The accrual anomaly in Europe: The role of accounting distortions

International Review of Financial Analysis 41 (2015) 176–185

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International Review of Financial Analysis

The accrual anomaly in Europe: The role of accounting distortions☆ Georgios A. Papanastasopoulos a,⁎, Emmanuel Tsiritakis b a b

Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou Street, 18534 Piraeus, Greece Department of Banking and Financial Management, University of Piraeus, 80 Karaoli & Dimitriou Street, 18534 Piraeus, Greece

a r t i c l e

i n f o

Article history: Received 5 February 2015 Accepted 18 June 2015 Available online 25 June 2015 JEL classification: M41 Keywords: Accounting accruals Accounting distortions Accrual anomaly International stock markets Trust

a b s t r a c t Numerous studies claim that the accrual anomaly in the U.S. stock market is due mostly to temporary accounting distortions arising from accrual accounting. We examine the validity of this explanation in an international setting. Across the 15 developed European equity markets we examine, accounting distortions contribute to the negative relation between accruals and future earnings performance in 14 equity markets. Further, we show that the negative relation between accruals and stock returns could be at least attributable to accounting distortions. In particular, accruals related to accounting distortions predict returns in 7 out of the 9 markets where the accrual anomaly occurs in Europe. Finally, we show that the impact of accounting distortions on the pricing of the accrual component of earnings is stronger in markets with a higher level of trust and a lower level of secrecy. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The accrual anomaly refers to the negative relation of accounting accruals with future earnings and stock returns. This pattern, first documented by Sloan (1996), presents an important challenge to rational asset pricing theories (Fama & French, 2008). Subsequent research extends Sloan's (1996) work by examining various aspects of the accrual anomaly. The primary motivation behind these studies is to assess the economic rationale for the subsequent drift in earnings and stock price performance. An extensive body of these studies (e.g., Chan, Chan, Jegadeesh, & Lakonishok, 2006; Dechow & Dichev, 2002; Richardson, Sloan, Soliman, & Tuna, 2005, 2006; Xie, 2001) follows that of Sloan (1996) and links the accrual anomaly to the greater subjectivity involved in the estimation of accruals. Accruals are negatively related with future earnings performance due to accounting distortions associated with their higher subjectivity. Such distortions could arise from estimation errors in accruing future benefits and obligations and from the opportunistic use of accruals by managers to mislead users of financial statements. Investors' misunderstanding of the implications of accounting ☆ The authors appreciate the helpful comments from the seminar participants at the 4th International Conference Meeting of the European Asian Economics, Finance, Econometrics and Accounting Association (AEEFA). The authors thank Gikas Hardouvelis and Dimitrios Thomakos for their insightful comments and suggestions. The usual disclaimer applies. ⁎ Corresponding author. E-mail addresses: [email protected] (G.A. Papanastasopoulos), [email protected] (E. Tsiritakis).

http://dx.doi.org/10.1016/j.irfa.2015.06.006 1057-5219/© 2015 Elsevier Inc. All rights reserved.

distortions, leads to significant overweighting of accruals in pricing stocks. Hence, under a mispricing-based interpretation, accounting distortions could possibly have an important role behind the underperformance of firms with high accruals relative to those with low accruals. The abovementioned studies focus on U.S. firms. The study by Pincus, Rajgopal, and Venkatachalam (2007) constitutes the first published international investigation in the literature showing that the accrual anomaly with respect to both future profitability and stock returns can be generalized to different country settings. In particular, Pincus et al. (2007) document the occurrence of the accrual anomaly in Australia, Canada and the U.K. Further, Pincus et al. (2007) consider in their analysis discretionary (abnormal) accruals and find supportive evidence of a significant role for earnings management in explaining the occurrence of the accrual anomaly.1 Recently, Papanastasopoulos (2014) finds that the accrual effect on stock returns occurs in 11 European capital markets: Belgium, Denmark, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland, and the UK. He also shows that accrual effect on stock returns in Europe is influenced by country-level factors that include culture, equity-market setting, financial analysts' research output, shareholder protection, and ownership structure. Yet, he finds factors that include accounting regimes and quality of reported accounting figures lacking similar influence. However, tests based on measures of discretionary (abnormal) accruals are not considered in the above 1 Numerous studies link abnormal (discretionary) to earnings management (e.g., Dechow, Ge, & Schrand, 2010).

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study, which does not help to assess whether the accrual effect on stock returns could be linked to accounting distortions in Europe. Concurrently, Doukakis and Papanastasopoulos (2014) show that accounting distortions constitute an important contributing factor on the accrual anomaly in the largest European capital market; the U.K. In a similar vein, Mouselli, Jaafar, and Goddard (2013) show that for the U.K. stocks, accruals quality explains the cross-section of stock returns, but does not represent an asset pricing risk factor. The purpose of this paper is to provide additional insights on the possible contributing role of accounting distortions on the accrual anomaly in Europe. We are motivated by a desire to get a deeper understanding of the properties of accruals and their potential effect on investor decision making in an international setting. We choose to focus on a sample of European countries with developed economies, where, expost, the possibility of mispricing is expected to be lower relative to the respective possibility on countries with emerging but less advanced economies. A key feature of our research design is that we do not rely on statistically oriented decompositions typically used in the literature to measure the component of accruals that picks up accounting distortions, but instead rely on Richardson et al.'s (2006) algebraic model. Second, we investigate whether trust could explain the implications of accounting distortions on the relation between accruals and stock returns at the country level. Prior research (e.g., Bushman & Piotroski, 2006; Bushman & Smith, 2001) clearly suggests that cross-country differences in institutional structures could affect financial reporting and the relation between accounting figures and stock returns. In the context of the accrual–return relation, existing research by Pincus et al. (2007) and Papanastasopoulos (2014) focuses more on formal institutions such as legal tradition, equity-market setting, accounting structure and investor protection mechanisms. The impact of informal institutions (societal norms and cultures) has been largely ignored and has been only recently assessed by Papanastasopoulos (2014) with emphasis on individualism and uncertainty avoidance. We need to stress here, that a recent but growing literature (Aggarwal & Goodell, 2009, 2010; Aggarwal, Kearney, & Lucey, 2012; Forner & Sanabria, 2010; Frijns, Gilbert, Lehnert, & Tourani-Rad, 2013; Siegel, Licht, & Schwartz, 2011) clearly suggests that societal norms and cultural dimensions could significantly affect actions and observed outcomes in accounting, economics and finance.2 We differentiate our study with prior research, by focusing on trust, which has been shown to affect economic growth, capital market development, government regulation, international trade, stock market participation and hiring money managers within a country (Gennaioli, Shleifer, & Vishny, 2015; Guiso, Sapienza, & Zingales, 2006, 2008; Knack & Keefer, 1997; Stulz & Williamson, 2003). Notably, Guiso et al. (2008) provide evidence that societal trust significantly impacts investors' portfolio decisions. Recently, Gennaioli et al. (2015) claim that when investors hold biased expectations, a higher level of trust causes money managers to pander to their beliefs, rather than to correct their errors. These issues form our essential motivation to posit that the level of trust in a country affects investors' subjective beliefs about the credibility of accounting accruals and, thereby, affecting the pricing of the accrual component of earnings. Our research is important for at least three reasons. First, we provide evidence on the pervasiveness of the impact of accounting distortions on corporate performance outside the U.S. stock market. As pointed out by Richardson et al. (2006, pp. 741) such an exercise is a one of the most important goals in financial accounting research. Second, we investigate whether accounting distortions constitute an important challenge for market efficiency in a global setting. Third, given that accounting information is used for contracting and investment decisions, by focusing on trust we provide insights on how investors from different

2

This literature is extensively reviewed by Aggarwal and Goodell (2014).

