GROWTH + β2 EPS SURPRISE + β4 REMUN

+ β5 GEARING + β6 GROSS MARGIN + β7 NET MARGIN

+ β9 INEDS BOARD + 𝜀


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Table 6.10 - Multiple Regression Two Significance Statistics (N=204)



T-Stat -2.436 F Stat 2.379


Table 6.11 - Multiple Regression Two: Seven Hypotheses (N=204)











Despite the marked improvement in the explanatory power of the model, only two

variables exhibit statistical significance in determining the direction of abnormal

provision for credit loss on trade receivables: the Change in Gearing (10% level) and

Proportion of INEDs to the Total Board (5% level). Moreover, the signs of the

coefficients of both variables are consistent with those hypothesised.

Regression Three: Fourteen Hypotheses Regression (N=138)

Regression three consists of the restricted sample of 138 underproviders and tests all

fourteen hypotheses as indicated in Figure 6.5 below.

Figure 6.5 – Multiple Regression Three Equation






+ β12 AUDIT MEET + β13 AUDITOR + β14 AUDIT FEES + 𝜀


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Table 6.12 - Multiple Regression Three Significance Statistics (N=138)



T-Stat -0.754 F Stat 1.583


The Adj. R. Square value of 0.056 indicates that the fourteen hypotheses model, when

restricted to the 138 identified underproviders, explains 5.6 per cent of the variation in

the direction of abnormal provision for credit loss on trade receivables. While the

model still retains overall explanatory significance at the 10% level (P-Value 0.093),

a marked reduction in the F Stat is noted relative to regression two.

Table 6.13 - Multiple Regression Three: Fourteen Hypotheses (N=138)






5 β4 Executive Incentive Remuneration - 0.022 0.688






11 β10 Proportion of INEDs to Audit Committee + -0.335 0.360



14 β13 Auditor Type + 0.328 0.060*



As indicated in Table 6.13 above, the Change in Gross Margin (5% level) and

Proportion of INEDs to the Total Board (5% level) variables exhibit a significant


receivables. However, the apparent significance of the Auditor Type variable (0.060*)

is deemed to be skewed and is disregarded (Refer to Table 6.7).


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Regression Four: Four Hypotheses Regression (N=138)

Regression four consists of the restricted sample of 138 underproviders and tests four

hypotheses, having eliminated the most insignificant variables, as indicated in Figure

6.6 below.

Figure 6.6 – Multiple Regression Four Equation


Table 6.14 - Multiple Regression Four Significance Statistics (N=138)



T-Stat -4.489 F Stat 4.370


The significance statistics of regression four are the most robust of all four multiple

regressions. With an Adj. R Square value of 0.089 and a regression significance

P-Value of 0.002, the regression has explanatory power at all levels of significance,

explaining 8.9 per cent of the variation in the direction of abnormal provision for

credit loss on trade receivables, which is to be anticipated given the removal of

insignificant variables.

Table 6.15 – Multiple Regression Four: Four Hypotheses (N=138)






14 β13 Auditor Type + 0.334 0.038*


As indicated in Table 6.15 above, the signs of the coefficients of all four independent

variables are consistent with those hypothesised.

Earnings Quality = β0 + β5 GEARING + β6 GROSS MARGIN + β9 INEDS BOARD

+ β13 AUDITOR + 𝜀


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64

Indeed, two of the four variables: the Change in Gross Margin and Proportion of

INEDs to the Total Board exhibit a significant relationship with the direction of

abnormal provision for credit loss on trade receivables. The apparent significance of

the Auditor Type variable (0.038*) is once again disregarded (Refer to Table 6.7).

6.4.5 H2 to H15: Summary Findings

Acceptance or rejection of the research hypotheses (H2 – H15 inclusive) is undertaken

relative to the results of univariate regression analysis and multiple regressions two

through four inclusive, with the results of multiple regression one excluded given its

insignificant explanatory power.

H2 – H15 are conceptualised with regard to the abnormal change in provision for

credit loss on trade receivables. The dependent variable utilised in the aforementioned

analysis is the prime indicator of such change and also captures the extent of its

abnormality. The results of the analysis, contained in Table 6.16, indicate that:

Table 6.16 – H2 to H15: Summary Findings

There is a significant negative association between the Change in Gearing and the

direction of abnormal provision for credit loss on trade receivables. H6 is

therefore accepted.

There is a significant negative association between the Change in Gross Margin

and the direction of abnormal provision for credit loss on trade receivables. H7 is

therefore accepted.

There is a significant positive association between the Proportion of INEDs to the

Total Board and the direction of abnormal provision for credit loss on trade

receivables. H10 is therefore accepted.

In light of these findings, all additional research hypotheses H2 to H15 not detailed in

Table 6.16 are therefore rejected.


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6.5 Stock Price Performance of Extreme Abnormal Providers

6.5.1 Descriptive Statistics

Descriptive statistics relating to the 25 most extreme abnormal underproviders for

credit loss on trade receivables are firstly presented in Table 6.17 below, while

descriptive statistics relating to the 25 most extreme abnormal overproviders are

presented in Table 6.18 thereafter.

Table 6.17 – Descriptive Statistics for Stock Price Performance: Underproviders

Extreme Abnormal Underproviders Mean Std

Dev Min Max Skew

Raw Stock Price Performance (%)* -11.10 0.28 -81.41 23.55 -0.87

Stock Beta: Risk Variable 0.97 0.60 -0.28 2.38 0.16

Natural Log of Total Assets: Size Variable 2.94 0.51 1.89 3.83 -0.26


As detailed in Table 6.17 above, the 25 most extreme abnormal underproviders

experienced an average post financial year end stock price performance of -11.1%,

with a standard deviation value of 0.28 indicating that the majority of stock price

performance values lie close to this mean. While the raw stock price performance

variable exhibits moderately negative skew, all three variables are relatively normally

distributed. The mean stock beta value of 0.97 indicates an average, normal level of

risk amongst the 25 companies.

Table 6.18 – Descriptive Statistics for Stock Price Performance: Overproviders

Extreme Abnormal Overproviders Mean Std

Dev Min Max Skew

Raw Stock Price Performance (%)* 3.89 0.24 -34.5 67.10 0.63

Stock Beta: Risk Variable 1.09 0.65 0.13 2.81 0.95

Natural Log of Total Assets: Size Variable 3.13 0.55 2.18 3.99 -0.12


As indicated in Table 6.18 above, the 25 most extreme abnormal overproviders

experienced an average post financial year end stock price performance of +3.9%.


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66

The standard deviation value of 0.24 suggests that the majority of stock price

performance values lie close to this mean, while the mean stock beta value of 1.09

indicates an average, slightly greater than normal level of risk amongst the 25

companies. Notably, there is a significant (15%) difference between the average stock

price performance of the 25 most extreme abnormal underproviders and

overproviders, as indicated in Figure 6.7 below.

Figure 6.7 – Mean Stock Price Performance Post Financial Year End

Evidently, the 25 FTSE 350 companies with extreme abnormal underprovision for

credit loss on trade receivables experienced an inferior stock price performance, post

financial year end, relative to the 25 FTSE 350 companies with extreme abnormal

overprovision for credit loss on trade receivables. H16 is therefore accepted.

6.5.2 Multiple Regression Analysis: Stock Price Performance Significance

OLS multiple regression analysis was conducted to examine the extent of multivariate

relationships between the dependent variable: Stock Price Performance

(Methodology: Section 5.9.2) and extreme abnormal provision for credit less on trade

receivables. Both the Beta Value and Natural Logarithm of Total Assets variables

were included to control for both risk and size. The corresponding results are

contained in Appendix D.


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67

When considered in aggregate, the results of regression one through regression three

inclusive (Appendix D) indicate that extreme abnormal underprovision for credit loss

on trade receivables significantly explains resultant post financial year end stock price

performance, while extreme abnormal overprovision is not significantly explanatory

in this regard.

Indeed, the extent of extreme abnormal underprovision for credit loss on trade

receivables exhibits a significant positive (5% level) relationship with resultant stock

price performance, where a 1% increase in earnings quality (reduction in the extent of

abnormal underprovision) results in a 0.416% more positive stock price performance.

6.6 Conclusion

The results of univariate and multivariate analyses, as presented in this chapter,

confirm the widespread existence of mean abnormal underprovision for credit loss on

trade receivables amongst FTSE 350 companies during the latest period.

A statistically significant negative association between the Change in Gearing and

Change in Gross Margin variables and the direction of abnormal provision for credit

loss on trade receivables is identified, with a significant positive association in the

case of the Proportion of INEDs to the Total Board variable. The results also confirm

that FTSE 350 companies with extreme abnormal underprovision for credit loss on

trade receivables experienced an inferior mean post financial year end stock price

performance, relative to those FTSE 350 companies with extreme abnormal

overprovision.

Chapter 7

Discussion

“One can’t say that figures lie. But figures, as used in financial

arguments, seem to have the bad habit of expressing a small part of the

truth forcibly”.

Fred Schwed Jr. (1940)

Chapter 7 Discussion

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CHAPTER SEVEN

DISCUSSION

7.1 Introduction

This chapter examines the implications of the research findings relative to regulation,

the previously discussed empirical research and accounting and economic theory

underlying earnings quality and earnings management. The major themes arising from

the findings are discussed sequentially, in the context of the research questions and

research objectives.


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7.2 Magnitude of Abnormal Provision for Credit Loss on Trade Receivables

7.2.1 Provisioning Activity at Variance with Credit Risk Environment

The aim of research objective one was to quantify the existence, direction and

magnitude of abnormal provision for credit loss on trade receivables amongst

FTSE 350 companies. Amidst a continuously challenging economic environment,

with significant pressure on corporate earnings (Bloomberg, 2012), the mean relative

increase in total gross trade receivables of 12.0 per cent suggests that companies are

increasing the extent of their credit sales, in itself a measure that increases the

inherent risk of default or credit loss, most likely to maintain or stimulate growth in

top line revenues. The mean increase of only 0.88 days in the average trade

receivables collection period also reinforces the concept of elevated credit risk, where

any extension in trade credit has been heavily restricted. Moreover, the mean relative

increase of only 2.1 per cent in the provision for credit loss on trade receivables

provides strong evidence of mean abnormal underprovision of -9.9 per cent, with 138

of the 204 FTSE 350 companies underproviding, despite an environment where

European corporates expect an even stronger deterioration in credit risk during 2012

(Atradius, 2012).

7.2.2 Increased Credit Delinquency: Downside Risk of Elevated Write-Offs

During the latest period, the mean level of trade receivables past due, but not impaired

(where disclosed) amounted to £ 138.8 million, compared with a mean of £ 128.0

million in the previous period, representing a relative increase of 8.4 per cent.

