Individual Characteristics and Earnings Quality Executive reputation and board director gender

Transcription

1 Stockholm School of Economics Department of Accounting Master's Thesis in Accounting and Financial Management Autumn 2013 Individual Characteristics and Earnings Quality Executive reputation and board director gender Abstract: We investigate the relation between executive reputation and firms' earnings quality as well as the relation between gender diversity in boards and earnings quality with the objective of increasing the current understanding of how individuals affect earnings quality. We study Swedish listed firms over the period 1999 to We present and employ a completely new proxy for executive reputation that is based on the bankruptcy involvement of executives. Boardroom gender diversity is measured as the proportion of female directors on the board. To measure earnings quality we use the extended Dechow and Dichev model (McNichols, 2002), and performance-matched abnormal accruals (Kothari et al., 2005). We document a negative relationship between executive reputation and earnings quality, meaning that more reputed executives are associated with poorer earnings quality. Performing several tests we however draw the conclusion that the negative relationship is not due to individual effects, but rather a result of matching. Furthermore, we find no association between boardroom gender diversity and earnings quality, and in line with previous research we find firm-specific factors to be most important in explaining earnings quality. Keywords: Individual characteristics, Earnings quality, Executive reputation, Boardroom gender diversity Authors: Oscar Norrman a and Daniel Vaigur b Tutor: Henrik Nilsson Date: 9 December 2013 a 21652@student.hhs.se b 21563@student.hhs.se

2 Acknowledgements We would like to express our gratefulness to our tutor Henrik Nilsson, Acting Professor at the Department of Accounting at the Stockholm School of Economics, for invaluable advice, inspiration and guidance throughout this thesis. Thank you! Stockholm, December 2013 Oscar Norrman Daniel Vaigur

3 Table of Contents 1 Introduction Scope Outline Theory and previous research Earnings quality What determines earnings quality? How is earnings quality measured? Individual characteristics and earnings quality Executive reputation and ability Other characteristics Hypotheses development I Board composition and earnings quality Boardroom gender diversity Other board characteristics Hypothesis development II Method Operationalization of key measures Executive reputation Boardroom gender diversity Earnings quality Linking earnings quality to individual characteristics Scientific method Reliability Validity Data Data sources Data on individuals... 31

4 4.3 Financial data Results Analysis Robustness and additional tests An alternative reputation measure CEO and CFO changes Summary of additional tests Comparison with previous research Executive reputation and ability Boardroom gender diversity Conclusions References Appendix... 62

5 LIST OF TABLES AND FIGURES TABLE 1: TABLE 2: TABLE 3: TABLE 4: TABLE 5: TABLE 6: TABLE 7: TABLE 8: TABLE 9: TABLE 10: TABLE 11: Summary of earnings quality metrics Summary statistics on individual and board characteristics Summary statistics on earnings quality and firm-specific variables for the estimation sample Simultaneous estimation of CEO reputation and earnings quality Simultaneous estimation of CFO reputation and earnings quality Summary statistics on press coverage of CEOs Simultaneous estimation of CEO press coverage and earnings quality Executive changes and earnings quality Changes in earnings quality and changes in executive reputation Summary statistics on individual and board characteristics for observations included in simultaneous equation estimations Summary statistics on firm-specific characteristics for observations included in simultaneous equation estimations FIGURE 1: FIGURE 2: Distribution of firm-years in the original estimation sample across 1-digit SIC codes Distribution of firm-years included in estimating the system of simultaneous equations for the CEO and CFO sample across 1-digit SIC codes, by earnings quality measure

6 1 Introduction Typically, Sweden is not viewed as a country where corporate scandals including fraud or creative accounting are common. This view is however not completely accurate since Sweden actually has a long, quite voluminous, history of corporate scandals. One of the earliest scandals, and perhaps the most influential, is the Kreuger crash in The Kreuger empire was the largest conglomerate in Sweden before the Great Depression. The depression however led to severe financial problems and the empire, with an estimated value of 30 billion SEK, collapsed in As a direct response to the collapse, the US Congress enacted the Securites Act of 1933 and Sweden began regulating the external accounting information of listed companies (Jones, 2011). Also part of the Swedish history of corporate scandals is Fermenta, a small biotech company that became listed on the Stockholm Stock Exchange in By 1986, Fermenta s market value had grown to around 10 billion SEK and its CEO, Refaat El-Sayed, had earned several awards including Swedish Manager of the Year in 1984 and Swede of the Year in The successful journey however took an abrupt end when the firm s internal auditor blew the whistle and it was revealed that Fermenta systematically had manipulated its accounting in order to increase its earnings. The discrepancy between reported earnings and the true and fair number was as large as 1.2 billion SEK in In 1989, El-Sayed was sentenced to five years in prison for his fraudulent behaviour (Jones, 2011). Around a decade later, it was time again and in 1998 it was revealed that Prosolvia, a fastgrowing software company, had intentionally inflated its profits. Prosolvia s share had skyrocketed since it was listed in 1997 but when the accounting irregularities were revealed in 1998, its share price fell fast and only seven months after the revelation the firm was in bankruptcy and the shareholders of Prosolvia, including thousands of small investors, lost their money (Jones, 2011). There are also more recent examples of Swedish accounting scandals, such as Kraft & Kultur and Stora Enso. Kraft & Kultur was one of Sweden s largest electricity companies and in 2011 it was accused by their owners for swindling around 2 billion SEK through accounting manipulation (Forsberg, 2013). Stora Enso is one of the largest forestry companies in the Nordic region and earlier this year its management was accused for having ordered and approved fabricated accounting entries, made forbidden asset transactions and dividends in order to manipulate the stock market s perception of previously made acquisitions (Ollevik, 2013). Enron, Worldcom and Saytam are some well-known examples that illustrate that accounting scandals also are a world-wide phenomenon. In the backwash of all these scandals that have taken place around the world, academia s interest in accounting quality matters in general, and earnings quality in particular, have soared (Gavious et al., 2012). The interest for quality matters has been further stimulated by the work of the IFRS Foundation (DeFond, 2010), whose principal objective is: 1