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social backgrounds do react differently to the distortions arising from accrual accounting. Our sample spans the period 1989–2008 and consists of the following 15 European countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden and Switzerland. Initially, we investigate the presence and the magnitude of the accrual anomaly in each country by considering the slope coefficient from Fama and MacBeth (1973) cross-sectional regressions of future operating profitability and future abnormal returns on current accruals, conditional on the current operating profitability. Then, within each country we group firms annually into portfolio quintiles based on their ranking of accruals and examine within each of these portfolios time series averages of the change between future operating profitability and current operating profitability, as well as time series averages of the abnormal returns of portfolios formed on the magnitude of total accruals. We also consider time-series averages of the spread in profitability changes and the spread in returns between extreme accrual portfolios. If the accrual anomaly occurs in a country, we expect a negative slope coefficient and a positive spread in profitability changes and in returns between the lowest and the highest portfolio. Further, following Watanabe, Xu, Yao, and Yu (2013), we report statistics using a countryneutral approach and a country-pooling approach. In the country-neutral approach we report cross-country averages of the slope coefficient from regressions and the spread from portfolios. In the country-pooling approach we report the slope coefficient from regressions and the spread from portfolios when stocks are pooled across countries. In order to quantify the contributing role of accounting distortions at the accrual anomaly in each country, we conduct the same tests mentioned above by replacing accruals with the component of accruals capturing accounting distortions. According to Richardson et al. (2006) algebraic model accruals are equal to a component capturing growth (i.e., accruals attributable to growth) minus a component capturing accounting distortions minus an interaction term between these components. Thereby, reductions (increases) in the component capturing accounting distortions lead to proportional increases (reductions) in accruals. Put another way, a low or even a negative value of this component ceteris paribus indicates a firm with high accruals due to accounting distortions. Given the impact of this component on the level of total accruals, it is configured in cross-sectional regressions with a negative sign. If accounting distortions have a contributing role in the accrual anomaly in a given country, we expect a negative slope coefficient on the component of accruals capturing accounting distortions. At the same time, we expect a positive spread in profitability changes and in returns between the lowest portfolio on this component (i.e., the portfolio which consists of firms with high accruals due to more accounting distortions) and the highest portfolio on this component (i.e., the one which consists of firms with low accruals due to less accounting distortions). Based on the Fama and MacBeth (1973) cross-sectional regressions, we find a strong negative relation between accruals and future profitability in all European countries considered in our analysis, except Austria. We also show that in all cases, the component of accruals that is attributable to accounting distortions contributes significantly and strongly to this negative relation. When we group firms into portfolio quintiles each year based on their ranking of accruals, we find that firms within the top accrual portfolio to experience significant reductions in their future accounting rate of return. A similar finding emerges for the bottom portfolio on the component capturing accounting distortions. In all countries examined (i.e., Austria is the exception), the spread in the accounting rate of return between extreme portfolios formed on the magnitude of both accrual measures is positive. Additional results from the Fama and MacBeth (1973) crosssectional regressions and firm-level portfolio tests confirm prior evidence documented on the generalization of the accrual effect on stock

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returns in Europe. There is a strong negative effect of accruals on stock returns in Belgium, France, Germany, Italy, Netherlands, Spain, Sweden and Switzerland. Within the above countries the bottom accrual portfolio outperforms on average the top accrual portfolio by a significant 7.4% in the following year, while within all European countries the respective outperformance falls to 5.9%. The outperformance when all stocks are pooled across all countries is 7.5%. Concerning accounting distortions, we find that it has a significant role on the generalization of the accrual effect on stock returns in Europe. In particular, accounting distortions is a contributing factor on the occurrence of the accrual–return relation in France, Germany, Netherlands, Spain, Sweden and Switzerland. Within these countries, the average spread in abnormal returns between extreme portfolios formed on the magnitude of accruals capturing accounting distortions is significantly positive at 6.2% per year. The respective spread averaged across all countries included in our tests is significantly positive at 3.50% per year. The spread when all stocks are pooled across all countries under consideration is equal to 3.8%. There is also substantial variation in the spread; it ranges from −1.6% for Greece to 10.2% for Denmark. Our country-level results concerning the influence of trust reveal that the implications of distortions arising from accrual accounting on the accrual–return relation are magnified and become more severe in countries with a higher level of generalized trust and a lower level of secrecy (i.e., an inverse measure of trust representing mistrust). We organize the remainder of the paper as follows. In the next section we develop our hypotheses. Section 3 describes the data, sample formation and variable measurement, Section 4 presents our empirical results, and Section 5 provides the concluding remarks. 2. Hypothesis development In a path-breaking paper, Sloan (1996) documents the well-known accrual anomaly. Accruals are negatively related with future earnings performance and stock returns. Sloan (1996) claims that the negative relation between accruals and future profitability arises because accruals are more subjective, while investors do not fully comprehend that greater subjectivity and make flawed pricing decisions. Such distortions could arise from estimation errors in accruing future benefits and obligations and from the opportunistic use of accruals by managers to mislead users of financial statements. An important implication of Sloan's (1996) explanation is that investors' expectations of future earnings are biased upwards (downwards) for firms with high (low) accruals. Consistent with this implication Sloan (1996) shows that firms with high (low) accruals experience around future earnings announcements negative (positive) abnormal returns. In a similar vein, Bradshaw, Richardson, and Sloan (2001) show that financial analysts' forecasts are relatively optimistic (pessimistic) for firms with high (low) accruals. Numerous studies extend Sloan's (1996) work on the accrual anomaly. These studies can be divided in two broad categories on the basis of the approach that they adopt. The first set of studies, builds on Sloan's (1996) subjectivity conjecture. In particular, Xie (2001) decomposes accruals into a normal (nondiscretionary) component and an abnormal (discretionary) component and shows that the accrual anomaly is mostly attributable to the latter component, suggesting that investors misunderstand potential earnings management. Similar findings are found by Chan et al. (2006). Dechow and Dichev (2002) provide evidence that firms with low accrual quality have less persistent earnings. Richardson et al. (2005) draw a natural link between Sloan's (1996) notion of subjectivity and the well-known concept of reliability. Specifically, they provide a comprehensive definition and categorization of accounting accruals in which each accrual category is rated according to its reliability and they document that less reliable accruals lead to lower earnings persistence and that investors do not fully anticipate this lower earnings persistence, leading to significant security mispricing.