Moreover, the average rate of provision for credit loss on trade receivables amongst

the 204 FTSE 350 companies declined to (5.98%) from (6.41%) in the previous

period. Despite the categorisation of non-impairment, there has been a clear increase

in the level of credit delinquency amongst the 204 FTSE 350 companies, rendering

the identified underprovision all the more suspect. With such widespread relative

underprovision, the downside risk arising from instances of elevated credit loss in the

near term is significant. Should credit risk deteriorate further as anticipated, there is

an underlying risk of increased write-offs and a negative impact upon corporate

earnings as a result of increased provisioning activity - given the current level of

abnormal underprovision.


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71

7.2.3 Regulatory Considerations

While no consistent approach to credit risk assessment was noted amongst the 204

FTSE 350 companies, significant divergence from the disclosure requirements of

IFRS 7 was noted throughout, particularly amongst those companies in the mining

and natural resources sectors. Of the final sample of 204 FTSE 350 companies, only

98 provided detail regarding trade receivables past due and specifically impaired in a

manner consistent with IFRS 7, while 178 provided detail regarding trade receivables

past due and not impaired in a manner consistent with IFRS 7. Such non-compliance

and the resultant information asymmetry provides ample opportunity for manipulation

of the provision for credit loss on trade receivables in the context of earnings

management.

Extensive regulation of the provision for credit loss on trade receivables by the IASB

is clearly impractical, as the impairment of trade receivables is inherently subjective

and unique to each company or sector type. However, enhanced disclosure

compliance with IFRS 7 is fundamentally important, given the extensive

abnormalities identified in this study, where some 90.1 per cent of the variation in the

change in provision for credit loss on trade receivables is explained by factors beyond

the relative change in gross trade receivables.

7.3 Determinants of Abnormal Provision for Credit Loss on Trade Receivables

The aim of research objective two was to develop a multivariate OLS regression

model that examines the applicability of previously identified and alternative

determinants of earnings management to abnormal provision for credit loss on trade

receivables amongst FTSE 350 companies.

7.3.1 Capital Market Variables – Limited Evidence

The significance of capital market determinants, including analyst earnings

expectations, is well documented in previous earnings management research (Healy

and Wahlen, 1998). However, the findings of this study provide only limited evidence

of a relationship between these variables and the direction of abnormal provision for



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Both the Consensus EPS Growth % and Earnings (EPS) Surprise % variables exhibit

an insignificant relationship with the direction of abnormal provision for credit loss on

trade receivables, which is surprising, given their significance in previous studies

including Payne and Robb (1997) and Kasznik (1999). Although the negative

association between the Consensus EPS Growth % variable and the direction of

abnormal provision for credit loss on trade receivables is insignificant, this result

provides limited support for the conception in agency theory that agents engage in

earnings management in order to comply with consensus forecasts (Palliam and

Shalhoub, 2003), where the extent of earnings quality decreases (abnormal

underprovision increases) as consensus earnings forecasts increase.

The insignificant, yet positive association between the Earnings (EPS) Surprise %

variable and the direction of abnormal provision, while surprising, provides limited

evidence that instances of increasing earnings surprise are complemented with

instances of abnormal overprovision for credit loss on trade receivables. While the

204 FTSE 350 companies are clearly not utilising abnormal underprovision to support

instances of positive earnings surprise, the mean earnings surprise value of (0.33%)

indicates that, on average, the total sample exceeded analyst earnings expectations,

consistent with Lopez and Rees (2002).

7.3.2 Contractual Variables – Significant Evidence

Contractual determinants of earnings management examined in this study comprise

the Existence of a Bonus Plan, Executive Incentive Remuneration and the Change in

Gearing. In both univariate and multivariate analyses, the Change in Gearing exhibits

a significant negative relationship with the direction of abnormal provision for credit

loss on trade receivables, at the 5% and 10% levels respectively.

This result, consistent with that hypothesised, indicates that increasing levels of

gearing significantly explain abnormal underprovision for credit loss on trade

receivables, providing support for the earnings inflation motive attached to abnormal

underprovision, where earnings inflation procedures are adopted to reduce the risk of

violating debt covenants (Dechow et al, 1996).


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73

While demonstrating consistency with DeFond and Jimbalvo (1994) and Sweeney

(1994), this result provides strong support for the debt hypothesis of positive

accounting theory, where: there is a positive relationship between an increasing debt

to equity ratio (level of gearing) and the likelihood of the adoption of earnings

inflation procedures specifically (Watts and Zimmerman, 1986). Amongst the 204

FTSE 350 firms, this result indicates that abnormal underprovision for credit loss on

trade receivables is utilised as an instrument for earnings inflation where the level of

gearing increases. However, it may also be that such abnormal underprovision is

utilised to enhance debt holders’ perceptions of the company’s prospects or to engage

in impression management more broadly.

Notably, neither the Existence of a Bonus Plan nor Executive Incentive Remuneration

variables exhibit a significant relationship with the direction of abnormal provision

for credit loss on trade receivables, contrasting sharply with prior research including

Healy and Wahlen (1998), Chen (2006) and the management compensation

hypothesis of positive accounting theory (Watts and Zimmerman, 1986). However,

given that 99 per cent of the 204 FTSE 350 companies operated bonus or incentive

remuneration related plans during the latest period; this variable is clearly no longer

robust in earnings management research. Nevertheless, the insignificant, yet negative

association between the Executive Incentive Remuneration variable and the direction

of abnormal provision for credit loss on trade receivables provides limited evidence

supporting Balsam (1998) and Gaver and Gaver (1998) – where the extent of earnings

quality decreases (abnormal underprovision increases) as executive incentive

remuneration increases.

7.3.3 Performance Variables – Significant Evidence

The Change in Gross Margin variable is found to have a significant negative


receivables in both univariate and multivariate analyses at the 5% level, indicating

that instances of an increasing gross margin significantly explain resultant abnormal

underprovision for credit loss on trade receivables.


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74

While consistent with the findings of Zhang (2006), this result provides additional

support for the earnings inflation motive adopted throughout this study, clearly

highlighting a motivation to maintain positive top line performance through to the

final earnings performance, utilising abnormal underprovision in this regard.

However, neither the Change in Net Margin nor Change in Trade Receivables Days

variables exhibit a statistically significant relationship with the direction of abnormal


7.3.4 Governance Variables – Significant Evidence

The intervening and mitigation effects of robust corporate governance structures on

earnings management are widely acknowledged (Sebahattin and Harlan, 2009).

Governance specific determinants of earnings management examined in this study

comprise the Proportion of INEDs to the Total Board, Proportion of INEDs to the

Audit Committee, No. of Audit Committee Meetings and No. of Governance Non

Compliance Issues. Consistent with Lin et al (2006), no significant relationship is

found between the No. of Audit Committee Meetings variable and the direction of

abnormal provision for credit loss on trade receivables. However, the direction of the

relationship between all four variables and abnormal provision for credit loss on trade

receivables is consistent with that hypothesised, while the Proportion of INEDs to the

Total Board variable exhibits a significant positive (5% level) relationship with the

direction of abnormal provision for credit loss on trade receivables.

These findings provide strong evidence of decreasing earnings inflation activity as the

robustness of corporate governance structures increase, consistent with Dechow et al

(1996), Beasley (1996), Peasnell et al (2005) and Sebahattin and Harlan (2009).

Indeed, with resultant abnormal overprovision for credit loss on trade receivables as

the proportion of INEDs to the total board of directors increases (categorised as

prudence rather than earnings deflation in an environment of elevated credit risk), this

study provides a strong impetus for continued convergence towards corporate

governance best practice, where the board of directors comprises of majority INEDs.


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75

A strong trend towards majority INED board composition has developed in recent

years (Grant Thornton, 2011) and this study confirms that the average board of

directors of the 204 FTSE 350 companies comprised of 53.9% INEDs during the

latest period. Nevertheless, amongst the 204 FTSE 350 firms, 52 reported board

structures that comprised of less than 50% INEDs, while eight reported audit

committee structures that comprised of less than three INEDs. The clear support for

the mitigating effects of robust corporate governance structures on earnings inflation

activity (via abnormal underprovision for credit loss on trade receivables specifically)

highlights the importance of such mechanisms in protecting stakeholders from

earnings management activity.

7.3.5 Auditor Variables – Mixed Evidence

Large auditors with industry specialist knowledge are widely considered to be more

effective in constraining the earnings management activity of their clients (Francis

and Krishnan, 1999). The findings of this study, upon initial examination, provide

some support in this regard, with an apparent significant positive (5% level)

relationship between the Auditor Type variable and the direction of abnormal

provision for credit loss on trade receivables. However, as detailed in Chapter 6, the

abnormal distribution of the Auditor Type (categorical) variable has skewed the

results at the multivariate level and this result is therefore disregarded. Given that 198

or 97 per cent of the final sample were audited by Big 4 firms, this variable clearly

lacks robustness in earnings management research. Contrasting with Frankel et al

(2002), no significant relationship between the Audit Specific Fee(s) variable and the

direction of abnormal provision for credit loss on trade receivables is exhibited.

7.3.6 Non Hypothesised Factors

The Adj. R Square values resulting from regression analyses examining research

objective two range from 1.6 – 8.9 per cent, with a significant extent of the variation

in the direction of abnormal provision for credit loss on trade receivables therefore

explained by factors omitted from the model employed. This result is not surprising

however, given that Dechow et al (2011) document the lack of power in earnings

management models and the relatively low Adj. R Square values recorded in prior

earnings management research; including Frankel et al (2002) and Chen (2006).


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76

The overall decision to manage earnings (abnormally provide for credit loss on trade

receivables) is a complex one; affected by the broader macroeconomic environment

and sector specific variables but also significantly by the economic and political

circumstances of a firm (Peltier-Rivest and Swirsky, 2000). While both the positive

accounting and agency theories provide a firm foundation for the determinants of

earnings management, the decision to manage earnings ultimately comprises many

variables that are simply non-linear and not quantifiable but qualitative in nature.

7.4 Stock Price Performance of Extreme Abnormal Providers

7.4.1 Evidence Supporting the Efficient Market Hypothesis

The aim of research objective three was to examine the individual and aggregate stock

price performance of the most extreme abnormal providers for credit loss on trade

receivables (both under and over providers) over a specified post financial year end

period. The results provide strong evidence that, of the 204 FTSE 350 companies,

those with the most extreme abnormal underprovision for credit loss on trade

receivables experienced an average negative (-11.1%) post financial year end raw

stock price performance, significantly inferior to the performance of the most extreme

abnormal overproviders (+3.9%). This result is directly consistent with Holthausen et

al (1995), Chan et al (2001) and Dechow et al (2007) – where companies with low

quality earnings experience a subsequent negative or inferior stock price performance

post issuance of their financial statements. Contrasting with Sloan (1996), this result

provides support for the efficient market hypothesis, indicating that capital market

participants identify instances of low quality earnings and discount stocks

accordingly, or discount stocks where abnormalities have already been identified.