7 To develop a single set of high quality, understandable, enforceable and globally accepted financial reporting standards based upon clearly articulated principles [emphasis added] (IFRS Foundation, 2013; p. 1) Earnings quality is, however, not only important from the perspective of stakeholders and standard-setters, but also for the firm itself. More specifically, it has been shown that more accurate financial information, i.e. better earnings quality, is associated with lower cost of capital (Easley and O'Hara, 2004). Hence, it should be in the interest of firms themselves to ensure good earnings quality. The majority of earnings quality research focuses on understanding how firm characteristics (e.g. size, market-to-book, and leverage) affect firms reporting and disclosure decisions. Two common results from this research are that there is large variance in accounting choices and disclosure practices across firms, and that this variance is not explained by firm characteristics nor corporate governance practices (Francis et al., 2008). Consequently, accounting researchers have recently started paying more attention to individuals within the firm and how these relate to differences in accounting practices and earnings quality. As Habib and Hossain (2013) notes, the few studies that exist in this stream of research have shown that individual characteristics are an important determinant of accounting outcomes in general, and earnings quality in particular. However, results and conclusions are mixed across studies and some results stands in contrast to each other. Further, Dichev et al. (2013) call for more research that considers the human element when trying to explain differences in earnings quality. Therefore, the objective of our study is to add to the existing, still limited, understanding of individuals effects on earnings quality. 1.1 Scope Individuals can influence earnings quality through two channels; real decisions and accounting choices (LaFond, 2008). Real decisions refer to decisions regarding investments, financing and operations while accounting choices refer to decisions regarding e.g. what measurement methods to use, when to recognize economic transactions and how to estimate accruals. To affect earnings quality individuals hence need to have decision-making power over these decisions. As such, focusing on those individuals with the most decision-making power over real decisions and accounting choices seems reasonable, and intuitively those are the CEO and CFO. Therefore, the individuals of main interest in our study are CEOs and CFOs, and more specifically, we focus on how differences in the characteristics of CEOs and CFOs are related to differences in earnings quality. Following from the focus on firms management, we think that it is important to also consider the board of directors because of its governance function and its role as the primary function for monitoring the firm s management. Regarding what individual characteristics to study it is important to understand that the multidimensionality of individuals characteristics and the complexity involved in measuring these make it practically impossible to study all at once. Therefore, we delimit ourselves to the reputation of CEOs and CFOs, and the gender of board directors. The focus on executives reputation is motivated by a 2

8 couple of reasons. First, accounting researchers call for more earnings quality work that considers the human element in general (Dichev et al., 2013), and executive reputation in particular (LaFond, 2008). Second, executives are the utmost responsible for firms real decisions and accounting choices, meaning they have the most decision-making power over decisions that affect earnings quality. Third, firms reporting decisions are significantly influenced by executives career concerns in terms of fear to adversely affect ones external reputation by not meeting market expectations such as earnings benchmarks (Graham et al., 2005). Last, Malmendier and Tate (2009) show that superstar CEOs, after being awarded for their performance, engage in earnings management to give the impression of strong performance and thus retain their superstar status. Seemingly, reputational concerns seems to influence earnings quality. Our interest for board director gender stems from the intensive debate on gender quotas on boards that is taking place in Europe. While some countries (e.g. Norway, France and Italy) have already legislated about gender quotas on company boards, others (including Sweden) are heavily discussing whether or not to do so. In this context we are surprised over how little attention accounting researchers have paid to the association between earnings quality and boardroom gender diversity and we think that further examination of this issue is needed. Further, we delimit ourselves to Swedish firms since we do not have access to all the data we require for other countries than Sweden. Given the objective of our study and our delimitations we try to answer the following questions: What role does CEO/CFO reputation play for earnings quality? What role does gender diversity in the boardroom play for earnings quality? 1.2 Outline Our study proceeds as follows. In section 2 we review previous research and theories that are used to develop hypotheses related to our two research questions. Section 3 is a discussion of our methodology in terms of how we operationalize key measures for our study and how these are linked to each other. In section 4 we describe our sample. Section 5 presents our results, which are analysed in section 6. Finally, section 7 concludes our work. 3

