The Effect of Managerial Ability on Earnings Quality in the Pre and Post IFRS Adoption Periods

The Effect of Managerial Ability on Earnings Quality in the Pre and Post IFRS Adoption Periods Weitzu Chen, Department of Accountancy, National Taipei University, Taiwan. E-mail: wtzchen@mail.ntpu.edu.tw Chia-Wei Tai, Department of Accountancy, National Taipei University, Taiwan. E-mail: evgueniaestes@gmail.com Abstract This study examines the effect of managerial ability on providing financial information. Generally, compared with ones with inferior ability in making judgments and estimates, managers with superior ability provide informative reports with earnings of higher quality to investors due to their better knowledge of firms conditions and prospects. Managers with superior ability have no need to manipulate earnings through discretionary accruals. In addition, due to many accounting treatments in International Financial Reporting Standards (IFRS) based on fair value measurements, the purpose of fair value accounting is to increase the information transparency for investors to make a better decision. Based on aforementioned assumptions of managerial ability and fair value accounting, this study investigates the effect of managerial ability on the preparation of financial information in the pre- and post-ifrs adoption periods. For this study, we extract data from Global Compustat to examine that the effect of managerial ability in the UK listed firms on firm's earnings quality in the pre- and post- IFRS adoption periods. We focus on the UK listed firms because the UK has the largest capital market in the European Union (EU). Based on our evidence, we find that earnings quality is negatively associated with managerial ability in the pre-ifrs adoption period. In addition, managers of superior ability are associated with better earnings quality in the post-ifrs adaption period. Compared empirical findings between the pre- and post-ifrs adoption periods, our results imply that managerial ability in making judgments and estimates affects earnings quality primarily due to fair value accounting in the IFRS. Overall, fair value accounting in the post-ifrs adoption period can mitigate managerial ability in manipulating earnings. Keywords: Managerial ability; fair value; International Financial Reporting Standards (IFRS). JEL Classification: M41, N24 1

1. Introduction There are many sources of firm value. Among others, managerial skill is the most important factor in the human capital of a firm. Previous research (Demerjian, Lev, and McVay 2012) has shown that the characteristics of managers including ability, talent, reputation, and style can affect economic outcomes. Therefore, managerial abilities are important for research and practical applications in the fields of economics, finance, accounting, and management. More capable managers have stronger professional knowledge related to the firm, enabling them to make better judgments and decisions. In addition, more capable managers show a more flexible understanding and application of accounting standards. Therefore, the financial reporting of managers to investors varies according to the intentions and choices of managers. Although it is obviously difficult to capture how the intentions and choices of managers are used to manipulate the quality of financial reporting, when compared to less capable managers, more capable managers show better understanding of the industry and the overall economic environment, as well as the ability to apply accounting standards more flexibly, and therefore have a significant influence on the quality of information. Better earnings quality can be used to appropriately reflect the firm s current operating performance through financial reports. Furthermore, it can enable better decision-making on the distribution of earnings from the perspective of investors (Dechow et al. 2010). More capable managers are able to better allocate resources, and create a positive impact on the quality of financial reporting. Related research on the quality of financial reporting including earnings forecasts also provides strong evidence for the impact of the ability of managers on financial reporting. Trueman (1986) points out ability of managers to anticipate future changes in the firm s economic environment and to adjust the firm s production plan accordingly is valuable for investors because managers with stock incentive compensation have incentives to voluntarily release earnings forecasts as signals to markets. Therefore, compared to firms that do not release earnings forecasts, firms that release earnings forecasts have higher firm value. That is, earnings forecasts include useful signals about the ability of managers, increasing the value of earnings information in forecasts. The ability of managers inevitably affects the accuracy and relevance of financial reporting. Therefore, more capable managers have a better understanding of a firm s overall economic environment and future development. In addition, managers play a key role in the distribution of firm resources, and therefore also influence the development of firms as well. 2