7.4.2 Capital Markets: A Potential Instrument for Effective Regulation

This result further suggests that capital markets may represent a potential instrument

for regulating accounting abnormalities, somewhat mitigating the effects of

accounting standard non-compliance and earnings management, with the discounting

of stocks where abnormal underprovision is exhibited acting as a warning signal to

prospective investors. Moreover, the abnormal underprovision identified in this study

may also be complemented by a series of additional earnings inflation accounting

abnormalities that further explain the subsequent inferior stock price performance.


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77

7.4.3 Non Hypothesised Factors

Notably, there is a slight difference between the average risk and size profiles of those

extreme abnormal underproviders and overproviders, with extreme abnormal

underproviders exhibiting slightly lower average risk and smaller size profiles relative

to those extreme abnormal overproviders. While neither of these factors was

hypothesised as a determinant of abnormal provision, these results suggest that in

instances of extreme abnormal underprovision; smaller companies with lower risk

profiles, arguably subject to reduced capital market scrutiny and monitoring, may be

availing of such reduced supervision to actively engage in earnings management.

7.5 Conclusion

This chapter, discussing the major themes and findings of this study, has identified a

significant link between positive accounting theory, prior empirical evidence and

extant earnings management practice. Theoretical foundations conceptualised some

forty years ago, including the debt hypothesis of positive accounting theory remain

robust in explaining the motives for earnings management activity. While presenting

strong evidence of widespread mean abnormal underprovision for credit loss on trade

receivables, this chapter also highlights the potential regulatory ability of capital

markets, possibly mitigating the effects and costs of earnings management activity.

Chapter 8

Conclusion

“Some regulation will be necessary, some changes in accounting rules”.

Henry Paulson (2008) – U.S. Department of the Treasury

Chapter 8 Conclusion

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79

CHAPTER EIGHT

CONCLUSION

8.1 Introduction

This final chapter draws the dissertation to a close, summarising the major findings

and conclusions reached. While making recommendations for practitioners including

auditors, accounting standard setters and capital market participants, this chapter also

identifies opportunities for further research.


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80

8.2 Research Questions, Research Objectives and Findings

The following research questions have provided the basis for the analysis undertaken

throughout this dissertation:





The three primary research objectives analysed and resultant major findings include:

Objective One: Key Findings

Objective: to quantify the existence, direction and magnitude of abnormal provision

for credit loss on trade receivables amongst FTSE 350 companies. The mean level of

abnormal underprovision of -9.9 per cent provides strong evidence of provisioning

practice at sharp variance with the current environment of elevated credit risk. The

results also indicate that some 90.1 per cent of the variation in the change in provision

for credit loss on trade receivables is explained by factors other than the relative

change in total gross trade receivables.

Objective Two: Key Findings

Objective: to develop a multivariate OLS regression model that examines the


management to abnormal provision for credit loss on trade receivables amongst FTSE

350 companies.

The results indicate that increasing gross margins significantly explain abnormal

underprovision. Strong support is also exhibited for the debt hypothesis of positive

accounting theory as a determinant of earnings management, where earnings inflation

through abnormal underprovision is undertaken to avoid the risk of violating debt

covenants. Significant support for the intervening and mitigating effects of robust

corporate governance structures on earnings inflation via abnormal underprovision is

also identified, where the board of directors is comprised of an increasing proportion

of INEDs.


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81

Objective Three: Key Findings

Objective: to examine the individual and aggregate stock price performance of the

most extreme abnormal providers for credit loss on trade receivables over a specified

post financial year end period. The results provide support for the efficient market

hypothesis that stock prices fully reflect all publicly available information, where

those FTSE 350 firms with extreme abnormal underprovision experience an inferior

post financial year end stock price performance. Moreover, in regulating accounting

abnormalities through the discounting of stock prices, capital markets are identified as

a potential instrument for mitigating the effects of accounting standard

non-compliance and earnings management.

8.3 Limitations of Research

The findings and conclusions reached throughout this study are subject to a number of

important limitations. It is possible that the attachment of prudence rather than an

earnings deflation motive to abnormal overprovision disregards a considerable degree

of earnings management activity. Although testing procedures validate compliance

with the underlying assumptions of OLS regression analysis, the justification for the

use of the ordinary least squares estimation is diminished where any additional

conditions are not observed entirely. This study considers the most recent change in

the provision for credit loss on trade receivables amongst a refined sample of 204

FTSE 350 companies over the latest available financial period. It does not consider

additional variables, including discretionary accruals, through which earnings

management may be exercised, nor does it adopt a longitudinal approach.

8.4 Recommendations for Practitioners

8.4.1 Auditors

This study highlights the potential for earnings inflation via the provision for credit

loss on trade receivables specifically. In circumstances where audit clients exhibit

increased levels of gearing, increasing gross margins or deficient corporate

governance structures, auditors should dedicate additional resources to examining

both anticipated credit losses on trade receivables and broader provisioning activities.

While not significantly material in isolation, multiple instances of undetected

manipulation via abnormal provisioning can serve to distort overall earnings.


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82

8.4.2 International Accounting Standards Board – Standard Setters

The significant level of non-compliance with the disclosure requirements of IFRS 7

recorded in this study and the resultant information asymmetry provides ample

opportunity for manipulation. Amidst the 2008 financial crisis, Henry Paulson, former

U.S. Secretary of the Treasury, stated that: “Some regulation will be necessary, some

changes in accounting rules”. In a similar vein, while complete regulation of the

provision for credit loss on trade receivables is impractical, the abnormalities

identified in this study provide a strong impetus for at least limited regulatory change.

A mandatory, standardised IFRS 7 reporting format or template, drafted by the IASB,

could serve well in eliminating the current non-compliance, while simultaneously

reducing the potential for manipulation.

8.4.3 Capital Market Participants

Despite strong support for the efficient market hypothesis in this study, both

shareholders and corporate stakeholders should apply detailed scrutiny to the

governance structures and board level composition of companies. Such scrutiny can

help to prevent against adverse asset allocation, investment and corporate lending

decisions – where these decisions are based upon higher quality earnings that are

supported by the existence of robust governance structures.

8.5 Recommendations for Future Research

This study may be expanded in a number of ways. Firstly, a longitudinal analysis of

the provision for credit loss on trade receivables could be conducted, to determine

whether multi-period abnormalities are exhibited and the extent to which the phase of

the business cycle impacts the extent of these abnormalities.

Those companies exhibiting extreme abnormal underprovision for credit loss on trade

receivables could be examined across a broader spectrum of earnings quality

indicators to ascertain the extent of overall earnings quality and earnings management

activity.

Finally, both financial and stock price performance in the years subsequent to

abnormal underprovision could be examined, to determine whether it serves as a

precursor to long run declining performance or financial distress.


___________________________________________________________________________

83

8.6 Conclusion

Writing in 1940 about the practices of Wall Street investors, Fred Schwed Jr. stated

that: “One can’t say that figures lie. But figures, as used in financial arguments, seem

to have the bad habit of expressing a small part of the truth forcibly”. Over 70 years

later, this statement holds significant truth given the abnormalities identified in this

study. In a post 2008 financial crisis world, with corporate financial information more

readily available than ever before and accounting regulation once again in the

spotlight, the opportunity to engage in earnings management is ever diminishing.

However, this study confirms the existence and potential for earnings management

through abnormal provision for credit loss on trade receivables. While excessive

regulation is impractical and may result in disproportionate monitoring costs, this

study provides a strong impetus for at least limited regulatory change.

Regulation, the macroeconomic environment and firm specific variables all fluctuate

over time, however; the fundamentals underlying both the agency and positive

accounting theories remain constant. Where conditions are compliant with either of

these theories, there exists a high risk of earnings management activity through

abnormal provision for credit loss on trade receivables; with potential adverse

implications across a broad spectrum of corporate stakeholders.

___________________________________________________________________________

___________________________________________________________________________

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85

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Appendix A

Personal

Reflection

Appendix A

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96

PERSONAL REFLECTION

The completion of this dissertation has left me with a great sense of achievement,

marking the high point of my experience on the MBS in Accounting programme.

As a student studying accounting over these past four years, I have been perplexed at

the extent of global corporate fraud and manipulation, often achieved through

accounting abnormalities, despite an environment of ever increasing accounting and

corporate regulation. In deciding upon a topic for my dissertation, my interest was

therefore immediately drawn towards the area of accounting manipulation and

earnings management.

After conducting preliminary research, I was surprised to find that a significant

majority of earnings management research to date has been undertaken in a U.S.

GAAP compliant financial reporting context. Having identified a literature gap with

regard to earnings management research in an IFRS compliant financial reporting

context, my attention was subsequently drawn to the methods through which

companies may manage their earnings. Moreover, having a strong personal interest in

the impact of the on-going macroeconomic uncertainty on accounting policy choice

within firms, I felt that it was important to link potential earnings management

activity with the difficulties that firms are currently experiencing.

Applying this logic, I initially considered deferred income recognition as a method of

earnings management and this formed the basis for my dissertation proposal.

However, the current limited disclosure on deferred income recognition rendered this

approach impossible. While discretionary accruals have been examined on numerous

occasions previously, I remained conscious of maintaining both the originality and

relevance of the dissertation and therefore examined individual accounting

mechanisms for earnings management. Thereafter, my focus centred upon

provisioning activity and the provision for credit loss on trade receivables more

specifically, given that credit risk has remained persistently elevated post the 2008

financial crisis. With only limited prior research in this area of earnings management,

it became clear to me that the research approach adopted would require significant

planning and articulation. However, the identifiable literature gap, originality and

relevance of the study spurred me onwards.

Appendix A

___________________________________________________________________________

97

At the time of drafting the dissertation proposal, I envisaged a timeline that included

the data collection process being substantially complete by the end of May.

While this timeline provided a tangible framework to word towards, it quickly

became evident that it was impractical, where on-going study and assignment

pressures consumed the majority of my time. I also materially underestimated the

time consumed on the data collection and analysis process, initially planning for three

weeks; with the actual process taking over 350 hours. However, the challenge of

dealing with these pressures simultaneously has benefited me greatly, instilling the

importance of rational planning while also preparing me for work in practice.