9 2 Theory and previous research This section reviews previous research that is related to our study. We begin by introducing the concept of earnings quality to create an understanding of how earnings quality can be thought of. This is followed by descriptions and discussions of different ways to measure earnings quality. We then turn to the association between individual characteristics and earnings quality. This section begins with a review of studies that have examined the association between individual characteristics and earnings quality. We continue by introducing several theoretical perspectives on the relation between executive reputation and earnings quality which, in the light of results from previous research, are used to develop hypotheses concerning our first research question. Next, we shift focus to our second research question and we begin with reviewing previous research on the association between the board of directors and earnings quality. Finally, we introduce theories on gender differences in decision-making situations and these are used to hypothesize on the relation between gender diversity on boards and earnings quality. 2.1 Earnings quality The term earnings quality is widely used within accounting research and has been so for quite some time. Since it was first introduced by Graham and Dodd in 1934 (Dechow et al., 2010) it has evolved into numerous different shapes and today there is no consensus on what earnings quality means or how to measure it (Schipper and Vincent, 2003; Dichev et al, 2013). The following examples illustrate this: - [ ] earnings [ ] is of good quality if it is a good indicator of future earnings. (Penman and Zhang, 2002; p. 237) - We define earnings quality as the extent to which reported earnings faithfully represent Hicksian income 1 [ ]. (Schipper and Vincent, 2003; p. 98) - We define earnings to be of high quality when the earnings number accurately annuitizes the intrinsic value of the firm. (Dechow and Schrand, 2004; p. 5) In their review of more than 300 earnings quality studies, Dechow et al. (2010) discusses this dispersion and argues that it is needed and nothing to be surprised about since the term earnings quality in itself is meaningless if it is not put in the context of a specific decision model. The idea here is that different stakeholders might have differing views on what constitutes a good quality earnings number. This contextual dependency has been emphasized recently by accounting researchers but in the world of practitioners the idea is not supported (Dichev et al., 2013). Rather, it appears as the standpoint of practitioners is that good quality earnings, independent of contextual setting, are those that are reliable, sustainable, and have a cash-predictability aspect (Dichev et al., 2013; p. 12). Nonetheless, it seems 1 Hicksian income refers to the concept of income being a measure of the change in wealth between periods (Dechow and Schrand, 2004). 4

10 as earnings quality, independent of whose perspective we take, can be thought of as the degree to which reported earnings represent true economic performance What determines earnings quality? Earnings quality research has until recently focused mainly on firm characteristics as determinants of earnings quality (Demerjian et al., 2013) and the effects of numerous firm characteristics on earnings quality have been examined. Dechow and Dichev (2002) is an influential paper on this subject and they infer that earnings quality is systematically linked to observable firm characteristics. Testing for a number of firm characteristics, they document that smaller firms and firms with more volatile revenue, more volatile cash flows, longer operating cycle, and higher incidence of negative earnings realizations are associated with lower quality, results that are supported by findings in several later studies (e.g. Francis et al., 2005; Francis et al., 2008; Dikolli et al., 2012; Demerjian et al., 2013). These firm characteristics that are systematically linked to earnings quality are typically referred to as innate drivers of earnings quality (e.g. Francis et al., 2005; Francis et al., 2008; Demerjian et al., 2013). Innate refers to the factors being driven by the firm s business model and operating environment and as such, they are not easily influenced by management in the short run and their effect on earnings quality should therefore not be assigned to individuals (Francis et al., 2005). This part of earnings quality is called innate earnings quality. Firm-level focused earnings quality research has however found that there is more to earnings quality than these innate drivers. Specifically, there is large variance in earnings quality affecting accounting choices and disclosure practices across firms but this variance is not explained by innate firm characteristics, other firm characteristics (e.g. financial leverage) or corporate governance practices (e.g. choice of auditor) (Francis et al., 2008). Accordingly, researchers have begun paying more attention to individuals rather than firms themselves. The subject is however rather unexplored yet, something that is illustrated by researchers requests of more work in which the human element is considered (e.g. Dichev et al., 2013). Some work has already been done though, and Habib and Hossain (2013) provide a review of this and they conclude that individual characteristics play an important role for accounting outcomes in general, and earnings quality in particular. Individuals and their discretionary accounting choices are hence believed to be the reason for why firm-specific factors do not fully explain differences in earnings quality. As such, the part of earnings quality that remains after controlling for innate features of the firm is called discretionary earnings quality. The notion that individuals do matter can be traced back to early work by Hambrick and Mason (1984). According to the neoclassical view of the firm, managers are perfect substitutes and would therefore make the same decisions if the circumstances and incentives were the same. Hambrick and Mason (1984), however, argues for the opposite in their Upper Echelons theory i.e. that individual characteristics do matter, managers are not homogenous. The theory builds on the idea of bounded rationality and the "givens" (March and Simon, 1958) that reflect the cognitive base and values an 5