In addition, managerial ability has a significant influence on the accuracy and relevance of financial reporting (Baik, Farber, and Lee 2011). However, it is not easy to observe the ability of managers. Therefore, this study adopts the data envelopment analysis (DEA) method developed by Demerjian, Lev, and McVay (2012) to estimate managerial ability. This study uses DEA to estimate firm efficiency, and then uses a regression model to estimate the portion of company efficiency that is attributable to the efficiency of managers. This measure is used as a proxy for the ability of managers. In addition to influence of managerial ability on financial reporting, many studies related to accounting and management research investigate whether managers use accounting methods and discretionary power to influence earnings reports, so called earnings management. At the same time, the preparation of financial reports is regulated by accounting standards. Currently, there is a global trend toward the adoption of the International Financial Reporting Standards (IFRS). Different countries in the world have gradually moved from their local standards to fully adopted or incorporated IFRS. The European Union (EU) fully adopted IFRS in 2005. Listed companies in Taiwan started full adoption of IFRS in 2013. 1 The main aims of IFRS are to enhance earnings quality and to achieve a high level of comparability in financial statements (Regulation (EC) No 1606/2002 of the European Parliament and of the Council). Higher quality financial statements enable the users of these statements to make better decisions. Following the global adoption of IFRS, there has been widespread discussion on whether this move has delivered the expected increase in financial reporting quality. However the consensus has not yet been reached. Barth, Landsman, and Lang (2008) and Chen, Tang, Jiang, and Lin. (2010) find that the adoption of IFRS can improve the quality of financial reporting. However, Jeanjean and Stolowy (2008) and Ahmed, Neel, and Wang (2013) find deterioration in the quality of earnings reporting following the adoption of IFRS. 2 The contribution of this study is to investigate the influence of managerial ability on earnings quality following the adoption of IFRS. This study covers the period from before- to 1 Before Taiwan formally adopted IFRS in 2013, national financial reporting standards had already begun to make reference to IFRS in 2001. 2 Jeanjean and Stolowy (2008) use data from Australia, France, and the United Kingdom to test whether there was an improvement in earnings quality after the adoption of IFRS. Ahmed, Neel, and Wang (2010) look at 20 countries that fully adopted IFRS in 2005 as the experimental group: Greece, Italy, the Philippines, Portugal, South Africa, Spain, Australia, Austria, Belgium, Denmark, Finland, France, Germany, Hong Kong, Ireland, the Netherlands, Norway, Sweden, Switzerland, and the United Kingdom. The experimental group was paired with a reference group of 15 countries that did not fully adopt IFRS in 2005: Argentina, Brazil, Chile, India, Israel, South Korea, Malaysia, Mexico, Pakistan, Taiwan, Thailand, Canada, Japan, New Zealand, and the United States. The authors use these two groups to compare and test earnings quality. 3

after-adoption of IFRS. Therefore, we can explore the role of managerial ability in the weakening of reliability of information due to bias in fair value measurements between before- and after-adoption periods of IFRS. This study provides strong evidence with regard to the inconsistent findings in the existing literature on financial reporting quality for the before- and after-adoption periods of IFRS, and shows the importance of management by examining the effect of managerial ability on the relationship between IFRS and earnings quality. This study is divided into five sections. The next section reviews the relevant literature and outlines the research hypotheses. The third section details the research design, including sample selection, data sources, and the empirical model. The fourth section contains the empirical analysis. The fifth section presents the research findings and recommendations. 2. Literature Review 2.1 Managerial Ability and Earnings Quality Earnings quality is a very important factor in the effective allocation of resources. The reason for this is that earnings information is a critical item in the decision-making models of investors. Therefore, firms with poor earnings quality may have a higher cost of equity (Francis et al. 2004), and be more likely to receive attention from securities supervisory bodies. Why do managers carry out earnings management by using discretionary accruals to alter reported earnings figures? Subramanyam (1996) and Kasanen, Kinnunen, and Niskanen (1996) consider why managers manipulate discretionary accruals, identifying two important issues. First, how do users of financial statements think that discretionary accruals should be interpreted? This is important because the discretionary accruals will increase or decrease the information value of earnings reporting. Second, legislators also hope that the design and formulation of guidelines can reduce the ability of managers to opportunistically manipulate financial reports (Bernard and Skinner, 1996). In research on managerial ability and earnings management, Libby and Luft (1993) find that since more competent managers have a better understanding of a firm s economic situation, they can produce higher quality financial reporting information. Research by Demerjian et al. (2013) find that a correlation between higher managerial ability and lower frequencies of subsequent earnings restatements, higher earning and accrual persistence, lower errors and bad debt provisions, and better ability to estimate accruals. In addition, with regard to earnings management motivation, some scholars find that more capable managers 4