The collection of journal articles and additional literature underlying this study has

also heighted my awareness of the vast theoretical resources available that provide

insights into both earnings management and accounting choice. In order to prioritise

and succinctly summarise these resources, I developed an electronic filing system,

where each item of literature was scored relative to its applicability to the study. I feel

that this system will be of great benefit to me into the future, given the vast array of

data that is managed and processed within the accounting profession.

On reflection, I found the data collection phase of the study to be the most difficult,

where occasionally; it was difficult to appreciate any significant progress despite

many hours of work. As the months of both June and July progressed, the dissertation

was the sole focus of my daily activities, while the time remaining for completion

seemed to diminish rapidly. In retrospect, it would have been more optimal to take

greater rest breaks during this period, as the occasional sense of lethargic progress

was heavily exacerbated by exhaustion.

In completing this dissertation, I have greatly developed my skillset, having

successfully articulated and executed a large project, which enhances my confidence

as I prepare to enter the world of work. The dissertation has provided me with a great

opportunity to enhance my analytical and research capabilities, two qualities that are

fundamentally important in the audit profession, while the subject of the dissertation

is directly associated with the work of an auditor. Finally, having gained practical

experience of statistical analysis, I now appreciate, more than ever before, its

importance in explaining the rationale for the qualitative decisions undertaken that

significantly influence resultant financial performance.

Appendix B

IFRS 7 and IAS 39:

Disclosure Requirements of IFRS 7 and

Accounting Standard Guidance of IAS 39

Appendix B

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99

B.1 Introduction

The purpose of this appendix is to provide an overview of the disclosure requirements

of IFRS 7 and the accounting standard guidance contained in IAS 39, relating to the

provision for credit loss on trade receivables specifically.

B.2.1 Allowance for Credit Losses: IFRS 7.16 – Disclosure Requirements

“When financial assets are impaired by credit losses and the entity records the

impairment in a separate account (eg. an allowance account used to record individual

impairments or a similar account used to record a collective impairment of assets)

rather than directly reducing the carrying amount of the asset, it shall disclose a

reconciliation of changes in that account during the period for each class of financial

assets”.

B.2.2 Financial Assets that are either Past Due or Impaired: IFRS 7.37

“An entity shall disclose by class of financial asset:

a) An analysis of the age of financial assets that are past due as at the end of the

reporting period but not impaired; and

b) An analysis of financial assets that are individually determined to be impaired as

at the end of the reporting period, including the factors the entity considered in

determining that they are impaired”.

B.2.3 Impairment of Financial Assets: IAS 39.58

Section 58 of IAS 39 prescribes that:

“An entity shall assess at the end of each reporting period whether there is any

objective evidence that a financial asset or group of financial assets measured at

amortised cost is impaired. If any such evidence exists, the entity shall apply

paragraph 63 to determine the amount of any impairment loss”.

(International Accounting Standards Board, 2011)

Appendix B

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100


In determining the existence of impairment, Section 59 of IAS 39 prescribes that:

“A financial asset or a group of financial assets is impaired and impairment losses are

incurred if, and only if, there is objective evidence of impairment as a result of one or

more events that occurred after the initial recognition of the asset (a ‘loss event’) and

that loss event (or events) has an impact on the estimated future cash flows of the

financial asset or group of financial assets that can be reliability estimated. It may not

be possible to identify a single, discrete event that caused the impairment. Rather the

combined effect of several events may have caused the impairment. Losses expected

as a result of future events, no matter how likely, are not recognised. Objective

evidence that a financial asset or group of assets is impaired includes observable data

that comes to the attention of the holder of the asset about the following loss events:

Significant financial difficulty of the issuer or obligor;

A breach of contract, such as default or delinquency in interest or principal

payments;

The lender, for economic or legal reasons relating to the borrower’s financial

difficulty, granting to the borrower a concession that the lender would not

otherwise consider;

It becoming probable that the borrower will enter bankruptcy or other financial

reorganisation;

The disappearance of an active market for that financial asset because of financial

difficulties;

Observable data indicating that there is a measurable decrease in the estimated

future cash flows from a group of financial assets since the initial recognition of

those assets, although the decrease cannot yet be identified with the individual

financial assets in the group, including:

o Adverse changes in the payment status of borrowers in the group or

national or local economic conditions that correlate with defaults on the

assets in the group”.

Appendix B

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101


In determining the amount of impairment loss, Section 63 of IAS 39 prescribes that:

“If there is objective evidence that an impairment loss on financial assets measured at

amortised cost has been incurred, the amount of the loss is measured as the difference

between the asset’s carrying amount and the present value of estimated future cash

flows (excluding future credit losses that have not been incurred) discounted at the

financial asset’s original effective interest rate (i.e. the effective interest rate

computed at initial recognition). The carrying amount of the asset shall be reduced

either directly or through the use of an allowance account. The amount of the loss

shall be recognised in the profit or loss”.

(International Accounting Standards Board, 2011)

Appendix C

Details of Final

Sample Population:

Company Details and Sector Specific

Composition of Final Sample Population

Appendix C

___________________________________________________________________________

103

C.1 Introduction

The purpose of this appendix is to provide detail regarding the names and sector

specific composition of the final sample of 204 FTSE 350 companies selected for

analysis in this study. Specific details relating to each individual company are

presented, along with a sector specific composition summary table.