11 individual brings with him/her to a firm. The "givens" constitute assumptions that individuals use as basis for choice, and individuals' behaviour is limited by those. Bounded rationality is the idea that humans make rational decisions on the basis of the limited amount of time we have at hand, and the information we have perceived, internalised, and sorted out as relevant for the current situation. We humans have a limited ability to comprehend every piece of information we encounter and therefore a kind of sub-conscious multi-staged filter assists us in our daily decision making. Building on the reasoning of Hambrick and Mason (1984), Bertrand and Schoar (2003) suggest that managers' individual practices are related to differences in performance. They argue that if managers are homogenous then two firms in the same industry with identical prerequisites will make the same choices and managers would not matter for corporate decisions. Their findings do, however, indicate that different managerial characteristics are systematically related to differences in choices and performance. This is some of the earliest empirical evidence for that individuals do matter for firm choices and decisions, and as such, Bertrand and Schoar s (2003) findings provide the basis for much of today s individual-focused accounting research How is earnings quality measured? With academia s view that earnings quality is contextually dependent and the dispersion in definitions used by researchers in mind, it is not surprising that researchers have developed an almost uncountable number of methods to measure earnings quality. Dechow et al. (2010) argues, based on the idea that earnings quality is contextually dependent, that there is no earnings quality measure that is superior to all the others. To illustrate the width and depth of earnings quality measures we go through and discuss the three major categories of earnings quality measures as defined by Dechow et al. (2010) in their extensive review properties of earnings, investor responsiveness to earnings, and external indicators of earnings misstatements Properties of earnings The category properties of earnings includes earnings quality measures based on earnings persistence, accruals, earnings smoothness, asymmetric timeliness and timely loss recognition, and target beating Earnings persistence Persistence based earnings quality research relies on a theoretical presumption that if one firm (A) has a more persistent earnings stream than another firm (B), then A s current earnings number contains more information about future performance than B s and as such, A s current earnings number is more useful for equity valuation purposes, meaning it is considered as being of higher quality than B s earnings number (Dechow et al., 2010). Within this stream of research the typical starting point is a model specification, developed by Freeman et al. (1982), that expresses future earnings as a function of current earnings: 6

12 EEEEEEEEEEEEEEEE tt+1 = αα 0 + αα 1 EEEEEEEEEEEEEEEE tt + νν tt The variable of interest is αα 1 which indicates to which degree the earnings number is persistent the higher the αα 1, the more persistent the earnings number. A common way of extending this rather simplistic model is to decompose the earnings number into components. For example, Sloan (1996) models future earnings as a function of an accrual component and a cash flow component: EEEEEEEEEEEEEEEE tt+1 = γγ 0 + γγ 1 AAAAAAAAAAAAAAAA tt + γγ 2 CCCCCCh ffffffff tt + νν tt By using regression analysis Sloan finds the coefficient for the accrual component ( γγ 1 ) to be significantly smaller than the coefficient for the cash flow component (γγ 2 ). This means that, on average, the portion of current earnings that is due to the accrual component is less likely to persist into the future than the portion of current earnings that is due to the cash flow component. Therefore, an earnings number consisting of mainly cash items is considered to be of higher quality than an earnings number composed of mainly accruals. Dechow et al. (2010) argues that the main strength of using earnings persistence as a quality metric stems from the fact that it fits well with the view of earnings being a good summary metric of expected cash flows, and hence useful for equity valuation. However, the persistence based approach suffers from the fact that persistence depends on the true performance of the firm as well as how things are measured through the accounting system. This means that persistence could be achieved by tricking the system through e.g. manipulating your earnings number (Dechow et al., 2010) Accrual models Of all the different proxies there are for earnings quality, accruals models are the most extensively used (DeFond, 2010). The basis for these models is a separation of total accruals into normal/innate and abnormal/discretionary accruals. Normal accruals are supposed to capture adjustments that represent true performance of the firm whereas abnormal accruals reflect distortions resulting from imperfect measurements within the accounting system that are due to accounting rules and/or earnings management 2 (Dechow et al., 2010). The first example of an accrual model was developed by Healy (1985) but the accrual model era did not take off for real until 1991 when the mother of all accruals models the Jones model was presented by Jennifer Jones. Today, a large portion of the models present in the literature has its roots in the Jones model (DeFond, 2010). Jones (1991) measures abnormal accruals as the difference between actual total accruals 3 (TA) and an estimated normal level of total accruals (the expression in brackets) that is supposed to represent firm fundamentals: AAAA ii,tt = TTTT ii,tt αα 1,ii (1/AA ii,tt 1 ) + αα 2,ii (ΔRRRRRR ii,tt ) + αα 3,ii (PPPPPP ii,tt ) 2 As such, the term abnormal accruals includes what some papers call discretionary accruals. 3 Total accruals are calculated as: TTTT tt = [ΔCCCCCCCCCCCCCC AAAAAAAAAAAA tt ΔCCCCCCh tt ] [ΔCCCCCCCCCCCCCC LLLLLLLLLLLLLLLLLLLLLL tt ΔCCCCCCCCCCCCCC MMMMMMMMMMMMMMMMMMMM oooo LLLLLLLL TTTTTTTT DDDDDDDD tt ] DDDDDDDDDDDDDDDDDDDDDDDD aaaaaa AAAAAAAAAAAAAAAAAAAAAAAA EEEEEEEEEEEEEE tt 7