The second set of studies adopts the viewpoint that the accrual anomaly applies more broadly to firm growth or correlated economic characteristics with firm growth. Fairfield, Whisenant, and Yohn (2003) show that it is a special case of a more general growth anomaly, suggesting that it arises from investors' misunderstanding of diminishing marginal returns to increased investment. Wu, Zhang, and Zhang (2010) argue that the anomaly arises from optimal investment growth as a rational response by firm executives to a falling discount rate. Richardson et al. (2006) provide a parsimonious algebraic decomposition of accruals into a component capturing growth, a component capturing accounting distortions and an interaction term between these components. Richardson et al. (2006) argue that this model is superior to the statistically oriented models (e.g., the Jones, 1991 model) used by Xie (2001) and Chan et al. (2006) in the context of the accrual anomaly in order to decompose accruals into a discretionary and a nondiscretionary component; it controls for non-linearities, and it does not require the estimation of any parameters since it is an algebraic identity. Richardson et al. (2006) show that the component capturing accounting distortions has the primary contributing role in the negative relation between accruals and future earnings performance. However, they do not rule the possibility that the growth component could also constitute a supplementary driving force. Pincus et al. (2007) and Papanastasopoulos (2014) show that the occurrence of the accrual anomaly is not specific to the U.S. stock market. Pincus et al. (2007) provides evidence that the anomaly occurs in Australia, Canada and the U.K., while Papanastasopoulos (2014) in Belgium, Denmark, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland, and the UK. Both studies focus on how fundamental formal and informal institutions can affect the occurrence and the magnitude of the accrual anomaly at the country level. However, research on what underlying factors drive the negative relation of accruals with future earnings and returns at the firm-level within the above-mentioned countries is limited. Pincus et al. (2007) show that the discretionary accruals measured by Jones (1991) predict returns in Australia, Canada and the U.K. Recently, Doukakis and Papanastasopoulos (2014) show that accruals attributable to accounting distortions exhibit a negative impact on future profitability and stock returns in the U.K. stock market. In the same capital market, Mouselli et al. (2013) provide evidence that accruals quality is useful in explaining portfolios' returns in the cross-section, but does not constitute a priced risk factor. The purpose of this paper is to provide additional insights on whether accounting distortions constitute an important driving factor behind the generalization of the accrual anomaly in an international setting. Our focus is on large European capital markets, where gathered evidence is most supportive of the existence of the accrual anomaly. In the global setting, we can have the presumption that there is a substantial cross-country variation in formal and informal institutional factors such as capital market setting, regulation, business practices, accounting structure, societal norms and culture. These institutions could be linked with the contributing role of accounting distortions on the negative relation of accruals with future earnings performance and stock returns. Thus, whether the contributing role of accounting distortions on the accrual anomaly documented in the U.S. stock market, can be generalized in other European countries is questionable. This leads to the first hypothesis (expressed as the alternative) of the study: H1. Accounting distortions have a contributing role on the accrual anomaly in Europe. Given that accounting information is a primary source of information used in investment decisions, we assess whether social trust constitutes an underlying channel (beyond traditional formal institutions) in affecting investors' understanding of the implications of accounting distortions and in the end investors' pricing of the accrual component

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of earnings. If social trust affects people's information processing, individuals may process and respond differently to the same feature of accounting accruals because they have different social and cultural connections to the features of accrual accounting. Guiso et al. (2008) define trust as the subjective probability that individuals attribute to the possibility of being cheated. This subjective probability depends not only on objective characteristics of the financial system (e.g., investor protection mechanism, legal enforcement), but also on subjective characteristics of the person trusting. As argued by Guiso et al. (2008) the decision to invest in a security requires trust in the fairness of the financial system and in the reliability of the numbers to invest in it. Guiso et al. (2008) provide evidence that a higher level of trust leads to higher stock market participation across countries. Thus, in countries with a higher level of trust, investors when investing in stocks are more likely to believe that they are characterized by reliable accounting figures. In doing so, they ignore that an important feature of accrual accounting is that the increase in the relevance of accounting numbers comes at the cost of reduced reliability. Reduced reliability results in temporary accounting distortions that in turn could potentially have a negative impact on future earnings performance. This insight, if it holds true, leads us to conjecture that the level of trust in a country could potentially influence investors' subjective beliefs about the credibility of accounting accruals and, thereby, affect the pricing of the accrual component of earnings. In this line, Gennaioli et al. (2015) show that when investors hold biased expectations, a higher level of trust causes money managers to pander their subjective beliefs rather than to correct their errors. Indeed, in the context of the accrual anomaly the presence of expectations bias is highly possible. The above discussion leads to the second testable hypothesis of our study: H2. The level of trust affects the contributing role of accounting distortions on the pricing of the accruals in an international setting.

3. Data & variable measurement

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accounting variables and annual stock returns (see Watanabe et al., 2013). Finally, we exclude firm-year observations with negative book value of equity (see Titman, Wei, & Xie, 2013) and negative value of net operating assets (see Richardson et al., 2006). After this screening process, our final sample consists of 40,539 firmyear observations (equivalent to 486,468 firm-month observations). In Table 3 we provide details about the final sample. As Table 3 shows, France and Germany represent the largest equity markets, accounting for about 40% of the total firm-year observations. Overall, there is substantial variation in firm-year observations across countries. 3.2. Measurement of firm-level variables In Table 1 we summarize the measurement of our variables at the firm-level and at the country-level. Using the indirect (balance sheet) method, we measure accruals (ACC) as the percentage change in net operating assets (NOA): ACCt ¼

ΔNOAt : NOAt−1

ð1Þ

NOA are equal to the difference between operating assets (OA) and operating liabilities (OL): NOAt ¼ OAt −OLt :

ð2Þ

Operating assets are equal to the difference between total assets (TA, Worldscope data item #02999) and cash (C, Worldscope data item #02001). Operating liabilities are equal to total assets minus minority interest (MINT, Worldscope data item #03426) minus ordinary & preferred stocks (OPS, Worldscope data item #03995) minus total debt (TD, Worldscope data item #03255). OAt ¼ TAt −Ct

ð3Þ

OLt ¼ ðTAt −MINTt −OPSt −TDt Þ:

ð4Þ

3.1. Sample selection Our sample consists of firm-level and country-level data that are obtained from various sources. The sample covers the following European countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and Switzerland. We start with all firms included in the active-firm lists and dead-firm lists of Worldscope-Datastream International, provided by Thomson Financial from January 1988 to January 2009 to avoid survivorship bias. We select all domestic common stocks listed on the major stock exchanges in each country and from them we remove financial firms that have Datastream industry codes (INDM) corresponding to the four-digit SIC codes between 6000 and 6999. All firm-level accounting and market variables are in US dollars. We also apply all the data screening procedures proposed by the existing literature. First, any monthly return (i.e., the percentage change in Datastream's month-end total return index RI) above 300% that is reversed within one month is set to be missing (see Ince & Porter, 2006). Specifically, if Rt or Rt − 1 (i.e., the gross return in montht and montht − 1, respectively) is greater than 300%, and (1 + Rt)(1 + Rt − 1) − 1 b 50%, then both Rt and Rt − 1are set to be missing. Second, we eliminate all monthly observations for delisted stocks from the end of the sample period to the first non-zero return date since Datastream keeps padding the last available data after the delisting date (see Ince & Porter, 2006). Third, we trim monthly returns at the top and bottom one percentiles of their distributions within each country (see McLean, Pontiff, & Watanabe, 2009). Fourth, all accounting variables are winsorized at the top and bottom one percentiles of their distribution within each country (see McLean et al., 2009). Fifth, we require a market to have at least 30 stocks with valid observations of