COMPANY NAME FTSE 350 SECTOR

AEGIS GROUP PLC Media

AGGREKO PLC Support Services

AMEC PLC Oil Equipment and Services

ANGLO AMERICAN PLC Mining

ANTOFAGASTA PLC Mining

ARM HOLDINGS PLC Technology and Hardware Equipment

ASHTEAD GROUP PLC Support Services

ASSOCIATED BRITISH FOODS PLC Food Producers

ASTRAZENECA PLC Pharmaceuticals and Biotechnology

AVEVA GROUP PLC Software and Computer Services

AZ ELECTRONIC MATERIALS PLC Chemicals

BABCOCK INTERNATIONAL GROUP PLC Support Services

BAE SYSTEMS PLC Aerospace and Defense

BALFOUR BEATTY PLC Construction and Materials

BARR A.G. PLC Beverages

BARRATT DEVELOPMENTS PLC Household Goods and Home Construction

BBA AVIATION PLC Industrial Transportation

BERENDSEN PLC Support Services

BERKELEY GROUP HOLDINGS PLC Household Goods and Home Construction

BG GROUP PLC Oil and Gas Producers

BHP BILLITON PLC Mining

BODYCOTE PLC Industrial Engineering

BOOKER GROUP PLC Food and Drug Retailers

BOVIS HOMES GROUP PLC Household Goods and Home Construction

BP PLC Oil and Gas Producers

BRITVIC PLC Beverages

BSKYB GROUP PLC Media

BT GROUP PLC Fixed Line Telecommunications

BTG PLC Pharmaceuticals and Biotechnology

BUMI PLC Mining

Appendix C

___________________________________________________________________________

104


BUNZL PLC Support Services

BURBERRY GROUP PLC Personal Goods

BWIN.PARTY DIGITAL ENTER. PLC Travel and Leisure

CABLE & WIRELESS COMM PLC Fixed Line Telecommunications

CABLE & WIRELESS W.W. PLC Fixed Line Telecommunications

CAPE PLC Oil Equipment and Services

CAPITA GROUP PLC Support Services

CARILLION PLC Support Services

CARPETRIGHT PLC General Retailers

CENTRICA PLC Gas, Water and Multiutilities

CHEMRING GROUP PLC Aerospace and Defense

COBHAM PLC Aerospace and Defense

COLT GROUP S.A. Fixed Line Telecommunications

COMPASS GROUP PLC Travel and Leisure

COMPUTACENTER PLC Software and Computer Services

COOKSON GROUP PLC General Industrials

CRANSWICK PLC Food Producers

CRH PLC Construction and Materials

CRODA INTERNATIONAL PLC Chemicals

CSR PLC Technology and Hardware Equipment

DAILY MAIL & GEN TRUST PLC Media

DAIRY CREST GRP PLC Food Producers

DE LA RUE PLC Support Services

DEBENHAMS PLC General Retailers

DEVRO PLC Food Producers

DIAGEO PLC Beverages

DIGNITY PLC General Retailers

DIPLOMA PLC Support Services

DIXONS RETAIL PLC General Retailers

DOMINO PRINTING PLC Electronic and Electrical Equipment

DOMINO'S PIZZA GROUP PLC Travel and Leisure

DRAX GROUP PLC Electricity

DS SMITH PLC General Industrials

EASYJET PLC Travel and Leisure

ELECTROCOMPONENTS PLC Support Services

Appendix C

___________________________________________________________________________

105


ELEMENTIS PLC Chemicals

ENQUEST PLC Oil and Gas Producers

ESSAR ENERGY PLC Oil and Gas Producers

EURAS. NATURAL RES. CORP PLC Mining

EUROMONEY INST INVESTOR PLC Media

EVRAZ PLC Industrial Metals and Mining

EXILLON ENERGY PLC Oil and Gas Producers

EXPERIAN PLC Support Services

FENNER PLC Industrial Engineering

FERREXPO PLC Industrial Metals and Mining

FIDESSA GROUP PLC Software and Computer Services

FIRSTGROUP PLC Travel and Leisure

FRESNILLO PLC Mining

G4S PLC Support Services

GALLIFORD TRY PLC Construction and Materials

GENUS PLC Pharmaceuticals and Biotechnology

GKN PLC Automobiles and Parts

GLAXOSMITHKLINE PLC Pharmaceuticals and Biotechnology

GLENCORE INTERNATIONAL PLC Mining

GO-AHEAD GROUP PLC Travel and Leisure

GREENE KING PLC Travel and Leisure

HALFORDS GROUP PLC General Retailers

HALMA PLC Electronic and Electrical Equipment

HAYS PLC Support Services

HIKMA PHARMACEUTICALS PLC Pharmaceuticals and Biotechnology

HOME RETAIL GROUP PLC General Retailers

HOMESERVE PLC Support Services

HOWDEN JOINERY GROUP PLC Support Services

HUNTING PLC Oil Equipment and Services

IMAGINATION TECH. GROUP PLC Technology and Hardware Equipment

IMI PLC Industrial Engineering

IMPERIAL TOBACCO GROUP PLC Tobacco

INCHCAPE PLC General Retailers

INFORMA PLC Media

INMARSAT PLC Mobile Telecommunications

Appendix C

___________________________________________________________________________

106


INTERCONTINENTAL HOTELS GRP. PLC Travel and Leisure

INTERSERVE PLC Support Services

INTERTEK GROUP PLC Support Services

INVENSYS PLC Software and Computer Services

ITE GROUP PLC Media

JD SPORTS FASHION PLC General Retailers

JOHN WOOD GROUP PLC Oil Equipment and Services

JOHSON MATTHEY PLC Chemicals

KAZAKHMYS PLC Mining

KCOM GROUP PLC Fixed Line Telecommunications

KESA ELECTRICALS PLC General Retailers

KIER GROUP PLC Construction and Materials

KINGFISHER PLC General Retailers

LADBROKES PLC Travel and Leisure

LAIRD PLC Technology and Hardware Equipment

LAMPRELL PLC Oil Equipment and Services

LOGICA PLC Software and Computer Services

LONMIN PLC Mining

MARKS & SPENCER GROUP PLC General Retailers

MARSTON'S PLC Travel and Leisure

MEGGITT PLC Aerospace and Defense

MELROSE PLC Industrial Engineering

MICHAEL PAGE INTERNATIONAL PLC Support Services

MICRO FOCUS INTERNATIONAL PLC Software and Computer Services

MILLENNIUM AND COP. HOTELS PLC Travel and Leisure

MITIE GROUP PLC Support Services

MONDI GROUP PLC Forestry and Paper

MONEYSUPERMARKET.COM PLC Media

MORGAN CRUCIBLE PLC Electronic and Electrical Equipment

MORRISON SUPERMARKETS PLC Food and Drug Retailers

N BROWN GROUP PLC General Retailers

NATIONAL EXPRESS GROUP PLC Travel and Leisure

NATIONAL GRID PLC Gas, Water and Multiutilities

NEXT PLC General Retailers

NORTHGATE PLC Support Services

Appendix C

___________________________________________________________________________

107


OCADO GROUP PLC Food and Drug Retailers

OXFORD INSTRUMENTS PLC Electronic and Electrical Equipment

PAYPOINT PLC Support Services

PEARSON PLC Media

PENNON GROUP PLC Gas, Water and Multiutilities

PERFORM GROUP PLC Media

PERSIMMON PLC Household Goods and Home Construction

PETROFAC LIMITED Oil Equipment and Services

PETROPAVLOVSK PLC Mining

POLYMETAL INTERNATIONAL PLC Mining

PREMIER FARNELL PLC Support Services

PZ CUSSONS PLC Personal Goods

QINETIQ PLC Aerospace and Defense

RANDGOLD RESOURCES LTD Mining

RECKITT BENCKISER GROUP PLC Household Goods and Home Construction

REED ELSEVIER PLC Media

REGUS PLC Support Services

RENISHAW PLC Electronic and Electrical Equipment

RENTOKIL INITIAL PLC Support Services

REXAM PLC General Industrials

RIGHTMOVE PLC Media

RIO TINTO PLC Mining

ROTORK PLC Industrial Engineering

ROYAL DUTCH SHELL PLC Oil and Gas Producers

RPC GROUP PLC General Industrials

RPS GROUP PLC Support Services

SABMILLER PLC Beverages

SAGE GROUP PLC Software and Computer Services

SALAMANDER ENERGY PLC Oil and Gas Producers

SDL PLC Software and Computer Services

SENIOR PLC Aerospace and Defense

SERCO GROUP PLC Support Services

SEVERN TRENT PLC Gas, Water and Multiutilities

SHANKS GROUP PLC Support Services

SHIRE PLC Pharmaceuticals and Biotechnology

Appendix C

___________________________________________________________________________

108


SIG PLC Support Services

SMITH & NEPHEW PLC Health Care Equipment and Services

SMITHS GROUP PLC General Industrials

SPECTRIS PLC Electronic and Electrical Equipment

SPIRAX-SARCO ENGINEERING PLC Industrial Engineering

SPIRENT COMMUNICATIONS PLC Technology and Hardware Equipment

SPORTS DIRECT INTERNATIONAL PLC General Retailers

SSE PLC Electricity

STAGECOACH GROUP PLC Travel and Leisure

STOBART GROUP LTD Industrial Transportation

SUPERGROUP PLC Personal Goods

SYNERGY HEALTH PLC Health Care Equipment and Services

TALKTALK PLC Fixed Line Telecommunications

TALVIVAARA MINING CO. PLC Industrial Metals and Mining

TATE & LYLE PLC Food Producers

TELECITY GROUP PLC Software and Computer Services

TESCO PLC Food and Drug Retailers

TRAVIS PERKINS PLC Support Services

TUI TRAVEL PLC Travel and Leisure

TULLOW OIL PLC Oil and Gas Producers

UBM PLC Media

ULTRA ELECTRONICS PLC Aerospace and Defense

UNILEVER PLC Food Producers

UNITED UTILITIES GROUP PLC Gas, Water and Multiutilities

VICTREX PLC Chemicals

VODAFONE GROUP PLC Mobile Telecommunications

WEIR GROUP PLC Industrial Engineering

WH SMITH PLC General Retailers

WHITBREAD PLC Travel and Leisure

WOLSELEY PLC Support Services

WPP PLC Media

WS ATKINS PLC Support Services

XSTRATA PLC Mining

YULE CATTO & CO. PLC Chemicals

Note: A sector specific composition summary table is provided overleaf.

Appendix C

___________________________________________________________________________

109

Figure C.1 - FTSE 350 Sector Specific Composition Summary

FTSE 350 SECTOR SPECIFIC COMPOSITION SUMMARY

FTSE 350 - Sector N %

Aerospace and Defense 7 3.4%

Automobiles and Parts 1 0.5%

Beverages 4 2.0%

Chemicals 6 2.9%

Construction and Materials 4 2.0%

Electricity 2 1.0%

Electronic and Electrical Equipment 6 2.9%

Fixed Line Telecommunications 6 2.9%

Food and Drug Retailers 4 2.0%

Food Producers 6 2.9%

Forestry and Paper 1 0.5%

Gas, Water and Multiutilities 5 2.5%

General Industrials 5 2.5%

General Retailers 15 7.4%

Health Care Equipment and Services 2 1.0%

Household Goods and Home Construction 5 2.5%

Industrial Engineering 7 3.4%

Industrial Metals and Mining 3 1.5%

Industrial Transportation 2 1.0%

Media 13 6.4%

Mining 14 6.9%

Mobile Telecommunications 2 1.0%

Oil and Gas Producers 8 3.9%

Oil Equipment and Services 6 2.9%

Personal Goods 3 1.5%

Pharmaceuticals and Biotechnology 6 2.9%

Software and Computer Services 9 4.4%

Support Services 31 15.2%

Technology and Hardware Equipment 5 2.5%

Tobacco 1 0.5%

Travel & Leisure 15 7.4%

Total 204

Appendix D

Multiple Regression

Analysis Data:

Multiple Regression Analysis Results:

Outliers, Secondary Dependent Variable and

Stock Price Performance

Appendix D

___________________________________________________________________________

111

D.1 Introduction

The impact of the six identified outliers upon preliminary testing along with the

results of multiple regression analysis that examine both the secondary dependent

variable and the significance of post financial year end stock price performance in the

study are detailed in this appendix.

D.2. Impact of Outliers upon Preliminary Testing

In analysing the determinants of earnings management (abnormal provision), removal

of the six identified outliers from preliminary regression analysis resulted in a marked

difference in the significance statistics of Multiple Regression One (Table D.1 below).

Table D.1 – Impact of Outliers upon Preliminary Regression Analysis

Identified Outliers R Square Adj. R

Square F Stat

P Value

(F Stat)

Inclusion of Six Outliers 0.841 0.829 73.739 0.000

Exclusion of Six Outliers 0.083 0.015 1.215 0.266

Although the significantly positive R Square and Adj. R Square values of 0.841 and

0.829 indicate apparently strong explanatory power, the removal of Heritage Oil PLC

resulted in a 45% reduction in the Adj. R Square value, indicating the impact of such

outliers on the analysis. All six outliers were therefore excluded from further testing.

D.3 Secondary Dependent Variable

Measure of Abnormal Provision – Proxy for Earnings Management Activity

The secondary dependent variable comprises the relative change in the provision for

credit loss on trade receivables, without controlling for changes in total gross trade

receivables.

{ } :

Where: EQ = Earnings quality.

Prov. (t) = Provision for credit loss on trade receivables in latest financial period.

Prov. (t-1) = Provision for credit loss on trade receivables in previous financial period.


Prov. (t-1)

X 100 EQ

Appendix D

___________________________________________________________________________

112

D.3.1 Alternative Regression One: Fifteen Hypotheses Regression (N=204)

Alternative multiple regression one consists of the full sample of 204 FTSE 350

companies and tests all fifteen hypotheses as indicated in Figure D.1 below, where the

relative change in total gross trade receivables is added as a fifteenth independent

explanatory variable.

Figure D.1 – Alternative Multiple Regression One Equation


The resultant regression significance statistics and fifteen hypotheses results are

contained in Tables D.2 and D.3 below and overleaf.

Table D.2 – Alternative Multiple Regression One Significance Statistics (N=204)



T-Stat -0.751 F Stat 2.859


The results contained in Table D.2 above indicate that the model has explanatory

power at all levels of significance, with a regression significance P-Value of 0.000.

The Adj. R Square value of 0.121 indicates that the model explains 12.1 per cent of

the variation in the change in provision for credit loss on trade receivables. The results

for all 15 hypotheses tested are contained in Table D.3 overleaf.





+ β12 AUDIT MEET + β13 AUDITOR + β14 AUDIT FEES

+ β15 GROSS T/REC + 𝜀

Appendix D

___________________________________________________________________________

113

Table D.3 - Alternative Multiple Regression One: Fifteen Hypotheses (N=204)









8 β7 Change in Net Margin (%) - -0.002 0.446



11 β10 Proportion of INEDs to Audit Committee + 0.759 0.045*

12 β11 # Of Governance Non Compliance Issues - 0.035 0.165

13 β12 # Of Audit Committee Meetings + -0.008 0.585

14 β13 Auditor Type + -0.377 0.008*


1 β15 Change in Gross Trade Receivables + 0.243 0.004


One variable: the Change in Gross Trade Receivables exhibits a significant positive

relationship with the change in provision for credit loss on trade receivables.

While the Auditor Type (0.008*) and Proportion of INEDs to the Audit Committee

(0.045*) variables exhibit apparent significance at the 1% and 5% levels respectively,

these results are deemed to be skewed and are disregarded, given that both variables

are not normally distributed, where only six of the 204 FTSE 350 companies were

audited by a non-Big 4 auditor and where only eight of the total sample reported audit

committee structures that comprised of less than 100% INED composition. The

remaining variables exhibit no significant relationship with the change in provision

for credit loss on trade receivables.

Appendix D

___________________________________________________________________________

114

D.4 Multiple Regression Analysis: Stock Price Performance

Regression One: Extreme Abnormal Underprovision (N=25)

Regression one consists of the full sample of 25 FTSE 350 extreme abnormal

underproviders and comprises the variables in Figure D.2 below.