are less likely to release earnings forecasts for opportunistic reasons (Francis et al. 2008; Malmendier and Tate 2009). Because earnings forecasts are also a means to influence the market, we can find that more capable managers have less motivation for opportunistic behavior. This is related to reputation costs. Research by Jeanjean and Stolowy (2008) has pointed out that incentives for managers and national institutional factors play a key role in financial reporting quality. Therefore, if managers have a better understanding of the industry and environment together with discretion over accounting standards, the quality of financial reports will be affected by managerial manipulation of accounts. Although we cannot be certain of the process by which managers choose to interfere in financial reports by carrying out earnings management, this may come from opportunistic interference in reported earnings, or it may come from use of discretionary power to increase the information value of financial results (Watts and Zimmerman 1986; Healy and Palepu 1993; Bernard and Skinner 1996). 2.2 International Financial Reporting Standards and Earnings Quality The International Accounting Standards Committee (IASC) was established in 1973, and published International Accounting Standards in 1975. Subsequently, these standards have undergone major changes. In 2001, the International Accounting Standards Board (IASB) reached a peak in its operations. Beginning in 2000, the International Organization of Securities Commissions (IOSCO) recommended that its member countries allow dealers in cross-border securities to use IFRS to prepare financial statements when applying for the issue and listing of securities. IFRS aims to provide consistent, high-quality global accounting standards. Limiting accounting options and gradually adopting fair value ensure that the actual economic situation and performance of the firm are better reflected. The limitation of accounting options reduces opportunities for discretionary earnings for company managers (Ashbaugh and Pincus 2001), enhances financial reporting quality, and provides investors with better quality information for making investment decisions. However, there has been no consensus on whether the IFRS has actually produced financial reports with higher quality. Barth, Landsman, and Lang (2008) find that in twentyone countries that adopted IFRS, when compared to matched samples from non-us companies, accounting numbers from US companies show less earnings management, more timely loss recognition, and higher value relevance. Horton, Serafeim and Serafeim (2013) also reach a similar conclusion, finding that the imposition of IFRS can improve the quality of 5

information in the investment market. By upgrading the quality and comparability of information, firms can improve their information environment. In addition, other studies using different methods support this finding. For example, Ozkan, Singer, and You (2012) look at compensation committees, examining whether accounting information is useful for evaluation. Their research finds that after the EU mandated IFRS in 2005, compensation committees significantly increased the weighting of accounting information in executive compensation. This finding clearly shows that from the perspective of compensation committees, IFRS does indeed improve the quality of financial reports, thereby increasing its use in executive compensation. Conversely, other studies find that IFRS does not produce an improvement in financial reporting quality. Jeanjean and Stolowy (2008) look at three countries 3 implementing IFRS for the first time and find that earnings management in these three countries did not decline with the implementation of IFRS and in fact increased in France. Their study points out that sharing rules is not a sufficient condition to create a common business language and instead argue that it is managerial incentives and institutional factors rather than accounting standards alone to affect financial reporting quality. In summary, past studies generally agree that managers influence earnings quality, but they have no conclusion on what form this influence takes. In addition, managerial ability including understanding of the industry, economic environment, and accounting standards may influence earnings management behavior. At the same time, with the adoption of IFRS, managers have less opportunity for manipulating earnings under limited accounting choices. On this basis, this study argues that the use of IFRS reduces the influence of managerial ability on earnings management. Therefore, we propose the following hypothesis: Hypothesis 1: Firm's earnings quality increases with higher managerial ability in the post-ifrs adoption period. However, as Baik, Farber, and Lee (2011) find, managerial ability has an important effect on the relevance and accuracy of financial reports. This study expects that if managers have a positive ability in understanding firm s operations, decision-making, and financial situation, this will influence financial reporting information. Managers with this positive ability are more affected than those with negative ability by the adoption of IFRS, which uses fair value accounting. Therefore, our second hypothesis is as follows: 3 Australia, France, and the United Kingdom. 6