The component of accruals capturing accounting distortions is defined from Richardson et al.'s (2006) model as the change in NOA Table 1 Variable measurement. Total accruals (ACC): Percentage change in net operating assets (NOA). NOA is the difference between operating assets (data item #02999–data item #02001) and operating liabilities (data item #02999–data item #03995–data item #02999–data item #03426). Accounting distortions (ΔAT): Change in NOA turnover ratio deflated by current NOA turnover ratio. NOA turnover ratio is equal to the ratio of sales (data item #01001). Operating profitability (RNOA): Operating income (data item 01250) deflated by lagged NOA. Abnormal returns (ARET): It is the one-year ahead abnormal return measured as follows. Six months after financial year-end, firms are first sorted into four quartile portfolios by market capitalization and in each of the resulted portfolios firms are further sorted into other four quartile portfolios by book to market ratio. Market capitalization (data item #08001) is measured at the financial year-end. Book to market ratio is measured as the natural logarithm of the ratio of the book value of equity (data item #03501) to the market capitalization. This procedure results in 16 benchmark portfolios and the matching return is the annual one-year ahead weighted average return for each benchmark portfolio. Then, the one-year ahead abnormal return is defined as the difference between the one year ahead raw return (RET) and the matching return of the benchmark portfolio to which the firm belongs. RET is measured as follows. The raw equity return at month is first calculated, using the return index provided by Datastream (item RI) Then, RET is calculated using compounded 12-monthly buy-and-hold returns. The 12-month return cumulation period begins six months after financial year-end. Trust (TRUST): Share of population answering yes to the question “In general, do you think that most people can be trusted, or can't you be too careful in dealing with people?” (data source: Bjornskov (2006)). Secrecy (SECRECY): The sum of uncertainty avoidance and power distance less individualism (data source: Hope et al. (2008)).

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turnover ratio (ΔAT).3 NOA turnover is measured as the ratio of sales (Worldscope data item #01001) to NOA.  ΔATt ¼ ATt

   Salest Salest−1 − NOAt NOAt−1   : Salest NOAt

ð5Þ

As in Richardson et al. (2006), operating profitability is measured as the ratio of operating profit (OI, Worldscope data item #01250) to lagged NOA (i.e., return on net operating assets). In order to make sure that the accounting variables are known before the returns, we follow Fama and French (1992) and match the financial statement data for financial year-end with one-year ahead annual returns that are measured based on a 12-month return cumulation period that begins six months after the financial year-end. The raw equity return at the month-level is first calculated, using the return index provided by Datastream (item RI). Then, the one-year ahead annual raw return (RET) is calculated using compounded 12-monthly buy-and-hold returns. The one-year ahead abnormal return (ARET) is measured as follows. Six months after financial year-end, firms are first sorted into four quartile portfolios by market capitalization and in each of the resulted portfolios firms are further sorted into other four quartile portfolios by book to market ratio. Market capitalization (Worldscope data item #08001) is measured at the financial year-end. Book to market ratio is measured as the natural logarithm of the ratio of the book value of equity (Worldscope data item #03501) to the market capitalization. This procedure results in 16 benchmark portfolios and the matching return is the annual one-year ahead weighted average return for each benchmark portfolio. The one-year ahead abnormal return (ARET) is defined as the difference between the one-year ahead raw return (RET) and the matching return of the benchmark portfolio to which the firm belongs.

3.3. Measurement of country-level variables Concerning country-level variables, we use a standard empirical proxy of societal trust that is based on the share of population answering yes to the question “In general, do you think that most people can be trusted, or can't you be too careful in dealing with people?” This question has been included in many surveys such as the General Social Survey (GSS) since the late 1950s and has been asked in all waves of the WorldValues Survey (WVS) since the early 1980s. In particular, we use a measure based on scores obtained from the two waves of the World Values Survey (WVS) conducted in 1997 and 1999–2001. In the case that a country has been included in both waves the measure takes scores from the most recent wave. Data on this measure are taken from Bjornskov (2006) and are tabulated in Table 2. As one can see, there is substantial cross-country variation in this measure of generalized trust. Bjornskov (2006) provides evidence that this trust measure appears to be reliable and remarkably stable across time. We also follow Hope, Kang, Thomas, and Yoo (2008) and use secrecy as an inverse measure of trust. Hope et al. (2008) construct this secrecy measure as the sum of uncertainty avoidance and power distance less individualism. Data on this measure that are tabulated in Table 2 reveal a large variation in terms of secrecy across European countries. As expected, we find a strong negative correlation between generalized trust and secrecy that is equal to −0.807.

3 Jansen, Ramnath, and Yohn (2012) argue that changes in NOA turnover ratio can be used a diagnostic for earnings management.

Table 2 Data & descriptive statistics on trust and secrecy. Country

TRUST

SECRECY

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Mean Median St. dev.

33.9 30.7 66.5 58 22.2 34.8 23.7 35.2 32.6 59.8 65.3 10.1 36.2 66.3 40.9 41.08 35.2 17.834

26 84 −33 29 83 33 137 −7 49 11 12 140 92 −11 24 44.6 29 52.131

4. Results 4.1. Descriptive statistics of extreme accrual portfolios In Table 3, we report the mean value of accruals and accruals capturing accounting distortions for country-specific portfolios, countryaverage portfolios and portfolios when countries are considered alltogether. Country-specific portfolios are formed as follows: each year (six months after the financial year-end) within a country, firms are first sorted on accruals and then allocated into five equal-sized portfolios (quintiles) based on these ranks. We report time-series averages of accruals and accruals capturing accounting distortions for the lowest portfolio and the highest portfolio. A “country-average” portfolio is formed as a portfolio that puts an equal weight on each countryspecific portfolio. The “all-countries” portfolios are formed with the same procedure used for country-specific portfolios with firms from all countries. Panel A presents the mean value of total accruals, while Panel B the mean value of accounting distortions. Mean values of accruals are highly negative (positive) within the lowest (highest) accrual quintile. Further, accounting distortions contribute significantly to the mean value of accruals. In particular, the mean values of the component of accruals capturing accounting distortions are highly positive (negative) within the lowest (highest) accrual quintile. Similar findings are reported by Richardson et al. (2006) for U.S. firms. Overall, these results suggest that also in Europe the lowest (highest) accrual portfolio corresponds to temporarily conservative (aggressive) accounting.4 4.2. Implications of accounting distortions on the relation of accruals with future earnings performance In Table 4 we report results from the Fama and MacBeth (1973) regressions of one-year ahead operating profitability on accruals and accruals capturing accounting distortions, after controlling for current operating profitability.5 We estimate annual cross-sectional regressions over the period 1988–2009 and report the time-series averages of the parameter coefficients and adjusted R2. Panel A presents estimation results for the following regression: RNOAtþ1 ¼ γ0 þ γ1 RNOAt þ γ2 ACCt þ υtþ1 :

4 See in Richardson et al. (2006) the numerical examples illustrating the effect of accounting distortions on profitability and accruals (pp. 725–726). 5 Following Richardson et al. (2006) we focus on the relation of total operating accruals with future operating profitability.