Figure D.2 – Stock Price Performance Multiple Regression One Equation


Where:

Stock Performance = Stock price performance post financial year end (Section 5.9.2).


β1 = Extreme abnormal underprovision for credit loss on trade receivables.

β2 = Stock beta value (Risk). β3 = Natural Logarithm of Total Assets (Size).

Table D.4 – Stock Price Performance Regression One Sig. Statistics (N=25)


Coefficient 0.164 Adj. R Square 0.107

T-Stat 0.462 F Stat 1.958


The results contained in Table D.4 indicate explanatory power, with an Adj. R Square

value of 0.107 indicating that the model explains 10.7 per cent of the variation in the

stock price performance. However, a regression significance P-Value of 0.151

indicates explanatory insignificance at all levels. The associated hypotheses results

are contained in Table D.5 overleaf.

Stock Performance = β0 + β1 EARNINGS QUALITY+ β2 BETA VALUE

+ β3 NAT. LOG ASSETS (SIZE) + 𝜀

Appendix D

___________________________________________________________________________

115

Table D.5 – Regression One: Stock Price Performance Hypotheses (N=25)

Variable Coefficient P-Value

β1 Ext. Abnormal U/Provision for Credit Loss 0.405 0.032

β2 Stock Beta Value (Risk) 0.035 0.721

β3 Nat. Log of Total Assets (Size) -0.002 0.985


The insignificance of the Stock Beta Value and Natural Logarithm of Total Assets

variables is most surprising (Table D.5 above), as both variables normally display a

statistically significant relationship with resultant stock price performance.

Notably, the extent of extreme abnormal underprovision for credit loss on trade

receivables exhibits a significant positive (5% level) relationship with resultant stock

price performance. Regression one was therefore re-run, with the exclusion of β2 and

β3 given their insignificance.

Figure D.3 – Stock Price Performance Regression Two Equation


Table D.6 – Stock Price Performance Regression Two Sig. Statistics (N=25)





A marked increase in the F Stat (6.249) and regression significance P-Value (0.020)

are noted, while the Adj. R Square value of 0.179 indicates that the model explains

17.9 per cent of the variation in the stock price performance.

Table D.7 – Regression Two: Stock Price Performance Hypothesis (N=25)


β1 Ext. Abnormal U/Provision for Credit Loss 0.416 0.020


Stock Performance = β0 + β1 EARNINGS QUALITY+ 𝜀

Appendix D

___________________________________________________________________________

116

The extent of extreme abnormal underprovision for credit loss on trade receivables

exhibits a significant positive (5% level) relationship with resultant stock price

performance, where a 1% increase in earnings quality (reduction in the extent of

abnormal underprovision) results in a 0.416% more positive stock price performance.

Regression Three: Extreme Abnormal Overprovision (N=25)

Regression three consists of the full sample of 25 FTSE 350 extreme abnormal

overproviders and comprises the variables in Figure D.4 below.

Figure D.4 – Stock Price Performance Multiple Regression Three Equation


Where:

Stock Performance = Stock price performance post financial year end (Section 5.9.2).


β1 = Extreme abnormal overprovision for credit loss on trade receivables.

β2 = Stock beta value (Risk). β3 = Natural Logarithm of Total Assets (Size).

Table D.8 – Stock Price Performance Regression Three Sig. Statistics (N=25)





The Adj. R Square value (0.153) indicates that the model explains 15.3 per cent of the

variation in the stock price performance, with explanatory significance at the 10%

level.

Stock Performance = β0 + β1 EARNINGS QUALITY+ β2 BETA VALUE

+ β3 NAT. LOG ASSETS (SIZE) + 𝜀

Appendix D

___________________________________________________________________________

117

Table D.9 – Regression Three: Stock Price Performance Hypotheses (N=25)


β1 Ext. Abnormal O/Provision for Credit Loss -0.161 0.328

β2 Stock Beta Value (Risk) -0.456 0.016

β3 Nat. Log Total Assets (Size) 0.968 0.020


The significance of the Stock Beta Value (5% level) and Natural Logarithm of Total

Assets (5% level) variables is evident in Table D.9 above, with a significant negative

relationship between the Stock Beta Value variable and resultant stock price

performance and a significant positive relationship in the case of the Natural

Logarithm of Total Assets variable. Notably, the extent of abnormal overprovision for

credit loss on trade receivables exhibits no significant relationship with resultant stock

price performance.

Appendix E

Disclosure Notes:

Disclosure Extracts from Annual Reports:

Trade Receivables

Appendix E

___________________________________________________________________________

119

E.1 Introduction

The purpose of this appendix is to provide extracts of the primary disclosure notes

from the financial statements that were utilised in order to gather data in this study.

Example One: QINETIC PLC – Trade and Other Receivables

Example Two: QINETIC PLC – Credit Risk Disclosure Note

While example one details the annual movement on trade receivables and the

associated provision, example two details information regarding the entity’s

assessment of credit risk. This information is utilised to determine the extent of

abnormal provision for credit loss on trade receivables during the latest period.

Appendix F

Dependent Variable

Dataset:

Company Specific

Dependent Variable Data

Appendix F

___________________________________________________________________________

121

F.1 Introduction

The dataset relating to both the primary and secondary dependent variables utilised in

this study, for the final sample of 204 FTSE 350 companies, is contained in this

appendix.

Primary Dependent Variable

The primary dependent variable comprises the relative change in the provision for

credit loss on trade receivables after controlling for the relative change in total gross

trade receivables, which is calculated as follows:

{ }-{ }: Secondary Dependent Variable

The secondary dependent variable comprises the relative change in the provision for

credit loss on trade receivables, without controlling for changes in total gross trade

receivables.

{ } : COMPANY NAME

Primary Dependent

Variable

Secondary

Dependent Variable

AEGIS GROUP PLC -0.96% -1.03%

AGGREKO PLC -24.23% 9.09%

AMEC PLC -41.18% -9.09%

ANGLO AMERICAN PLC 5.86% 1.89%

ANTOFAGASTA PLC 13.48% 14.71%

ARM HOLDINGS PLC -38.62% -21.14%

ASHTEAD GROUP PLC -24.58% -12.18%

ASSOCIATED BRITISH FOODS PLC -11.91% 0.00%

ASTRAZENECA PLC -24.33% -18.52%

AVEVA GROUP PLC -96.49% -45.04%


Prov. (t-1)

X 100

GTR (t) - GTR (t-1)

GTR (t-1)

X 100 EQ


Prov. (t-1)