13 where AAAA ii,tt = firm ii s abnormal accruals at tt scaled by total assets at tt 1; TTTT ii,tt = firm ii s total accruals at tt scaled by total assets at tt 1; ΔRRRRRR ii,tt = firm ii s revenues in year tt less revenues in year tt 1 scaled by total assets at tt 1; PPPPPP ii,tt = firm ii s gross property, plant, and equipment in year tt scaled by total assets at tt 1; AA ii,tt 1 = firm ii s total assets at tt 1; and αα 1,ii, αα 2,ii, αα 3,ii = firm-specific parameters obtained from time-series 4 regressions of TTTT ii,tt on 1/AA ii,tt 1, ΔRRRRRR ii,tt and PPPPPP ii,tt. The intuition behind this specification is that changes in working capital are included in total accruals and dependent on changes in revenues. Therefore, changes in revenues are included in the model to control for the economic environment of the firm since revenues are fairly unbiased, i.e. subject to less managerial manipulation compared to earnings for example, thus a more objective measure of the firm s operational performance. Further, property, plant and equipment (PPE) is included to control for the portion of total accruals that comes from the depreciation expense. The inclusion of these control variables is thus an attempt to capture the portion of total accruals that is normal. Since its introduction, the Jones model has been rigorously investigated by other researchers. This has led to several modifications that aim to address the shortfalls of the original model. One such modification that has become extensively used in the literature is the so called Modified Jones model, presented by Dechow et al. (1995). The only modification that they make to the original Jones model is that they adjust the calculation of normal accruals by including the change in receivables. The reason for this modification is that the original model assumes that revenues are not subject to managerial manipulation, hence underestimating the level of abnormal accruals if revenues are manipulated. Instead, Dechow et al. s modified Jones model assumes that all changes in credit sales during a period results from earnings management. This assumption might not be completely true but it at least reduces the problem that the measure of abnormal accruals is biased towards zero as in the original Jones model (Dechow et al., 1995). Dechow et al. s modified Jones model is specified as: AAAA ii,tt = TTTT ii,tt αα 1,ii (1/AA ii,tt 1 ) + αα 2,ii (ΔRRRRRR ii,tt ΔRRRRRR ii,tt ) + αα 3,ii (PPPPPP ii,tt ) where ΔRRRRRR ii,tt = firm ii s net receivables in year tt less net receivables in year tt 1 scaled by total assets at tt 1. Abnormal accruals estimated with variations of the Jones model suffer from being correlated with firm performance (Dechow et al., 2010). To address this, Kothari et al. (2005) suggest a model where the abnormal accruals estimate of a sample firm is adjusted by deducting the abnormal accruals of the firm, within the same industry, that has the closest level of return-on-assets to that of the sample firm. This adjustment yields so called performance-matched abnormal accruals (Dechow et al., 2010). Several studies have also developed new models that compete with those based on the Jones (1991) model. However, as DeFond (2010) notes, the majority of these competitors does not survive. 4 Later studies have shown that the model works equally well when regressing cross-sectionally, either based on industry (Subramanyam, 1996) or based on size (Ecker et al., 2013). 8

14 Exceptions do exist however and two survivors are the Dechow and Dichev (2002) model and an extension of this model proposed by McNichols (2002). The Dechow and Dichev (2002) model emphasizes the importance of the matching function of accruals to cash flows. Specifically, their model is an attempt to capture the mapping of current accruals 5 (TCA) into corresponding past, present, and future cash flows. To proxy for those corresponding cash flows they use cash flow from operations 6 : TTTTTT ii,tt = αα + ββ 1 CCCCCC ii,tt 1 + ββ 2 CCCCCC ii,tt + ββ 3 CCCCCC ii,tt+1 + εε ii,tt where TTTTTT ii,tt = firm ii s current accruals in year tt scaled by average assets in year tt; and CCCCCC ii,tt = firm ii s cash flow from operations in year tt scaled by average assets in year tt. The model is regressed either on a time-series basis or a cross-sectional basis (industry-wise) and the firm-specific residuals from the regression, εε ii,tt, reflect accruals unrelated to cash flow realizations. The standard deviation of these residuals (normally measured over tt to tt 4) is Dechow and Dichev s measure of accruals quality. A higher standard deviation means that current accruals map poorer into cash flows, hence indicating poorer earnings quality. The approach is not flawless though since it is restricted to only short-term accruals (Dechow and Dichev, 2002) which implies, as Dechow et al. (2010) notes, that the model cannot handle longterm accruals such as impairments of PPE and goodwill that possibly reflect earnings management and hence are very important for the evaluation of earnings quality. Responding to these limitations, McNichols (2002) proposes an extended model and includes revenue growth as an attempt to reflect performance, and PPE to allow for a more long-term measure of accruals: TTTTTT ii,tt = αα + ββ 1 CCCCCC ii,tt 1 + ββ 2 CCCCCC ii,tt + ββ 3 CCCCCC ii,tt+1 + ββ 4 RRRRRR ii,tt + ββ 5 PPPPPP ii,tt + εε ii,tt where RRRRRR ii,tt = firm ii s change in revenues between year tt 1 and year tt scaled by average assets in year tt; and PPPPPP ii,tt = firm ii s gross value of PPE in year tt scaled by average assets in year tt. In line with the Dechow and Dichev (2002) model, accruals quality is measured as the standard deviation of firm-specific regression residuals and the interpretation is the same, i.e. higher standard deviation indicates poorer earnings quality and vice versa. Francis et al. (2005) employ McNichols (2002) proposed adjustments and find that the addition of change in revenues and PPE increases the explanatory power from an average of 39% (original Dechow and Dichev model) to an average of 50% for their sample, indicating that McNichols s (2002) extensions provide increased statistical power. An additional strength of both the original Dechow and Dichev model (2002) and McNichols s (2002) extended version are that they enable the 5 Total current accruals is calculated as: TTTTTT ii,tt = ΔCCCCCCCCCCCCCC AAAAAAAAAAAA ii,tt ΔCCCCCCh ii,tt ΔCCCCCCCCCCCCCC LLLLLLLLLLLLLLLLLLLLLL ii,tt ΔCCCCCCCCCCCCCC MMMMMMMMMMMMMMMMMMMM oooo LLLLLLLL TTTTTTTT DDDDDDDD ii,tt 6 Cash flow from operations is calculated as CCCCCC ii,tt = NNNNNN iiiiiiiiiiii bbbbbbbbbbbb eeeeeeeeeeeeeeeeeeeeeeeeee iiiiiiiiii ii,tt TTTTTTTTTT aaaaaaaaaaaaaaaa ii,tt, where TTTTTTTTTT aaaaaaaaaaaaaaaa ii,tt = TTTTTT ii,tt DDDDDDDDDDDDDDDDDDDDDDDD aaaaaa aaaaaaaaaaaaaaaaaaiioooo eeeeeeeeeeeeee ii,tt 9