G.A. Papanastasopoulos, E. Tsiritakis / International Review of Financial Analysis 41 (2015) 176–185 Table 3 Mean values of accruals and accruals capturing accounting distortions for extreme portfolios of firm-years formed on quintile rankings of accruals.

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Table 4 Regressions of future operating profitability on current operating profitability, accruals and accruals capturing accounting distortions. Panel A: RNOAt + 1 = γ0 + γ1RNOAt + γ2ACCt + υt + 1

Panel A: Mean values of accruals within extreme portfolios on ACC Country

Obs.

% of obs.

Low

High

Country

γ0

γ1

γ2

Adj. R2

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

912 1256 1944 1538 8732 7015 2719 695 2889 2260 1973 891 1615 3440 2660 2201 40,539

2.250% 3.098% 4.795% 3.794% 21.540% 17.304% 6.707% 1.714% 7.126% 5.575% 4.867% 2.198% 3.984% 8.486% 6.562% 6667% 100%

−0.22*** −0.209*** −0.151*** −0.186*** −0.181*** −0.289*** −0.148*** −0.173*** −0.16*** −0.195*** −0.288*** −0.142*** −0.167*** −0.246*** −0.182*** −0.196*** −0.204***

1.237** 0.803*** 0.629*** 0.617*** 0.829*** 1.148*** 0.748*** 0.886*** 0.693*** 0.717*** 1.297*** 0.585*** 0.638*** 1.091*** 0.52*** 0.829*** 0.84***

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

0.008 0.037*** 0.031*** 0.029*** 0.038*** 0.008 0.027*** 0.056*** 0.012** 0.055*** 0.049*** 0.012** 0.03*** 0.022 0.03*** 0.03*** 0.031***

0.669*** 0.737*** 0.723*** 0.801*** 0.688*** 0.636*** 0.746*** 0.884*** 0.768*** 0.676*** 0.544*** 0.749*** 0.774*** 0.701*** 0.795*** 0.726*** 0.695***

−0.003 −0.096*** −0.096*** −0.171*** −0.091*** −0.053*** −0.083*** −0.18* −0.056*** −0.092*** −0.076*** −0.041*** −0.078*** −0.053** −0.124*** −0.086*** −0.074***

0.466 0.514 0.537 0.557 0.53 0.45 0.591 0.669 0.623 0.574 0.359 0.574 0.569 0.512 0.602 0.542 0.53

Panel B: Mean values of accruals capturing accounting distortions within extreme portfolios on ACC Country

Low

High

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

0.122 0.202*** 0.149*** 0.171*** 0.191*** 0.264*** 0.148*** 0.131*** 0.133*** 0.174*** 0.247*** 0.145*** 0.19*** 0.214*** 0.162*** 0.176*** 0.191***

−0.747** −0.492*** −0.347*** −0.328*** −0.426*** −0.636*** −0.42*** −0.387*** −0.357*** −0.408*** −0.701*** −0.31*** −0.285*** −0.556*** −0.263*** −0.444*** −0.437***

Note: The sample consists of 40,539 firm-year observations over the period 1988–2009. All variables are defined in Table 1. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

The presence of the negative relation between accruals and future earnings performance implies a negative coefficient on accruals (i.e., γ2 b 0). Indeed, results confirm the existence of the negative impact of accruals on future earnings performance in Europe. The coefficient on accruals is highly negative and significant in all European countries under consideration, except Austria. The country-average coefficient on accruals in Europe is equal to −0.086. When, all European countries are considered together, the coefficient decreases to − 0.074. The respective coefficient for the U.S. reported by Richardson et al. (2006) is equal to − 0.131. Thus, the magnitude of the accrual effect on future profitability is somewhat less intense in Europe. Panel B presents estimation results for the following regression: RNOAtþ1 ¼ γ 0 þ γ 1 RNOAt −γ2 ΔATt þ υtþ1 : Our hypothesis concerning the contributing role of accounting distortions (i.e., H1) implies a negative coefficient on accruals capturing accounting distortions. Results support this prediction. The coefficient on accruals capturing accounting distortions is highly negative and significant in all European countries except Austria. In Ireland the coefficient is highly negative, but insignificantly different from zero. The country-average coefficient on accruals in Europe is equal to − 0.075, while when all European countries are considered together is equal to −0.073. The respective coefficient for the U.S. reported by Richardson

Panel B: RNOAt + 1 = γ0 + γ1RNOAt − γ2ΔATt + υt + 1 Country

γ0

γ1

γ2

Adj. R2

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

0.006 0.033*** 0.029*** 0.027*** 0.031*** 0.003 0.022*** 0.032** 0.009** 0.059*** 0.041*** 0.008** 0.024*** 0.021 0.026*** 0.025*** 0.025***

0.645*** 0.68*** 0.677*** 0.744*** 0.665*** 0.623*** 0.706*** 0.826*** 0.747*** 0.651*** 0.524*** 0.737*** 0.741*** 0.674*** 0.751*** 0.693*** 0.672***

0.021 −0.057*** −0.059** −0.175*** −0.103*** −0.059*** −0.07*** −0.141 −0.041*** −0.095*** −0.059** −0.042*** −0.079*** −0.057*** −0.116*** −0.075*** −0.073***

0.47 0.481 0.519 0.547 0.525 0.449 0.581 0.654 0.616 0.574 0.351 0.582 0.557 0.505 0.591 0.533*** 0.524

Note: The sample consists of 40,539 firm-year observations over the period 1988–2009. All variables are defined in Table 1. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

et al. (2006) is equal to − 0.117. Thus, as in the U.S., the lower persistence of the accrual component of earnings in Europe is at least partially attributable to distortions arising from accrual accounting. In Table 5, we report time-series averages of the change between one-year ahead and current operating profitability (i.e., difference between one-year ahead and current operating profitability) for country-specific portfolios, country-average portfolios and portfolios when countries are considered all-together. Portfolio formation is similar to that described in the discussion of the results that appear in Table 3. The ranking variables are accruals and accruals capturing accounting distortions. We concentrate on the results for the extreme quintile portfolios. In doing so, we examine the economic significance of the contributing role of accounting distortions on the negative relation of accruals with future earnings. Panel A presents the results for portfolios formed on the magnitude of accruals. Firms within the highest accrual portfolio, experience in the great majority of the countries under consideration (Austria and Germany are the exceptions), decreases in their future accounting rate of return. The consequence from taking a long position on the lowest accrual portfolio and a short position on the highest accrual portfolio in each country is a group of firms that experience significant increases in one-year ahead operating profitability. The country-average spread of the change in future operating profitability between firms in the highest quintile and firms in the lowest quintile is highly positive and

