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ACCRUAL EARNINGS MANAGEMENT, REAL EARNINGS MANAGEMENT, AND INFORMATION UNCERTAINTY By Thi Thu Ha Nguyen Kingston University Kingston Business School Thesis submitted for the degree of Doctor of Philosophy ABSTRACT The aim of this thesis is to contribute to the research on earnings management, by first investigating models of real earnings management, then extending the literature by examining both accrual and real earnings management within the context of information uncertainty. The thesis comprises of three main studies which analyse secondary data of firms with available data that are listed on the London Stock Exchange during the period from 1992 to 2018. In the first empirical chapter, the relative performance of models to detect accrual earnings management and real earnings management is evaluated by comparing the power of widely used models. The power of test statistics of earnings management detection models is evaluated through examining the frequency with which detection models of accrual earnings management and real earnings management generate type II errors.

I adopt a similar approach to that used by Dechow et al. (1995) and Brown and Warner (1985) in which I randomly select a sample of firm-year observations and artificially add accrual manipulation and real earnings management with the magnitude ranging from 0 percent to 10 percent of lagged assets. I compare the bias in the estimates of accrual earnings management generated by Dechow et al. (1995), Kothari et al.

The results show that the detection models for real earnings management generates larger biased estimates of real earnings management activities compared to models to detect accrual-based earnings management. Among the three types of real earnings management activities, the power of the model for detecting real-based sales manipulation is lowest due to the biased estimates. Moreover, the power of the model for uncovering abnormal research and development (R&D) expenditure is improved when lagged R&D expenditures is added to the existing model. The second empirical chapter investigates the role of information uncertainty in explaining the opportunistic behaviour of managerial discretion when firms have high incentives to manage earnings (i., meeting/beating earnings benchmarks).

To address endogeneity, in which there are potential differences in characteristics of suspect firms (i., those beating earnings expectations) and non-suspect firms (i., those missing earnings expectations), I apply propensity score matching (PSM) developed by Rosenbaum and Rubin (1983) (Shipman et al. More specifically, suspects are matched with non-suspects (by one- i to-one matching without replacement) that have the closest propensity-matching score. These scores are based on a range of different firm characteristics. In addition, this study also uses Heckman (1979) selection model that depends on a particular functional form to give an indirect estimate of suspect firms’ treatment effects.

This empirical evidence contributes to the existing literature by determining the condition in which accrual-based earnings management occurs. Under the condition of high information uncertainty, managerial opportunistic behaviour is unobservable and difficult to detect by market participants; hence, the result shows that when facing high information uncertainty, managers of firms beating earnings expectation are more likely to use discretionary accruals. Moreover, managers of suspect firms also engage in earnings smoothing under the condition of high information uncertainty. In addition, this study contributes to the literature by exploring the role of information uncertainty in managers’ decisions to use accrual earnings management compared to real earnings management.

The last empirical chapter examines the effect of information uncertainty on the long-run performance of firms meeting/beating earnings expectations. There is mixed evidence about whether market participants are irrationally over-optimistic about the information contained within earnings announcements. The evidence provided in this chapter contributes to our knowledge on the interaction effect of information uncertainty on the mispricing of investors. Indeed, empirical results show that firms meeting/beating earnings benchmarks underperform in the long-run period under high information uncertainty compared to low information uncertainty, after controlling for variables such as firm size, market-to-book ratio, capital expenditures, and sales growth in the fiscal year that earnings are announced.

The results are robust after using alternative measures of stock performance. The evidence overall suggests that the condition of information uncertainty is necessary for explaining irrational behaviour of investors. These findings indicate that future underperformance may follow managed earnings under high information uncertainty. ii ACKNOWLEDGEMENTS To make my PhD thesis possible, my Mum and my Dad are always the sources of inspiration for me to overcome obstacles on the road to my achievements I have made.

Their endless love, encouragement and understanding give me huge motivation for completing my PhD thesis. They are always in my heart, and I would like to express my gratitude for never leaving me alone whatever I get in my life, success, or failure. I also thank my husband for his patience in this long journey to complete my PhD thesis. I would like to wholeheartedly thank my supervisors, Professor Salma Ibrahim, and Dr George Giannopoulos, without them I cannot go this far.

They not only have shared me with their expertise, knowledge, but also have helped me overcome difficult time during my PhD journey. Absolutely, the experience that I have had when working with my supervisors is absolutely one of the best, I get out of my PhD study. I would also like to thank Kingston University, Kingston Business School in general for giving excellent research environment, facilities, and necessary support during my study here. Especially, I am so thankful to Kingston Business School to provide me the full-funded studentship.

Without this generous funding, I would not be able to achieve my dream about pursuing PhD program. I would like to thank the research panel committees and administrative staffs at Kingston University, Kingston Business School who conducted all paperwork and procedures related to my thesis. I am also thankful to many other people who in one way or the other contribute to my PhD journey. The feedback and comments I received from faculty, discussants and participants at conferences are valuable for me.

Finally, I also thank my friends for their interests in my work or simply be there for me. iii Table of contents ABSTRACT. III TABLE OF CONTENTS. IV LIST OF TABLES .VIII LIST OF FIGURES.