15 isolation of the managed part of accruals (Dechow et al., 2010). Also, these two methods have become the accepted methodology to capture discretionary effects on earnings quality (Dechow et al., 2010) Earnings smoothness The idea of smoother earnings being of higher quality rests on the assumption that accrual based earnings is a better representation of firms true performance than an earnings number based solely on cash receipts and payments (Dechow et al., 2010). Smoothness measures are typically based on the variability in earnings relative to a benchmark figure, typically the variability in cash flows. This approach is however weak since smoothness in earnings (which can be understood as the absence of variability) can be interpreted in two ways. On the one hand, smoothness is desirable if managers smooth earnings to communicate their private information about future earnings. On the other hand, smoothness is not desirable if managers use their discretion to create a smooth earnings number for the purpose of hiding true firm performance (Tucker and Zarowin, 2006) Asymmetric timeliness and timely loss recognition Asymmetric timeliness is an expression that was first developed in Basu (1997). Basu studies how good and bad news are incorporated into firms earnings. To proxy for news he uses stock returns and the intuition behind this is that market prices reflect information that reaches the market so returns are a measure of news reaching the market in a given time period. Basu expects, and subsequently also finds, that bad news (negative returns) are relatively more reflected in current earnings than good news (positive returns). Hence, Basu (1997) argues that bad news earnings are timelier than good news earnings, mostly due to conservatism. The Basu model of asymmetric timeliness is specified as: XX ii,ττ /PP ii,ττ 1 = αα 0 + αα 1 DDDD ii,ττ + ββ 0 RR ii,ττ + ββ 1 RR ii,ττ DDDD ii,ττ + εε ττ where XX ii,ττ = firm ii s earnings in year ττ; PP ii,ττ 1 = firm ii s market capitalization in the beginning of year ττ; RR ii,ττ = firm ii s return in period ττ; and DDDD ii,ττ = dummy variable that is set to 1 (0) if RR ii,ττ < 0 (RR ii,ττ 0). In the Basu model, ββ 1 is the asymmetric timeliness coefficient and is a measure of the difference in the timeliness of loss versus profit recognition. A higher ββ 1 is understood as relatively timelier recognition of losses, which is argued to increase the usefulness of accounting earnings and therefore enhance earnings quality (Ball and Shivakumar, 2005). The Basu model is the most frequently used measure of timely loss recognition to proxy for earnings quality. However, this type of model is associated with some concerns which make the validity of the model questionable. First, it is a return based approach and as such it assumes market efficiency, meaning that variation in observed timeliness could be due to variation in the quality of the market, i.e. how it determines prices, instead of variation in the quality of earnings. Second, being a return based measure, the asymmetric timeliness coefficient not only reflects information in earnings but all 10