Hypothesis 2: Firms with higher managerial ability have greater earnings quality than those with lower managerial ability in the post-ifrs adoption period. 3. Methodology 3.1 Empirical Model First, this study uses the method proposed by Demerjian, Lev, and McVay (2012) to estimate managerial ability, using data envelopment analysis (DEA) to solve the following optimization problem: Sales max v v1 Cogs v2sg & A v3ppe v4opslease v5r & D v6goodwill v7otherint In this study, the only output is Sales. Inputs include the following: property, plant and equipment (PPE), net operating leases (OpsLease), net R&D expenditure (R&D), cost of goods sold (Cogs), goodwill (Goodwill), other intangible assets (OtherInt), and selling, general and administrative expense (SG&A). Total firm efficiency is estimated using a single output and seven inputs. Therefore, we can obtain the firm s efficiency value from Equation (1). The firm efficiency value includes efficiency motivations specific to both firms and managers. Therefore, the regression model in this study includes four firm specific factors. These four firm specific factors represent firm-specific efficiency motivation, while the residuals are efficiency motivation for managers, meaning managerial ability. The regression model is as follows: FirmEfficiency 4 α β ln 1 (TotalAssets) β2marketshare β3fcfi β FCI (2) YearIndicator IndustryIndicator ε Where Total Assets is the total assets of the company. Larger firms have higher market share, and have greater negotiating capacity than smaller firms. This is one of the firm specific efficiency motivations. Market Share refers to firm market share. Firms with higher market share have operational advantages over firms with smaller market share. FCFI is the free cash flow indicator. When firms have higher free cash flow, this indicates that they are able to more effectively carry out positive net present value investments. FCI is the foreign currency indicator, capturing the diversification of the company. This indicator is based on whether firms have foreign currency exchange item. When companies are more diversified, managers face greater resource allocation challenges, and need to bring together a broader range of knowledge. The model also controls for the year and the industry. (1) 7

For earnings quality, this study uses discretionary accruals (e.g. DeAngelo 1986; Jones 1991; Dechow et al. 1995) as the substitute variable for earnings quality, and applies the model proposed by Kothari, Leone, and Wasley (2005) for estimation: TA A ijt ijt 1 REV REC PPE ( ROA ( 3) ijt ijt ijt 0 1 ) 2( ) 3( ) Aijt 1 Aijt 1 Aijt 1 Where TA ijt is the total accruals for company i in industry j at period t, A ijt-1 is the total assets for company i in industry j at the end of period t, ΔREV ijt is the net change in operating revenue for company i in industry j at the end of period t, ΔREC ijt is the change in accounts receivable for company i in industry j at the end of period t, PPE ijt is the gross property, plant and equipment for company i in industry j at the end of period t, ROA ijt is the return on assets for company i in industry j at the end of period t, ε ijt is the error term for company i in industry j at the end of period t. The regression in Equation (3) obtains the parameter estimates through ordinary least squares, obtaining the non-discretionary accruals NDA ijt, and subtracts NDA ijt from TA ijt to obtain the earnings quality variable DA ijt. This is the discretionary accrual. As firms have different earnings management intentions, there is a possibility that earnings will be manipulated both upwards and downwards. This study seeks to capture whether or not a firm has engaged in earnings management behavior, and therefore sets DA ijt as an absolute value. This study sets the regression model below to test Hypothesis 1: 4 ijt ijt DA 0 1Ability 2Post 3Ability Post 4DEBT 5ABS _ TA OUTS LOSS SIZE CFO 6 7 8 9 (4) Where DA is the absolute value discretionary accruals, Ability is managerial ability, extracting residuals from equation 2. However, Demerjian et al. (2013), in order to avoid the effects of extreme values, used the deciles value of managerial ability as a substitute variable to examine the relationship between managerial ability and earnings restatements. 4 Post is a dummy variable, set to 1 when IFRS is adopted, otherwise to 0. DEBT is the interest-bearing debt ratio. Beneish and Press (1993) and Watts and Zimmerman (1990) point out debt covenants as a potential motivation for earnings management. Defond and Jiambalvo (1994) also point out the relationship between debt covenants and the preferred accounting methods of managers. At the same time, in order to avoid violating debt covenants, managers will inflate the firm s earnings. ABS_TA is the absolute value of total accruals. Francis, Maydew, and Sparks (1999) believe that since it is difficult for external users of financial statements to distinguish between discretionary accruals and non-discretionary accruals, when the firm has 4 We use deciles value of managerial ability in the additional testing section. 8