X 100 EQ

Appendix F

___________________________________________________________________________

122

COMPANY NAME Primary Dependent

Variable

Secondary

Dependent Variable

AZ ELECTRONIC MATERIALS PLC -42.31% -45.00%

BABCOCK INTERNATIONAL GROUP PLC -14.01% -35.35%

BAE SYSTEMS PLC 3.86% -4.88%

BALFOUR BEATTY PLC -22.05% -4.17%

BARR A.G. PLC 42.25% 58.58%

BARRATT DEVELOPMENTS PLC 27.50% 26.67%

BBA AVIATION PLC -9.30% -6.06%

BERENDSEN PLC -5.69% -12.07%

BERKELEY GROUP HOLDINGS PLC -85.38% 0.00%

BG GROUP PLC -23.75% 7.45%

BHP BILLITON PLC -19.41% 2.72%

BODYCOTE PLC -16.65% -10.14%

BOOKER GROUP PLC -19.30% -8.51%

BOVIS HOMES GROUP PLC -141.60% 0.00%

BP PLC -35.85% -22.43%

BRITVIC PLC -13.31% 0.00%

BSKYB GROUP PLC 37.02% 27.45%

BT GROUP PLC -1.18% -2.60%

BTG PLC -22.83% -22.22%

BUMI PLC -100.00% 0.00%

BUNZL PLC -18.11% -11.54%

BURBERRY GROUP PLC -39.47% -37.19%

BWIN.PARTY DIGITAL ENTER. PLC -56.42% 107.14%

CABLE & WIRELESS COMM PLC 3.37% 8.06%

CABLE & WIRELESS W.W. PLC -22.21% -30.30%

CAPE PLC -87.33% -64.00%

CAPITA GROUP PLC 11.46% 32.60%

CARILLION PLC 31.33% 32.05%

CARPETRIGHT PLC -4.94% 0.00%

CENTRICA PLC -10.11% -6.55%

CHEMRING GROUP PLC -47.29% -30.00%

COBHAM PLC -6.75% -16.67%

COLT GROUP S.A. 18.88% 15.71%

COMPASS GROUP PLC -15.01% -5.06%

COMPUTACENTER PLC -15.14% 0.79%

Appendix F

___________________________________________________________________________

123


Variable

Secondary

Dependent Variable

COOKSON GROUP PLC -9.98% -11.50%

CRANSWICK PLC -4.60% -12.68%

CRH PLC -11.11% 1.32%

CRODA INTERNATIONAL PLC -28.20% -31.34%

CSR PLC -36.91% -22.00%

DAILY MAIL & GEN TRUST PLC -2.19% -5.15%

DAIRY CREST GRP PLC -16.34% -8.62%

DE LA RUE PLC 14.61% 20.51%

DEBENHAMS PLC 87.06% 100.00%

DEVRO PLC 3.32% 9.09%

DIAGEO PLC -0.39% 0.00%

DIGNITY PLC -0.40% -16.22%

DIPLOMA PLC -32.46% -16.67%

DIXONS RETAIL PLC -7.45% -13.57%

DOMINO PRINTING PLC -30.33% -25.98%

DOMINO'S PIZZA GROUP PLC -104.29% 0.00%

DRAX GROUP PLC 9.58% 15.38%

DS SMITH PLC -17.75% -1.13%

EASYJET PLC -9.09% 0.00%

ELECTROCOMPONENTS PLC -14.35% -14.55%

ELEMENTIS PLC 4.29% -7.14%

ENQUEST PLC 3.74% 0.00%

ESSAR ENERGY PLC -20.26% 0.00%

EURAS. NATURAL RES. CORP PLC 135.34% 154.55%

EUROMONEY INST INVESTOR PLC -21.12% -4.22%

EVRAZ PLC 8.07% -11.22%

EXILLON ENERGY PLC -13.91% 3.68%

EXPERIAN PLC -23.67% -21.28%

FENNER PLC -40.39% -11.54%

FERREXPO PLC -74.39% -61.76%

FIDESSA GROUP PLC -6.53% -1.71%

FIRSTGROUP PLC 26.96% 15.38%

FRESNILLO PLC -13.11% -9.81%

G4S PLC -13.02% -8.22%

GALLIFORD TRY PLC 4.32% -33.33%

Appendix F

___________________________________________________________________________

124


Variable

Secondary

Dependent Variable

GENUS PLC -1.12% 6.06%

GKN PLC -6.41% 20.00%

GLAXOSMITHKLINE PLC 13.98% 8.15%

GLENCORE INTERNATIONAL PLC -41.85% -16.77%

GO-AHEAD GROUP PLC -21.26% -27.78%

GREENE KING PLC -6.56% 15.69%

HALFORDS GROUP PLC -1.96% 0.00%

HALMA PLC 25.48% 37.29%

HAYS PLC -19.12% 11.11%

HIKMA PHARMACEUTICALS PLC -47.57% -17.45%

HOME RETAIL GROUP PLC 9.65% 8.18%

HOMESERVE PLC -54.99% -44.97%

HOWDEN JOINERY GROUP PLC -10.04% -5.43%

HUNTING PLC -51.22% 29.41%

IMAGINATION TECH. GROUP PLC 60.87% 150.00%

IMI PLC -4.67% 4.60%

IMPERIAL TOBACCO GROUP PLC 0.71% 0.00%

INCHCAPE PLC -9.89% -1.28%

INFORMA PLC -1.00% 6.79%

INMARSAT PLC 9.65% 0.00%

INTERCONTINENTAL HOTELS GRP. PLC -19.26% -20.69%

INTERSERVE PLC -1.02% 2.11%

INTERTEK GROUP PLC 12.93% 49.51%

INVENSYS PLC -0.66% -8.33%

ITE GROUP PLC 12.67% 32.00%

JD SPORTS FASHION PLC -11.09% 18.33%

JOHN WOOD GROUP PLC -23.30% -9.91%

JOHSON MATTHEY PLC -44.84% -4.00%

KAZAKHMYS PLC 27.07% 5.08%

KCOM GROUP PLC -17.31% -31.14%

KESA ELECTRICALS PLC -20.45% -7.89%

KIER GROUP PLC -20.43% -27.03%

KINGFISHER PLC -9.72% -25.00%

LADBROKES PLC -29.39% -33.33%

LAIRD PLC -21.72% -20.00%

Appendix F

___________________________________________________________________________

125


Variable

Secondary

Dependent Variable

LAMPRELL PLC -118.93% 3.74%

LOGICA PLC 17.61% 18.03%

LONMIN PLC 74.05% 0.00%

MARKS & SPENCER GROUP PLC -23.96% -7.69%

MARSTON'S PLC 55.94% 42.86%

MEGGITT PLC 44.76% 84.62%

MELROSE PLC -36.14% -51.52%

MICHAEL PAGE INTERNATIONAL PLC -5.38% 10.88%

MICRO FOCUS INTERNATIONAL PLC 102.76% 98.47%

MILLENNIUM AND COP. HOTELS PLC -8.26% 9.09%

MITIE GROUP PLC -15.91% -23.19%

MONDI GROUP PLC -1.63% -15.69%

MONEYSUPERMARKET.COM PLC -35.34% -37.65%

MORGAN CRUCIBLE PLC -3.30% 2.17%

MORRISON SUPERMARKETS PLC 27.49% 25.00%

N BROWN GROUP PLC 3.13% 9.31%

NATIONAL EXPRESS GROUP PLC -32.33% -28.67%

NATIONAL GRID PLC 1.02% -9.00%

NEXT PLC -6.18% 4.50%

NORTHGATE PLC 39.57% 30.04%

OCADO GROUP PLC -53.49% 15.38%

OXFORD INSTRUMENTS PLC 19.02% 0.00%

PAYPOINT PLC 44.63% 13.99%

PEARSON PLC 19.38% 22.89%

PENNON GROUP PLC 3.09% 15.02%

PERFORM GROUP PLC 96.70% 191.18%

PERSIMMON PLC 14.85% 0.00%

PETROFAC LIMITED -52.88% -42.60%

PETROPAVLOVSK PLC -14.42% 0.00%

POLYMETAL INTERNATIONAL PLC -90.28% 0.00%

PREMIER FARNELL PLC -0.68% -2.08%

PZ CUSSONS PLC -54.75% -40.00%

QINETIQ PLC -22.28% -34.09%

RANDGOLD RESOURCES LTD -133.73% 0.00%

RECKITT BENCKISER GROUP PLC 10.53% 14.63%

Appendix F

___________________________________________________________________________

126


Variable

Secondary

Dependent Variable

REED ELSEVIER PLC -13.70% -13.70%

REGUS PLC -2.71% 0.00%

RENISHAW PLC -30.99% 0.45%

RENTOKIL INITIAL PLC -6.13% -4.74%

REXAM PLC -50.60% -47.06%

RIGHTMOVE PLC -14.21% 15.63%

RIO TINTO PLC 0.09% -5.41%

ROTORK PLC -26.02% 10.78%

ROYAL DUTCH SHELL PLC -30.05% -1.45%

RPC GROUP PLC -14.96% 32.69%

RPS GROUP PLC 10.07% 25.05%

SABMILLER PLC -3.57% -5.77%

SAGE GROUP PLC -0.07% 6.57%

SALAMANDER ENERGY PLC 85.16% 0.00%

SDL PLC 6.38% 2.42%

SENIOR PLC -36.39% 0.00%

SERCO GROUP PLC 65.84% 71.43%

SEVERN TRENT PLC 7.92% 7.30%

SHANKS GROUP PLC -17.21% -2.17%

SHIRE PLC 10.08% 32.91%

SIG PLC -7.24% -9.86%

SMITH & NEPHEW PLC -27.18% -26.53%

SMITHS GROUP PLC -9.21% -4.11%

SPECTRIS PLC 16.61% 30.77%

SPIRAX-SARCO ENGINEERING PLC -14.23% -11.54%

SPIRENT COMMUNICATIONS PLC 33.23% 50.00%

SPORTS DIRECT INTERNATIONAL PLC 23.86% 30.56%

SSE PLC -34.07% -3.16%

STAGECOACH GROUP PLC -47.06% -57.78%

STOBART GROUP LTD 72.14% 58.76%

SUPERGROUP PLC -132.00% 0.00%

SYNERGY HEALTH PLC -38.69% -31.92%

TALKTALK PLC 15.66% -16.22%

TALVIVAARA MINING CO. PLC -22.30% 0.00%

TATE & LYLE PLC -0.77% -20.83%

Appendix F

___________________________________________________________________________

127


Variable

Secondary

Dependent Variable

TELECITY GROUP PLC -37.89% -33.78%

TESCO PLC -16.36% 13.64%

TRAVIS PERKINS PLC 20.96% 22.75%

TUI TRAVEL PLC 17.26% 5.45%

TULLOW OIL PLC -71.43% 0.00%

UBM PLC -4.88% 1.67%

ULTRA ELECTRONICS PLC 48.42% 81.37%

UNILEVER PLC -14.21% -0.85%

UNITED UTILITIES GROUP PLC -7.38% -73.28%

VICTREX PLC -46.31% -25.00%

VODAFONE GROUP PLC 5.79% 0.80%

WEIR GROUP PLC -53.37% -7.25%

WH SMITH PLC 47.14% 50.00%

WHITBREAD PLC 12.66% 9.09%

WOLSELEY PLC -29.24% -21.67%

WPP PLC 9.13% 9.69%

WS ATKINS PLC -43.23% -28.07%

XSTRATA PLC 22.72% 0.00%

YULE CATTO & CO. PLC 34.05% 60.35%

Appendix G

Methodology

Continued:

Additional Variables, Measures and

OLS Regression Assumptions

Appendix G

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129

G.1 Introduction

The purpose of this appendix is to provide additional detail relating to the

composition of the various independent variables and measures utilised in this study,

where such information is not presented in the Methodology chapter. This appendix

also details the results of various procedures undertaken to ensure compliance with

the underlying assumptions of OLS regression analysis.

G2. Explanatory Detail – Additional Variables and Measures

For all of the following variables and measures:

(t) = Latest financial period (latest available annual report financial period).

(t-1) = Previous financial period.

G.2.1 Analyst Consensus EPS Growth %

{ } G.2.2 Earnings (EPS) Surprise %

Obtained directly from the Thomson One Banker database, this variable comprises the

actual EPS surprise % for the financial year end of the latest available annual report.

G.2.3 Existence of Bonus Plan

This measure comprises a dummy variable, where the existence of a bonus plan, as

ascertained from the annual report, is coded as ‘1’ and non-existence is coded as ‘0’.

G.2.4 Executive Incentive Remuneration

This variable comprises the bonus or incentive specific executive director

remuneration, as disclosed in the latest available annual report, expressed as a

component of total executive director remuneration.

G.2.5 Change in Gross Margin

{ } Explanatory detail overleaf…

Mean EPS Forecast (t) – Actual EPS (t -1)

Actual EPS (t -1)

X 100

Gross Margin (t) – Gross Margin (t -1)

Appendix G

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130

As the gross margin of a company is already stated in percentage terms, the change in

this variable is calculated by subtracting the gross margin of a company in the

previous period from that of the latest available financial period.

G.2.6 Change in Net Margin

{ } The change in the net margin is calculated, consistent with the gross margin measure.

G.2.7 Change in Gearing

{ } The measure of gearing employed in this study is defined as total debt expressed as a

percentage of total assets, commonly defined as leverage (Thomson One Banker,

2012). This measure of gearing was chosen as alternative measures resulted in

instances of missing data when utilising the Thomson One Banker database. As this

measure is already stated in percentage terms, the change is calculated by subtracting

the level of gearing in the previous period from that of the latest available financial

period.

G.2.8 Change in Average Trade Receivables Collection Period

{ } As the average trade receivables collection period is stated in terms of days, the

change in this variable is calculated in a manner consistent with the abovementioned

variables.

G.2.9 Proportion of INEDs to the Total Board of Directors

This variable is stated in percentage terms, where the total number of independent,

non-executive directors, as ascertained from the annual report, is expressed as a

percentage of the total board of directors.

Net Margin (t) – Net Margin (t -1)

Gearing (t) – Gearing (t -1)

T/Rec Days (t) – T/Rec Days (t -1)

Appendix G

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131

G.2.10 Proportion of INEDs to the Audit Committee

This variable is stated in percentage terms, where the total number of independent,

non-executive directors on the audit committee, as ascertained from the annual report,

is expressed as a percentage of the total audit committee.

G.2.11 No. of Governance Non Compliance Issues

This variable comprises the number of reported non-compliance issues with the UK

Combined Code on Corporate Governance, as ascertained from the annual report.

G.2.12 No. of Audit Committee Meetings

This variable comprises the number of audit committee meetings held during the

latest financial period, as ascertained from the annual report.