16 information that is available to the market and hence it is problematic to determine what effects are due to earnings information (Dechow et al., 2010) Target beating Researchers have found that earnings distributions are often skewed around zero with few firms reporting small losses and many firms reporting small profits (e.g. Hayn, 1995; and Burgstahler and Dichev, 1997). This has commonly been interpreted as evidence of earnings management. The idea is that firms which are about to report a small loss manage their earnings in such a way that they can report a small profit instead of the small, but true, loss (Dechow et al., 2010). However, as Dechow et al. (2010) note, there is mixed evidence on whether this small profit/small loss avoidance phenomenon represents earnings management, which according to Dechow et al. suggests that the phenomenon is not indicative of earnings management. This is also supported by other studies that find explanations for the skewed earnings distributions other than earnings management, such as Beaver et al. (2007) who explains the skewness as a tax issue rather than intentional earnings management. Similar to the small profit/small loss avoidance phenomenon, firms have been found to just meet or beat analysts expectations (e.g. Degeorge et al., 1999). In the literature, meeting or beating analysts expectations have more support as an indication of earnings management (Dechow et al., 2010). For example, firms manage their tax expense (Dhaliwal et al., 2004) and classification of income statement items (McVay, 2006) to just meet or beat analysts expectations. Further, target beating seems to be related to firms opportunities to meet or beat expectations. Frankel et al., (2002) find positive correlation between lower audit quality and beating analysts expectations, suggestive of firms being opportunistic in their accounting choices when possible. Nonetheless, meeting or beating targets is far from being a perfect proxy for earnings management. One disadvantage with the approach (that also applies to small profits/small loss avoidance) is that it is highly problematic to determine whether a firm meets or beats a target through manipulation or true performance (Dechow et al., 2010) Investor responsiveness to earnings The investor responsiveness to earnings stream of earnings quality research consists mostly of studies examining the earnings response coefficient (ERC). The ERC is a measure of the stock price reaction to the announcement of unexpected earnings. A stronger relation between the earnings component and the stock price means that earnings better reflect true economic performance, and hence that the earnings number is of better quality. To estimate the ERC, an earnings-return model is used. In its simplest form, such a model is specified as: RRRRRRRRRRRR ii,tt = αα + ββ(eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee ii,tt ) + εε ii,tt Here, ββ is a measure of how informative the earnings number is (the higher the ββ, the higher the informativeness) and is thus the variable of interest in these models. Further, the coefficient of determination (RR 2 ) of the regression is a measure of how value relevant the earnings number is, higher RR 2 implies more value relevant earnings (Dechow et al., 2010). 11

17 This approach to measure earnings quality is associated with some concerns that must be kept in mind. First, researchers have not been able to determine whether or not the ERC captures intentional earnings management, and as such it is questionable to use ERCs to proxy for this aspect of earnings quality. Second, ERCs only reflect the informativeness of earnings conditional on all other information available. When earnings announcements are made, the market has more information than the information contained in the earnings number. Hence, this other information might affect stock prices, meaning that if this other information is left out of the model you might have the problem that the dependent variable (the return) is explained by more variables than those included in the model. Third, in similarity with the asymmetric timeliness approach, the validity of ERCs as earnings quality proxies is conditioned on the market being efficient. If market prices do not reflect true value it is not possible to use ERCs to say something about earnings quality (Dechow et al., 2010) External indicators of earnings misstatements The usage of external indicators of earnings misstatements is to a large extent limited to US data and the external indicators that are commonly used in the literature include SEC Accounting and Auditing Enforcement Releases (AAERs), restatements, and internal control deficiencies reported under the Sarbanes Oxley Act (SOX). All of these are used to proxy for earnings misstatements, both intentional (earnings management) and unintentional errors. The idea behind this stream of research is that firms which have reported erroneous earnings (AAER firms and restatement firms) or are likely to have done so (internal control deficiencies) have lower earnings quality (Dechow et al., 2010). The main advantage of using external indicators is that no models are needed to identify low quality since either one of the aforementioned indicators clearly reflects problems in the accounting measurement system. However, the approach has its disadvantages as well. First, the sample will be limited to and dependent on enforcement bodies ambition (e.g. earnings managed within the limits of GAAP might not get too much attention) and ability (which is constrained by available resources) to detect and prosecute any irregularities. Second, it is difficult to distinguish intentional errors from unintentional errors. In the case of restatements for example, the sample will include observations of firms that are correcting unintentional errors or are applying new accounting rules retrospectively. This implies that restatements capture more than just earnings management, hence creating noise in the proxy (Dechow et al., 2010) Summary of earnings quality metrics Table 1 provides a short summary of the different categories of earnings quality measures. 12

18 TABLE 1 Summary of earnings quality metrics Empirical proxy Underlying idea Pros and cons Properties of earnings Persistence Persistent earnings represent a more sustainable earnings/cash flow stream, hence being more useful in a valuation context + Consistent with the view of earnings as being a good summary metric of expected cash flows, hence useful for equity valuation - May be achieved through earnings management Abnormal accruals Accrual model residuals Smoothness Timely loss recognition Target beating Investor responsiveness to earnings External indicators of earnings misstatements Abnormal accruals are lower quality since they represent a less persistent earnings component Residuals represent management's discretionary choices and errors in the measurement system Accrual earnings (more smooth) better represent fundamental performance than do cash earnings (less smooth) Timelier recognition of losses increases the usefulness of earnings Skewed earnings distributions indicate earnings management Stronger relation between earnings and stock price indicates earnings better reflect fundamentals Firms who have reported erroneous earnings or are likely to do so have poorer earnings quality + Provides a direct approach to capture problems with the accounting measurement system - Differences in abnormal accruals could be driven by differences in fundamental performance + Provides a direct approach to isolate the discretionary or error part of accruals - Difficult to distinguish between what is discretionary and unintended errors - Smoothness may be the result of fundamentals, accounting rules, or earnings manipulation and it is difficult to determine from where smoothness comes - Assumes market efficiency and does not only reflect information in earnings - Skewness not necessarily due to earnings management - Assumes market efficiency and does not only reflect information in earnings + No need to estimate quality - Limited samples and difficulties in distinguishing between intentional and unintentional errors Notes: This table summarizes the most common earnings quality metrics. 13