the greater potential internal accruals, there is greater uncertainty in its profits. In addition, there is no perfect way to distinguish discretionary and non-discretionary accruals within the total accruals. This study adopts the approach of Beckrer et al. (1998), considering the absolute value of the total number of accruals on discretionary accruals. OUTS is a dummy variable, which equals one if outstanding change in shareholding exceeds 10%, and is otherwise zero. Teoh, Welch, and Wong (1998) find that when firms want a cash injection, they use a relatively good earnings situation to obtain better cost of capital, and therefore have a motivation to adjust discretionary accruals. LOSS is a dummy variable, which equals one if a firm has negative earnings in the current period and zero if it does not. Studies by Antle et al. (2006) and Frankel et al. (2002) find that when firms have negative earnings, they will manipulate discretionary accruals. SIZE is the size of the firm, using total assets to obtain a natural logarithm. Becker et al. (1998) believe that firm size represents a large number of missing variables. Therefore, including firm size can increase the testing ability of the model. CFO is cash flow from operating activities. Dechow et al. (1995) and Becker et al. (1998) find a negative correlation between operational cash flow and discretionary accruals. This study sets the regression model below to test Hypothesis 2: DA Ability _ H Post Ability _ H Post DEBT 0 ABS _ TA OUTS LOSS SIZE CFO 5 1 6 2 7 3 8 9 4 (5) Where Ability_H is the ability dummy variable, which equals one if a firm has negative earnings in the current period and zero if it does not. Ability_H*Post is the product term of the ability dummy variable and use of the IFRS dummy variable. The remaining variables are as described in Equation (4). 3.2 Sample Selection and Data Sources The study period was between 1998 and 2013, including the period from before- to afteradoption of IFRS in the United Kingdom. Since the sample group covers the period between before- and after-adoption of IFRS, we can effectively test the relationship between managerial ability and earnings quality before and after the adoption of IFRS. In this study, financial variables and variables related to earnings quality come from Compustat Global data for listed companies in the United Kingdom, which we use to calculate firm efficiency, managerial efficiency, and other data required in this study. We begin processing of sample data from the firm efficiency value. We follow the approach in Demerjian et al. (2012), using DEA to calculate firm efficiency of each firm. The original sample contained a total of 23,464 annual observations. After excluding 13,489 9

observations that did not cover calendar years, 60 observations where input and output items were negative, and 37 missing values, we are left with a total effective sample for firm efficiency of 9,878 observations, as shown in Table 1. After using DEA to calculate the annual efficiency value of each firm, we use Equation (2) to calculate managerial efficiency value, and matched with the information needed to calculate managerial ability. After deducting 117 observations with missing data, there were 9,761 valid samples to measure managerial efficiency. For calculation of discretionary accruals, we use Equation (3). After excluding noncalendar year data, missing values, and initial data required for the model, we end up with 5,626 observations, and matched these observations with the information needed to calculate the managerial ability efficiency value. Finally, we have 5,578 valid observations that can be used for hypothesis testing. 4. Results and Discussion 4.1 Data Description Table 2 provides a description of variables that are used in this paper. We put the sample into higher and lower ability and use t-test to compare two sample mean. All of variables are significant at the 5 percent level in a two-tailed test excepted absolute value of discretionary accruals and firm size. 4.2 Univariate Correlation (Insert Table 2 about here) To avoid problems of multicollinearity, we check each variable s variance inflation factor (Variance Inflation Factor, VIF) before the multiple regression analysis. Table 3 shows that the VIF values for each variable are less than 10, initially ruled out multicollinearity. (Insert Table 3 about here) Table 4 reports the Pearson and Spearman correlations of the variables. In the Pearson correlations, absolute value of discretionary accruals is positively and significantly related with ABS_TA, OUTS, and LOSS and negatively and significantly associated with SIZE and CFO. The results of correlations in Spearman are same with Pearson substantially. 4.3 Empirical Findings (Insert Table 4 about here) In hypotheses 1, we examine the effect of on managerial ability on earnings quality in the post-ifrs adoption period. Table 5 reports, whether or not including firm fixed effects, the coefficient on the interaction variable of ability with the Post, Ability Post, is significantly 10