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Table 5 Mean values of changes in operating profitability for extreme portfolios of firm-years formed on quintile rankings of accruals and accruals capturing accounting distortions. Panel A: Mean values of changes in operating profitability within extreme portfolios on ACC Country

Low

High

Spread (L–H)

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.013 0.051*** 0.043* 0.067 0.018* 0.039** 0.009 0.095 0.009 −0.005 0.044*** 0.003 0.021* 0.047* 0.041*** 0.031*** 0.029***

−0.071 −0.097*** −0.051*** −0.093*** −0.134*** −0.035 −0.098*** −0.109*** −0.047*** −0.184*** −0.087*** −0.029*** −0.058*** −0.018 −0.059*** −0.078*** −0.081***

0.058* 0.148*** 0.094*** 0.16*** 0.152*** 0.074** 0.107*** 0.204*** 0.056*** 0.179*** 0.131*** 0.032*** 0.079*** 0.065** 0.1*** 0.109*** 0.11***

Table 6 Regressions of future abnormal returns on current operating profitability, accruals and accruals capturing accounting distortions. Panel A: ARETt + 1 = γ0 + γ1RNOAt + γ2ACCt + υt + 1 Country

γ0

γ1

γ2

Adj. R2

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.019 0.005 0.015 0.021* −0.002 −0.008 −0.034 −0.034 −0.014 −0.006 −0.016 −0.026 0.004 −0.012 −0.006 −0.009 −0.006

0.111 −0.017 −0.056 −0.231 −0.005 0.019 −0.011 0.217* 0.131* 0.083** −0.028 0.112 0.04 0.062 −0.0098 0.028* 0.022

−0.017 −0.07* −0.16*** −0.052 −0.087*** −0.055*** −0.011 −0.06 −0.092*** −0.087*** −0.036 −0.084 −0.07** −0.06** −0.104*** −0.07*** −0.072***

0.002 0.031 0.019 0.013 0.019 0.019 0.007 0.026 0.018 0.02 0.003 0.002 0.022 0.015 0.017 0.016 0.011

Panel B: ARETt + 1 = γ0 + γ1RNOAt − γ2ΔATt + υt + 1 Panel B: Mean values of changes in operating profitability within extreme portfolios on ΔAT Country

Low

High

Spread (H–L)

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.057 −0.066*** −0.03** −0.071*** −0.116*** −0.017 −0.071*** −0.071 −0.034*** −0.167*** −0.079** −0.028** −0.053*** −0.019 −0.047*** −0.062*** −0.064***

−0.011 0.025 0.028 0.05 0.013 0.035** 0.006 0.081 −0.005 −0.001 0.026 0.001 0.018 0.065** 0.039*** 0.025*** 0.022***

0.046 0.091*** 0.058** 0.121*** 0.129*** 0.052* 0.077*** 0.152** 0.029*** 0.166*** 0.105** 0.029** 0.071*** 0.084*** 0.086*** 0.087*** 0.086***

Note: The sample consists of 40,539 firm-year observations over the period 1988–2009. All variables are defined in Table 1. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

equals about 11%. We find the same spread, when all countries are pooled together. Panel B presents results for portfolios formed on the magnitude of accruals capturing accounting distortions. Firms within the bottom portfolio, that are more likely to have high accruals due to accounting distortions, experience a significant drop on their future earnings performance. This result applies to all European countries except Austria and Ireland. Within all countries under investigation except Austria, taking a long (short) position on the top (bottom) quintile results in groups of firms with a rising operating profitability in the future. The country-average difference in one-year ahead accounting rate of return between firms in the lowest quintile and firms in the highest quintile is highly positive and equals about 8.7%. A similar difference is found, when all countries are considered together. In sum, the evidence in Tables 3 and 4 is suggestive of an important role for accounting distortions in explaining the negative relation of accruals with future earnings performance.

Country

γ0

γ1

γ2

Adj. R2

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.031** −0.008 0.012 0.011 −0.011 −0.018* −0.044 −0.042 −0.018* −0.007 −0.025** −0.021 0.002 −0.011 −0.006 −0.014*** −0.014**

0.175* −0.03 −0.131 −0.259* −0.032 0.011 −0.003 0.193 0.107 0.062* −0.037 0.073 −0.006 0.052 −0.036 0.009 −4E−04

−0.029 −0.019 −0.11*** −0.074* −0.068*** −0.039*** 0.027 −0.032 −0.014 −0.083*** −0.008 −0.027 −0.057* −0.063* −0.105** 0.047*** 0.047***

0.001 0.008 0.009 0.008 0.01 0.008 0.001 0.025 0.015 0.019 0.001 0.001 0.014 0.015 0.019 0.01 0.005

Note: The sample consists of 40,539 firm-year observations over the period 1988–2009. All variables are defined in Table 1. ***, ** and * represent statistical significance at 1%, 5%, and 10% levels, respectively, two-tailed.

capturing accounting distortions, after controlling for current operating profitability. We estimate annual cross-sectional regressions over the period 1988–2009 and report the time-series averages of the parameter coefficients and adjusted R2. Panel A presents estimation results for the following regression: ARETtþ1 ¼ γ0 þ γ1 RNOAt þ γ2 ACCt þ υtþ1 : The presence of the negative relation between accruals and future returns is suggestive of a negative coefficient on accruals. We find that the coefficient on accruals is highly negative and significant in 9 out 15 stock markets under investigation. Taken together with prior findings, the evidence suggests that out of 14 countries with a negative impact on future earnings performance, in 9 countries this impact can be extended in future stock performance. Those are: Belgium, Denmark, France, Germany, Italy, Netherlands, Spain, Sweden and Switzerland. Thus, generally firms with high current accruals underperform those with low current accruals in terms of operating profitability in the future, but not necessarily in terms of future returns. Panel B presents estimation results for the following regression:

4.3. Implications of accounting distortions on the relation of accruals with future returns

ARETtþ1 ¼ γ0 þ γ1 RNOAt −γ 2 ΔATt þ υtþ1 :

In Table 6 we report results from the Fama and MacBeth (1973) regressions of one-year ahead abnormal returns on accruals and accruals

According to our hypothesis (i.e., H1), the component of accruals capturing accounting distortions should also have a negative sign, if