19 2.2 Individual characteristics and earnings quality Having introduced the concept of earnings quality and different ways to measure it, we now turn to the relationship between individual characteristics and earnings quality, i.e. our first research question. We begin with reviewing previous research within this area. Next, we present different theoretical perspectives on the relationship between executive reputation and earnings quality that are used to develop hypotheses related to our first research question Executive reputation and ability Two personal characteristics that are closely related to each other are managerial reputation and ability. Francis et al. (2008) look at CEOs in S&P 500 companies over the period and investigate the relationship between the reputation of these CEOs and earnings quality. They define reputation as the totality of enduring images that major stakeholders form based on perceived CEO performance, his or her ability, and values (Francis et al., 2008; p. 114), and to proxy for reputation they use the number of articles containing the CEO s name in major US and global business newspapers and business wire services. They argue that the relation between CEO reputation and earnings quality is endogenous and therefore model it as a simultaneous equation system. The system consists of two equations: one where earnings quality is explained by CEO reputation and a set of control variables, and one where CEO reputation is explained by earnings quality and a set of control variables. They estimate earnings quality by employing both the Dechow and Dichev (2002) model and performance-matched abnormal accruals (Kothari et al., 2005) (as well as the extended Dechow and Dichev (McNichols, 2002) in unreported tests) and find firm-specific factors to be most important in explaining earnings quality but that CEO reputation also is a significant factor in explaining earnings quality. The relationship between CEO reputation and earnings quality is found to be negative. According to Francis et al. (2008) this finding is consistent with more reputed CEOs either behaving opportunistically or being picked by firms with poorer earnings quality because such firms require their superior skills. They perform several tests to distinguish between these two explanations and end up with the conclusion that more reputed CEOs are associated with poorer earnings quality because poor earnings quality firms require the talents of more reputed CEOs, rather than more reputed CEOs behaving more opportunistically than less reputed CEOs. Malmendier and Tate (2009) use a slightly different approach. From the perspective that people who experience a sudden rise to fame may change their behaviour to prevent that they delve back to anonymity, they examine how award winning managers, so called superstar CEOs, in the US perform after being rewarded. They analyse performance measures such as return on assets, book-to-market ratio, and market capitalization and find that award winning CEOs firms performs worse afterward, and that the CEOs engage in more earnings management to meet increased expectations that follow the award. More specifically, they find that exactly meeting analysts earnings forecasts occurs more often after 14

20 CEOs win awards and that award-winning CEOs exactly meet earnings forecasts more frequently than their non-winning counterparts. Further, they find that the occurrence of this earnings manipulation is mainly concentrated to firms with weak corporate governance, suggesting that CEOs take advantage of their power to reap personal benefits. Lastly, their findings suggest that award-winning CEOs attempt to maintain their superstar status for as long as possible by inflating their earnings numbers. Aier et al. (2005) instead examine the CFO and what role different CFO characteristics play for earnings quality. Using US data from 1997 to 2002 they look at CFO characteristics including years of work as a CFO, experience at another company, education, and professional certifications and they use accounting restatements to proxy for earnings quality. Using logit models they test if the likelihood of restating earnings is associated with characteristics of the CFO. Their results indicate that CFO expertise is negatively associated with the incidence of restatements. More specifically, they find that CFOs with more work experience as CFO, advanced degrees (master s degree in business administration or higher), and/or professional certifications are less likely to be involved in restatements, i.e. associated with better earnings quality. Aier et al. (2005) are, however, unable to tell if less skilled CFOs cause restatements or if companies doing restatements more frequently choose to hire less skilled CFOs. Furthermore, the authors emphasize that the study suffers from limited data availability, which may have influenced the outcome. A recent study by Demerjian et al. (2013) derive their measure of managerial ability from firmspecific accounting data. This differs from the previously discussed studies that all use non-financial data (number of citations in business press, awards and experience/education) to capture individual characteristics. Using quantitative analysis Demerjian et al. (2013) calculate a firm-efficiency measure and subsequently separate the efficiency measure into one part that is explained by firm-specific factors (e.g. market share, business segment concentration) and a residual which they attribute to the manager, which is the CFO in this case. This residual is thus Demerjian et al. s (2013) measure of CFO ability. They relate CFO ability to several different measures of earnings quality and, using US data from 1989 to 2009, they find that more able managers are associated with fewer restatements, higher persistence, less errors in the bad debt provision estimation, and better accrual estimations (they estimate accrual quality through the extended Dechow and Dichev model (McNichols, 2002)). They explain this pattern as a result of more able managers being more knowledgeable with regards to e.g. the firm s operations, macro-economic environment, and accounting standards, and hence able to estimate accruals more accurately, which results in a higher quality earnings number. Subsequently, they conclude that managers can and do impact earnings quality and that the relationship between managerial ability and earnings quality is positive Other characteristics Other individual characteristics that have been examined in the context of earnings quality are overconfidence and gender. According to Skala (2008), overconfidence in finance is usually an 15