negative (including the firm fixed effect=-0.0262, t=-2.12; excluding the firm fixed effect=- 0.0423, t=-3.77). In addition, we find that absolute value of discretionary accruals is significantly positive with Ability in the pre-ifrs adoption period (including the firm fixed effect=0.0198,t=1.70; excluding the firm fixed effect=0.0441, t=4.34)and significantly negative with Post (including the firm fixed effect=-0.0100, t=-3.62; excluding the firm fixed effect=-0.0046, t=-2.04). Compared above empirical findings between the pre- and post-ifrs adoption periods, the result implies the effect of managerial ability on earnings quality is affected by implementation of IFRS. We also observe in Table 5 that a number of control variables are as expected and significantly related to absolute value of discretionary accruals both in models of including and excluding firm fixed effect. Specifically, the coefficient on ABS_TA (0.6836, t=7.13; 0.6429, t=9.11), OUTS (0.0137, t=3.80; 0.0206, t=6.03) and LOSS (0.0103, t=2.77; 0.0273, t=8.73) are positive. In model of excluding firm fixed effect, DEBT (-0.0104, t=-1.86) and SIZE (-0.0032, t=-6.36) are significantly negative. (Insert Table 5 about here) In Table 6 we investigate the impact of higher managerial ability on earnings quality in the post-ifrs adoption periods and examine our hypothesis 2 by investigating that firms with higher managerial ability have greater earnings quality than those with lower managerial ability. In the first column of estimates, which control for firm fixed effects, the coefficients on Ability_H Post (=-0.0110, t=-1.92) and Post (=-0.0065, t=-2.14) are significantly negative and on Ability_H (=0.0103, t=2.01) are significantly positive. Those results imply impact of positive manager ability on earnings quality was improved by implementation of IFRS. In the second column of estimation, we find similar results when firm fixed effects are excluded. The coefficients on Ability_H Post (=-0.0192, t=-3.80) are significantly negative and on Ability_H (=0.0103, t=2.01) are significantly positive. Similarly, we fine the same results in control variables, including ABS_TA (=0.6842, t=7.12; 0.6437, t=9.09), OUTS (=0.0137, t=3.78; 0.0206, t=6.00) and LOSS (=0.0103, t=2.79; 0.0273, t=8.73), DEBT (=-0.0107, t=- 1.90) and SIZE (=-0.0031, t=-6.28). 4.4 Additional Test (Insert Table 6 about here) Following research of Demerjian et al. (2013), we create decile ranks of our earnings quality and managerial ability variables by year and industry. Table 7 shows that regardless of 11