G.A. Papanastasopoulos, E. Tsiritakis / International Review of Financial Analysis 41 (2015) 176–185

accounting distortions matter on the negative relation of accruals with future returns. Out of 9 equity markets where we find supporting evidence for occurrence of the negative relation between accruals and future returns, the coefficient on accruals attributable to accounting distortions is highly negative and significant in 7 equity markets. Those are: France, Denmark, Germany, Netherlands, Spain, Sweden and Switzerland. Thus, within the above countries the pricing of the accrual component of earnings is at least attributable to accounting distortions arising from accrual accounting. Further, within the above countries the average coefficient on accruals is equal to −0.087, while the average coefficient on accruals capturing accounting distortions is equal to −0.076. At the same time, in all the European countries under investigation the coefficients on accruals and accruals attributable to accounting distortions are around −0.07 and −0.047, respectively. The coefficient on accruals capturing distortions is negative, but not significant in Belgium and Italy. Thus, accounting distortions do not contribute to the accrual effect on stock returns in Belgium and Italy. At the same time, in Finland accruals attributable to accounting distortions are negatively related with future returns, but total accruals are not. In Table 7, we provide an economic summary of the contributing role of accounting distortions on the accrual–return relation by reporting time-series averages of abnormal returns for portfolios formed on the magnitude of accruals and accruals capturing accounting distortions.6 Details on the formation of portfolios can be found above in the discussion of the results that appear in Table 1. Abnormal returns for the top portfolio formed on accruals and the bottom portfolio on accruals attributable to accounting distortions are highly negative and significant. In countries with evidence supporting the negative relation of accruals with future returns the bottom accrual portfolio outperforms on average the top accrual portfolio by a significant 7.4% in the following year, while within all the European countries the respective outperformance falls to 5.9%. At the same time, in countries with evidence supporting the contributing role of accounting distortions on the accrual–return relation, the average spread in abnormal returns between extreme portfolios formed on the magnitude of accruals capturing accounting distortions is significantly positive at 6.2% per year. The respective spread averaged across all countries included in our tests is significantly positive at 3.50% per year. Overall, our findings in Tables 5 and 6 suggest an important role for accounting distortions in explaining the negative relation of accruals with future returns. Thus, the evidence presented up to this point is supportive of our first hypothesis concerning the contributing role of accounting distortions on the accrual anomaly in Europe. 4.4. Trust and implications of accounting distortions on the pricing of the accrual component of earnings In this section, we examine whether societal trust could explain the implications of accounting distortions on the pricing of the accrual component on earnings at the country level. We evaluate the implications of accounting distortions at the country level based on two measures labeled as SLOPE and SPREAD, respectively. SLOPE for each country is given by negative one times the time-series average of the country-specific coefficients obtained from the Fama and MacBeth (1973) regressions of one-year ahead abnormal returns on accruals capturing accounting distortions, after controlling for current operating profitability (see Panel B in Table 6). A positive value of SLOPE indicates that accounting distortions contribute to the negative relation between accruals and stock returns. SPREAD for each country is the time-series average of the annual return differences in abnormal returns between the highest and the lowest country-specific portfolios formed on the 6 Whether and why investors can benefit from exploiting accrual based trading strategies is an important aspect of the accrual anomaly (see Richardson et al., 2005; Strydom, Skully, & Veeraraghavan, 2014).

183

Table 7 Mean values of abnormal returns for extreme portfolios of firm-years formed on quintile rankings of accruals and accruals capturing accounting distortions. Panel A: Mean values of abnormal returns of within extreme portfolios on ACC Country

Low

High

Spread (L–H)

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.027 0.028* 0.029 0.002 0.002 −0.018 0.005 0.001 0.007 0.021 −0.026 −0.012 0.017 −0.018 0.006 0.001 0.001

−0.024 −0.057** −0.081*** −0.052* −0.101*** −0.093*** −0.027 −0.021 −0.044*** −0.042** −0.086*** −0.037 −0.041** −0.091*** −0.075*** −0.058*** −0.074***

−0.003 0.084*** 0.11*** 0.054 0.103*** 0.075*** 0.032 0.022 0.051* 0.063** 0.06** 0.025 0.058** 0.073** 0.081*** 0.059*** 0.075***

Panel B: Mean values of abnormal returns of within extreme portfolios on ΔAT Country

Low

High

Spread (H–L)

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland Country-average All countries

−0.011 −0.022 −0.082*** −0.032 −0.074*** −0.066*** −0.04 −0.023 −0.037*** −0.044*** −0.053** −0.043 −0.046*** −0.072*** −0.057*** −0.047*** −0.055***

−0.001 −0.014 0.02 0.006 −0.018 −0.026 −0.056 −0.022 −0.022** 0.026* −0.03 −0.026 −0.001 −0.014 0.004 −0.012* −0.017*

0.01 0.008 0.102*** 0.038 0.056*** 0.04** −0.016 0.001 0.015 0.07*** 0.023 0.017 0.045** 0.058* 0.061* 0.035*** 0.038***

Note: The sample consists of 40,539 firm-year observations over the period 1988–2009. All variables are defined in Table 1. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

magnitude of accruals capturing accounting distortions (see Panel B of Table 7). A positive value of SLOPE indicates that accounting distortions contribute to the negative relation between accruals and stock returns. As mentioned before, to evaluate trust at the country level we use a measure of generalized trust (TRUST, hereafter) and a measure of secrecy (SECRECY, hereafter). Our cross-country analysis has a panel data structure with timeinvariant country-level variables. In doing so, we investigate the between-countries power of trust in explaining accounting distortions as a contributing factor of the accrual–return relation. However, one could argue that a possible limitation of our analysis could be the lack of a time-dimension in our country-level variables. In this line, we echo the claim of Watanabe et al. (2013) among others that in an international setting, time-invariant variables have more explanatory power on asset pricing regularities relative to time-variant variables. We need to stress that according to Bjornskov (2006) the measure of generalized trust used in our tests is remarkably stable over time. In a similar vein, Tang and Koveos (2008) claim that cultural indices provide information about a country’s position relative to other countries, which changes scarcely. Note that the measure of secrecy measure used in our tests is based on such cultural indices. In Table 8, we report regressions of SLOPE and SPREAD on TRUST. Regressions are estimated by ordinary least squares (OLS) and the respective standard errors are reported in italics. Panel A presents results

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Table 8 Cross-country analysis: regressions of abnormal returns attributable to accruals capturing accounting distortions on trust. Panel A: SLOPEc = γ0 + γ1TRUSTc + υc

Table 9 Cross-country analysis: regressions of abnormal returns attributable to accruals capturing accounting distortions on secrecy. Panel A: SLOPEc = γ0 + γ1SECRECYc + υc

Coefficients

γ0

γ1

Coefficients

γ0

γ1

Value of coefficients

0.005 0.234

0.001* 1.942

Value of coefficients

−0.064*** −5.617

−0.0003** −2.299

Adj. R2 Obs.

Adj. R2 Obs.

0.165 15

Panel B: SPREADc = γ0 + γ1TRUSTc + υc

0.234 15

Panel B: SPREADc = γ0 + γ1SECRECYc + υc

Coefficients

γ0

γ1

Coefficients

γ0

γ1

Value of coefficients

−0.006 −0.355

0.001** 2.565

Value of coefficients

0.049*** 5.228

−0.0003** −2.249

Adj. R2 Obs.

0.285 15

Adj. R2 Obs.