X LIST OF ABBREVIATIONS. XI 1 CHAPTER 1: THESIS INTRODUCTION .1 Background of the thesis.2 Motivation of the thesis .3 Objectives of the thesis .4 Methodology and data .5 Main empirical findings .6 Structure of the thesis. 7 2 CHAPTER 2: DEFINITION, CLASSIFICATION, THEOREITCAL PERSEPCTIVE AND INCENTIVES OF EARNINGS MANAGEMENT .2 Definition of earnings management .3 Classification of earnings management .1 Accrual earnings management .2 Real earnings management .4 Theoretical perspective of earnings management .3 Agency theory and earnings management .5 Incentives of earnings management .5 Import relief and political costs. DETECTING ACCRUAL EARNINGS MANAGEMENT AND REAL EARNINGS MANAGEMENT .2 Literature review: Earnings management detection models .1 Existing literature on accrual earnings management .2 Existing literature on real earnings management .3 Practical ways to detect accrual earnings management and real earnings management .1 Testing the hypothesis.1 Problem 1: Unintentionally removing some or all the earnings manipulation from DAP and REM .2 Problem 2: Inclusion of correlated variables in DAP and REM .3 Problem 3: Inclusion of uncorrelated variables in DAP and REM .2 Measuring earnings management .1 Measuring discretionary accruals (DAP) .2 Measuring real earnings management (REM) .4 Types of manipulation .5 Practical detection of accrual earnings management and real earnings management .2 Overvalued inventory and overproduction.3 Aggressive reduction in discretionary expense .2 Testing for bias in estimates of discretionary accruals and real earnings management .1 Sample 1: of firms with artificially induced earnings management with no reversal .2 Sample 2: of firm-years with artificially induced earnings management with reversal .3 Power of tests for detecting artificially induced earnings management .1 Sample 1: firms with artificially induced earnings management .2 Sample 2: firm-years with artificially induced earnings management.4 Financial ratio analysis .1 Detecting sales manipulation .2 Detecting overvalued assets and overproduction .3 Detecting aggressive reduction in discretionary expenditures .5 New model to detect abnormal research and development expenses (R&D) .1 Model to detect abnormal R&D expenditures .2 Bias in estimate of REMR&D .3 Power to detect abnormal R&D expenditures .6 Summary and conclusion.

ACCRUAL EARNINGS MANAGEMENT, REAL EARNINGS MANAGEMENT, AND INFORMATION UNCERTAINTY .2 Literature and hypothesis development .1 Earnings management and information uncertainty .2 The choice of earnings management strategies and information uncertainty .3 Income smoothing and information uncertainty .1 Propensity score matching (PSM).2 The inverse mills ratio (IMR) method .4 Association of accrual-based earnings management and information uncertainty of suspects .5 Association of real earnings management and information uncertainty of suspects .6 Accrual earnings management versus real earnings management and information uncertainty .7 Income smoothing and information uncertainty .1 The relation between accrual-based earnings management and information uncertainty of firms beating/meeting earnings benchmarks .2 The relation between real earnings management and information uncertainty of firms beating/meeting earnings benchmarks .3 Real earnings management versus discretionary accruals and information uncertainty.4 Income smoothing and information uncertainty .6 Summary and conclusion. FUTURE PERFORMANCE FOLLOWING BENCHMARK BEATING UNDER INFORMATION UNCERTAINTY .1 The efficient market hypothesis .2 The market anomalies and the emergence of behavioural finance .3 Earnings-based benchmarks.1 Subsequent operating performance following firms meeting/beating earnings benchmarks under high information uncertainty .2 Subsequent stock performance following firms meeting/beating earnings benchmarks under high information uncertainty .2 Suspect firms just beating/meeting important earnings benchmarks .3 Empirical model for hypothesis testing for long-run accounting performance of firms meeting or beating earnings benchmarks and information uncertainty .4 Empirical model for hypothesis testing about subsequent stock performance of firms meeting or beating earnings benchmarks and information uncertainty .1 Descriptive statistics and correlations .1 Evidence of earnings management to avoid earnings decreases and losses 180 5.2 Regression analyses of suspects’ long-run accounting performance and information uncertainty .3 Regression analyses of suspects’ long-run stock performance and information uncertainty .4 Additional analysis: Accrual earnings management and subsequent accounting performance and information uncertainty .7 Summary and conclusion .1 Summary of key findings .2 Practical and theoretical implications of the findings .3 Limitations of the thesis and some suggestions for future research. 206 vii List of tables Table 2.1 Alternative terms and definition of earnings management.3 Bias in estimates of earnings management using sample 1.4 Bias in estimates of earnings management using sample 2.5 Power for test of accrual and real earnings management conducted for artificially induced amount of earnings management from 0% to 10% of lagged assets. The simulation uses a random sample of 500 firms (sample 1) .6 Power for test of accrual and real earnings management conducted for artificially induced amount of earnings management from 0% to 10% of lagged assets.

Simulation uses random sample of 500 firms-years (sample 2) .7 Account receivable days (A/R days) using sample 1 .8 Account receivable days (A/R days) using sample 2 .9 Days’ sales in receivables index (DSRI) using sample 1 .10 Days’ sales in receivables index (DSRI) using sample 2 .11 Sales growth index (SGI) using sample 1 .12 Sales growth index (SGI) using sample 2 .13 Inventory days using sample 1 .14 Inventory days using sample 2 .15 Total accrual to total assets (TATA) using sample 1 .16 Total accrual to total assets (TATA) using sample 2 .17 Sales, general, and administrative expenses index (SGAI) using sample 1 .18 Sales, general, and administrative expenses index (SGAI) using sample 2 .19 Estimation of normal R&D expenditure.20 Biases in estimates of real earnings management using Sample 1 .21 Biases in estimates of real earnings management using sample 2 .22 Power for tests of REMR&D using sample 1.23 Power for tests of REMR&D using sample 2.24 Summary of main findings of chapter 3 .2 Descriptive statistics full sample and propensity-score matched samples .3 The association between discretionary accrual and information uncertainty of firms beating/meeting earnings benchmarks .4 The association between real earnings management and information uncertainty of firms beating/meeting earnings benchmarks .5 Average absolute value of DAP and AREAL sorted by information uncertainty level .6 The probability of using accrual earnings management than real earnings management with the level of information uncertainty .