G.2.13 Auditor Type

This measure comprises a dummy variable, where a Big 4 auditor, as ascertained from

the annual report, is coded as ‘1’ and a non-Big 4 auditor is coded as ‘0’. At the time

of this study, a Big 4 auditor includes PricewaterhouseCoopers, KPMG, Deloitte and

Ernst & Young.

G.2.14 Audit Specific Fee(s)

This variable is stated as a ratio, where the total audit specific fee(s) and audit fee(s)

pursuant to legislation, as ascertained from the annual report, are expressed as a

component of total revenue, acting as an appropriate control for size. While previous

studies control for size using the natural logarithm of audit fees (Frankel et al, 2002),

revenue is utilised in this instance, to avoid excessive use of the natural logarithm of

key variables (see Natural Logarithm of Total Assets below).

G.2.15 Beta Value

This measure is obtained directly from the Thomson One Banker database for the date

of 07 June 2012 - providing a measure of the sensitivity of a stock’s price to the

movement of an index (Thomson One Banker, 2012).

G.2.16 Natural Logarithm of Total Assets

Consistent with prior studies, including Frankel et al (2002), this study utilises the

natural logarithm of a key variable to control for size – in this case, total assets.

Appendix G

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132

G3. Standard Assumptions for the OLS Multiple Regression Model

Where the assumptions underlying multiple regression analysis hold true, a strong

justification for the use of ordinary least squares estimation exists (Newbold, 1988,

p.499). In an ideal scenario, conditions will support all such assumptions; however, it

is unlikely that all assumptions are fully valid in any single regression (Webster,

1995). The primary assumptions underlying such analysis, as outlined by (Newbold,

1988 and Webster, 1995) include:

1. The model is correctly specified, depicting real-world behaviour, generating

results that are not at significant variance with underlying reality.

2. The error observations (𝜀i) are not correlated with one another, i.e. there is no

autocorrelation amongst them.

3. The regression error term (𝜀i) is a random, normally distributed variable with a

mean of zero.

4. All errors have the same variance, i.e. the condition of equal variance in errors

(homoscedasticity) exists.

5. The number of observations (n) exceeds the number of independent variables (k)

by at least two.

6. There is no multicollinearity (significant linear relationship) between the

independent, explanatory variables.

According to Newbold (1988, p.501), where the abovementioned assumptions hold

true, by virtue of the Gauss-Markov theorem, the regression model and least squares

estimators are said to be the best, linear, un-biased estimators.

G.3.1 Assumption One – Model Specification

Where the regression model utilised differs substantially from actual practice, any

conclusions drawn from tests undertaken may be subject to error (Newbold, 1988,

p.566). Specification bias is present where the model, as designed, is inconsistent with

its theoretical foundations, often through the omission or inclusion of certain variables

(Webster, 1995, p.720). Misspecification in previous earnings management models is

well documented, with correlated omitted variables in instances of extreme financial

performance, while attempts at misspecification mitigation procedures have often

reduced test power (Dechow et al, 2011).

Appendix G

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133

The accrual reversal framework of Dechow et al (2011) provides an apparent solution

for mitigating model misspecification, yet there is no generally identified optimal

model for detecting or examining earnings management activity. The multivariate

regression model employed in this study for examining the determinants of earnings

management is consistent with prior research, where no instances of misspecification

have been identified.

G.3.2 Assumption Two: Error Observations Uncorrelated - No Autocorrelation

Proceeding with an OLS regression model where there are autocorrelated errors (the

error observations are not independent of each other) can have severe implications,

where resultant inferences relating to hypotheses and confidence levels are potentially

misleading (Newbold, 1988, p.593). The Durbin-Watson statistic is utilised to detect

the presence of autocorrelation, with values of this measure close to two generally

indicating that autocorrelation is not a problem (Webster, 1995, p.572).

The Durbin-Watson statistic, calculated for this study on Multiple Regression One

(Section 6.4.4) is measured as follows, where, at the 1% significance level:

DL = 1.539 : DU = 1.813

K = 14 : N = 204

The resultant Durbin-Watson statistic of 1.878 is greater than DU (1.813) above,

therefore the null hypotheses of no autocorrelation amongst the error observations is

accepted (Newbold, 1988, p.587), indicating that autocorrelation is not an issue with

the model employed.

G.3.3 Assumption Three: Normally Distributed Error Term – Zero Mean

O’ Mahony (2010) states that a regression error term with a mean other than zero

results in biased coefficient estimates. The regression error term, calculated on

Multiple Regression One (Section 6.4.4), has a mean of zero (4.2E-16) and a standard

deviation of one (0.999), complying with underlying OLS assumptions. The

assumption of normal distribution is also maintained, indicated by the un-skewed

bell-curve and frequency distribution in Figure G.1 overleaf.

Appendix G

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134

Figure G.1 – Frequency Distribution and Bell-Curve (Multiple Regression One)

G.3.4 Assumption Four: Homogeneity of Variance

Where the error terms do not all have equal variance, a model is said to exhibit a

degree of heteroscedasticity (Newbold, 1988, p.576). According to Webster (1995,

p.754), where heteroscedasticity exists, the regression coefficients become less

efficient and more unreliable. As the error terms are estimated by the residuals, visual

inspection of a scatter plot where the residuals are plotted against the expected y

values is utilised to determine if any pattern exists amongst the residuals (Newbold,

1988, p.576). As no such discernible pattern exists in Figure G.2 below, the null

hypothesis of homoscedasticity (homogeneity of variance) is accepted.

Figure G.2 – Scatter Plot of Regression Residuals (Multiple Regression One)

-4

-3

-2

-1

0

1

2

3

4

5

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

Reg

ress

ion

Sta

nd

ard

ised

Resid

ual

Regression Standardised Predicted (Y) Value

Scatter Plot of Regression Residuals

Appendix G

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135

G.3.5 Assumption Five: (N) Exceeds (K) By at Least Two

Webster (1995, p.701) states that in multiple regression analysis, there are (k + 1)

parameters to be estimated and that in order to retain at least one degree of freedom in

the model, N must exceed K by at least two. This requirement is significantly

exceeded in this study, where: N (No. of observations) = 204 and K (No. of

independent Variables) = 14.

G.3.6 Assumption Six: No Multicollinearity between Independent Variables

Multicollinearity arises where there is strong intercorrelation between the independent

variables in the model employed (Newbold, 1988, p.570) and its presence violates one

of the conditions for multiple regression analysis, with resultant inflation of the

standard errors of the coefficients (Webster, 1995, p.717). While there is no

pre-determined cut-off point at which correlation is determined to be excessive in

testing for multicollinearity (Webster, 1995, p.717), generally accepted practice

suggests that a Pearson correlation coefficient of 0.8 between any two independent

variables indicates the presence of multicollinearity. The highest degree of

intercorrelation between the independent variables in this study remains well below

this threshold, measured at 0.2526 between the Change in Gross Margin and the

Change in Net Margin, as indicated in Table G.2 overleaf. Multicollinearity may also

be detected through variance inflation factor analysis, which measures the degree of

multicollinearity contributed by each independent variable, where a VIF result of at

least 10 indicates the presence of multicollinearity (Webster, 1995, p.719). As

indicated in Table G.1 below, the VIF values of all continuous independent variables

are well below this threshold, demonstrating clearly that multicollinearity is not

present in the model employed.

Table G.1 – Variance Inflation Factor Analysis Results

Variable VIF Variable VIF

EPS Growth (%) 1.14 Change in Trade Rec. Days 1.06

Earnings (EPS) Surprise (%) 1.20 INEDs to Total Board (%) 1.18

Exec. Incentive Remuneration 1.05 INEDS to Audit Committee (%) 1.36

Change in Gross Margin (%) 1.21 # Gov. Non-Compliance Issues 1.48

Change in Net Margin (%) 1.24 # Of Audit Committee Meetings 1.06

Change in Gearing (%) 1.08 Audit Specific Fee(s) 1.15

Appendix G

______________________________________________________________________________________________________________________________

136

Table G.2 – Test for Multicollinearity: Continuous Independent Variables - (MAX CORRELATION = 0.2526)

CORRELATION Earnings

Quality

EPS

Growth %

EPS

Surprise

%

Exec.

Incentive

Rem.

Change in

Gross

Margin

Change in

Net

Margin

Change in

Gearing

Change in

T/R Days

INEDs to

Total

Board

INEDs to

Audit

Committee

# Gov. Non

Compliance

Issues

# Audit

Committee

Meetings

Audit

Specific

Fee(s)

Earnings Quality 1.000 -0.094 0.101 -0.048 -0.063 0.090 -0.161 -0.030 0.147 0.045 -0.023 0.031 -0.042

EPS Growth % -0.094 1.000 -0.125 0.016 0.035 0.200 -0.014 -0.188 -0.068 0.035 -0.070 -0.027 0.033

EPS Surprise % 0.101 -0.125 1.000 0.096 -0.231 -0.111 -0.083 -0.019 -0.128 0.013 -0.077 0.038 -0.186

Exec. Incentive Rem. -0.048 0.016 0.096 1.000 -0.058 0.051 0.017 -0.001 0.056 -0.059 0.019 -0.024 0.070

Change in Gross Margin -0.063 0.035 -0.231 -0.058 1.000 0.253 0.051 0.095 0.077 0.015 -0.064 -0.071 0.240

Change in Net Margin 0.090 0.200 -0.111 0.051 0.253 1.000 -0.137 0.029 0.068 0.015 -0.163 -0.034 -0.018

Change in Gearing -0.161 -0.014 -0.083 0.017 0.051 -0.137 1.000 0.024 -0.076 -0.063 -0.047 0.021 0.141

Change in T/R Days -0.030 -0.188 -0.019 -0.001 0.095 0.029 0.024 1.000 -0.021 0.036 -0.022 0.002 0.069

INEDs to Total Board 0.147 -0.068 -0.128 0.056 0.077 0.068 -0.076 -0.021 1.000 0.146 -0.229 0.170 -0.022

INEDs to Audit Committee 0.045 0.035 0.013 -0.059 0.015 0.015 -0.063 0.036 0.146 1.000 -0.487 0.056 -0.104

# Gov. Non Compliance

Issues -0.023 -0.070 -0.077 0.019 -0.064 -0.163 -0.047 -0.022 -0.229 -0.487 1.000 0.012 0.076

# Audit Committee

Meetings 0.031 -0.027 0.038 -0.024 -0.071 -0.034 0.021 0.002 0.170 0.056 0.012 1.000 -0.025

Audit Specific Fee(s) -0.042 0.033 -0.186 0.070 0.240 -0.018 0.141 0.069 -0.022 -0.104 0.076 -0.025 1.000

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