0.225 15

Note: SLOPE for each country is given by negative one times the time-series average of the country-specific coefficients obtained from the Fama and MacBeth (1973) regressions of one-year ahead abnormal returns on accruals capturing accounting distortions, after controlling for current operating profitability (see Panel B in Table 6). SPREAD for each country is the time-series average annual return differences in abnormal returns between the highest and the lowest country-specific portfolio formed on the magnitude of accruals capturing accounting distortions (see Panel B of Table 7). Data on TRUST and SECRECY are provided in Table 2. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

Note: SLOPE for each country is given by negative one times the time-series average of the country-specific coefficients obtained from the Fama and MacBeth (1973) regressions of one-year ahead abnormal returns on accruals capturing accounting distortions, after controlling for current operating profitability (see Panel B in Table 6). SPREAD for each country is the time-series average annual return differences in abnormal returns between the highest and the lowest country-specific portfolio formed on the magnitude of accruals capturing accounting distortions (see Panel B of Table 7). Data on TRUST and SECRECY are provided in Table 2. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

for SLOPE, while Panel B for SPREAD. The respective regression models are:

portfolio formation procedure is as follows. First, countries are classified in three-equally weighted groups (i.e., terciles) based on the level of TRUST and SECRECY: the bottom group, the medium group and the top group. Then, within each of these groups, we report countryaverages of the spread in abnormal returns between the highest and the lowest country-specific quintile-portfolio formed on the magnitude of accruals capturing accounting distortions, by putting an equal weight on each country-specific portfolio (see Panel B of Table 7). Resulted tstatistics in italics are based on the variation of country-specific abnormal returns. Panel A presents the results for the group of countries

SLOPEc ¼ γ0 þ γ1 TRUSTc þ υc SPREADc ¼ γ 0 þ γ 1 TRUSTc þ υc : Results from both models indicate that trust has strong power in explaining the implication of accounting distortions on the pricing of the accrual component of earnings. The estimated coefficient on TRUST in both models is equal to 0.001. The trust coefficient in the SLOPE model has a t-statistic of 1.942, while the SPREAD model has a t-statistic of 2.565. Thus, both coefficients are significant at less than the 10% level based on a two-tailed test. The adjusted R2 for the SLOPE model is 0.165, while for the SPREAD model it rises to 0.285. In Table 9, we report regressions of SLOPE and SPREAD on SECRECY. Regressions are estimated by ordinary least squares (OLS) and the respective standard errors are reported in italics. Panel A presents the results for SLOPE, while Panel B for SPREAD. The respective regression models are:

Table 10 Cross-country analysis: country-mean values of the spread in abnormal returns between extreme portfolios formed on accruals capturing accounting distortions, conditional on trust and secrecy. Panel A: Mean values of the spread in abnormal returns between extreme portfolios on ΔAT Group of countries based on the level of TRUST

Spread (L–H)

Bottom group

0.016 1.379 0.031** 2.799 0.058*** 4.276

SLOPEc ¼ γ0 þ γ1 SECRECYc þ υc

Medium group

SPREADc ¼ γ 0 þ γ 1 SECRECYc þ υc :

Top group

We find that secrecy bears explanatory power on the impact of accounting distortions as a driving force behind the negative relation of accruals with future stock returns. The estimation coefficient of secrecy in the SLOPE model is equal to −0.0003 with a t-statistic of −2.299, and in the SPREAD model it is equal to −0.0003 with a t-statistic of −2.249. Thus, both coefficients are significant at the 5% level based on two-tailed test. The adjusted R2 for the SLOPE model is 0.234, while for the SPREAD model falls to 0.225. In sum the evidence presented in Tables 8 and 9 clearly suggests that the implications of accounting distortions on the accrual–return relation are magnified and become stronger in countries with a higher level of trust and a higher level of secrecy. In Table 10, we report country-averages of the spread in one-year ahead abnormal returns between extreme portfolios formed on accruals capturing accounting distortions conditional, on trust and secrecy. The

Panel B: Mean values of the spread in abnormal returns between extreme portfolios on ΔAT Group of countries based on the level of SECRECY

Spread (L–H)

Bottom group

0.051** 2.863 0.033** 3.546 0.022 1.699

Medium group Top group

Note: Within each group of countries based on the level of TRUST and SECRECY, the value of the spread (L–H) represents the country-average of the spread in abnormal returns between the highest and the lowest country-specific quintile-portfolio formed on the magnitude of accruals capturing accounting distortions by putting an equal weight on each country-specific portfolio (see Panel B of Table 7). Data on TRUST and SECRECY are provided in Table 2. ***, ** and * represent statistical significance at the 1%, 5%, and 10% levels, respectively, two-tailed.

G.A. Papanastasopoulos, E. Tsiritakis / International Review of Financial Analysis 41 (2015) 176–185

based on TRUST, while Panel B for the group of countries based on SECRECY. The evidence is entirely in accordance with our findings from the cross-country regression analysis. In countries with the lowest level of TRUST and the highest level of SECRECY, the country-average spread in returns between extreme portfolios formed on the magnitude of accruals capturing accounting distortions is attenuated and statistically insignificant. Once the magnitude TRUST (SECRECY) increases (decreases) the spread in returns rises and becomes more significant at conventional levels. 5. Conclusion Sloan (1996) documents the negative relation of accruals with future earnings and future returns. Following Sloan (1996), an extensive body of research shows that this prominent empirical regularity is robust to various samples in the U.S. capital market and that it also exists in international equity markets. However, an active debated issue in the literature is what drives the accrual anomaly. A wellaccepted explanation goes beyond the properties of accounting accruals. The negative relation between accruals and future earnings performance is attributable to distortions arising from accrual accounting, but investors ignore them in security valuation. The primary objective of our paper is to examine the validity of the explanation associated with accounting distortions in an international setting. In doing so, we focus on European equity markets. We show that accounting distortions constitute a very compelling explanation for the negative relation between accruals and future earnings performance. Across the 15 equity markets that we examined, accounting distortions constitute a contributing factor in 14 equity markets. Further, we show that the negative relation between accruals and stock returns could be at least attributable to distortions arising from accrual accounting. Accounting distortions predict returns in 7 out of the 9 markets where the accrual anomaly occurs in Europe. Finally, we show that the impact of accounting distortions on the pricing of the accrual component of earnings is stronger in markets with a higher level of trust and a lower level of secrecy. A limitation of our study is that we do not focus on other explanations that have been proposed by the literature for the accrual anomaly. Importantly, the driving factors under some of these explanations could be not mutually exclusive and coexist with accounting distortions (Chan et al., 2006; Shi & Zhang, 2012). Indeed, Richardson et al. (2006) show that diminishing marginal returns from investment growth could have a supplementary contributing role on the lower persistence of accruals. We believe that the interplay between Richardson et al. (2006) and our study are highly suggestive of certain questions for future research. Does a growth-related factor contribute to the negative relation of accruals with future profitability outside of the U.S.? How do investors price the implications of investment growth recorded in accounting accruals in international stock markets? References Aggarwal, R., & Goodell, J. (2009). Markets and institutions in financial intermediation: National characteristics as determinants. Journal of Banking & Finance, 33, 1770–1780. Aggarwal, R., & Goodell, J. (2010). Financial markets versus institutions in European countries: Influence of culture and other national characteristics. International Business Review, 19, 502–520. Aggarwal, R., & Goodell, J. (2014). National cultural dimensions in finance and accounting scholarship: An important gap in the literatures? Journal of Behavioral and Experimental Finance, 1, 1–12. Aggarwal, R., Kearney, C., & Lucey, B. (2012). Gravity and culture in foreign portfolio investment. Journal of Banking & Finance, 36, 525–538.

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