whether the firm fixed effects are included or not, the result is consistent with the main empirical results. (Insert Table 7 about here) Furthermore, we exclude the data in year 2005 here. Ozkan, Singer, and You (2012) examine how the mandatory adoption of IFRS in Continental Europe affects the contractual usefulness of accounting information in executive compensation. Ozkan et al. (2012) think that this is the first year firms were required to report according to IFRS, and companies may have needed some time to adjust their compensation contracts after adopting IFRS. After excluding observations in year 2005, we have 5,200 firm-year observations. Table 8 and Table 9 have the same results as the main model substantially. (Insert Table 8 and 9 about here) 5. Conclusions and Recommendations Managers have been an important factor in the company s operating activities. Managers show their abilities in understanding for the prospect forecast, industry and economic environment, thereby providing the results of the company s operating activities through financial reports to investors. On the other hand, the preparations of the financial reports are governed and regulated by accounting standards. In particular, in the impact of implementation of IFRS, which the use of fair value to measure and have limited selection of accounting methods, the quality of financial reports will be affected. Because the use of fair value will increase errors of measure and limited accounting choices will mitigate opportunities to manipulate earnings, the relationship between the managerial ability and earnings quality, according to findings of this study, are affected by the implementation of IFRS. In this study, we gathered data from UK listed firms to investigate relationship between managerial ability and earnings quality. The results of this study show, when IFRS in place, discretionary accruals decrease with increasing managerial ability. It implies managers can exert their ability to minipulate earnings and be effectively reduced their opportunities to manipulate earnings in the post-ifrs adoption period, and thus enhance the quality of the overall financial reports. In addition, Compared better (positive) to poor (negative) managerial ability, we fine firms with higher managerial ability have greater earnings quality than those with lower managerial ability in the post-ifrs adoption periods. Because managers with higher ability better able to deal with issues related to IFRS and provide less 12

discretionary financial information. It implies the impact of higher (positive) managerial ability on quality of earnings was significantly improved after the implementation of IFRS. Overall, the managerial ability plays a key role in the financial reporting process and is affected by IFRS, which can effectively improve the overall quality of information. The adoption of discretionary accruals, there are possibilities of opportunism and enhancing the value of information. No matter what the managers intentions, the implementation of IFRS can effectively mitigate the exertion of managers to use discretionary accruals. The possible reasons for this are IFRS can reduce the managers' exertion and capability to speculatively use the discretionary accruals or effectively improve the quality of financial reporting by its norm (e.g., fair value measurement), thereby reducing managers use discretionary accruals necessary adjustments. The related reason is yet to be discussion. References Ahmed, A., M. Neel, and D. Wang. 2013. Does Mandatory Adoption of IFRS Improve Accounting Quality? Preliminary Evidence. Contemporary Accounting Research 30(4):1344-1372. Antle, R., E. Gordon, G. Narayanamoorthy and L. Zhou. 2006. The Joint Determination of Audit Fees, Non-Audit Fees, and Abnormal Accruals. Review of Quantitative Finance and Accounting 27 (3):235. Ashbaugh, H., and M. Pincus. 2001. Domestic Accounting Standards, International Accounting Standards, and the Predictability of Earnings. Journal of Accounting Research 39(3):417-434. Baik, B., David B. Farber, and Sam Lee. 2011. CEO Ability and Management Earnings Forecasts. Contemporary Accounting Research 28(5):1645-1668. Barth, M. E., W. R. Landsman, and M. H. Lang. 2008. International Accounting Standards and Accounting Quality. Journal of Accounting Research 46(3):467-498. Becker C. L., M. L. Defond, J. Jiambalvo, and K. R. Subramanyam. 1998. The Effect of Audit Quality on Earnings Management. Contemporary Accounting Research 15(1):1-24. Beneish, M. D. and E. Press. 1993. Costs of Technical Violation of Accounting-Based Debt Covenants. The Accounting Review 68(2):233-257. Bernard V. L. and D. J. Skinner. 1996. What Motivates Managers' Choice of Discretionary Accruals? Journal of Accounting and Economics 22(1-3):313. Chen, H., Q. Tang, Y. Jiang, and Z. Lin. 2010. The Role of International Financial Reporting Standards in Accounting Quality: Evidence from the European Union. Journal of International Financial Management and Accounting 21(3):220-278 DeAngelo, L E. 1986. Accounting Numbers as Market Valuation Substitutes: A Study of Management Buyouts of Public Stockholders. The Accounting Review 61 (3):400-420. Dechow, P. M., R. G. Sloan, and A. P. Sweeney. 1995. Detecting Earnings Management. The Accounting Review 70(2):193-225. 13

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Ozkan N., Z. Singer, and H. You. 2012. Mandatory IFRS Adoption and the Contractual Usefulness of Accounting Information in Executive Compensation. Journal of Accounting Research 50(4):1077-1107. Subramanyam, K. R. 1996. The Pricing of Discretionary Accruals. Journal of Accounting and Economics 22:249-282. Teoh, S. H., I. Welch, and T. J. Wong. 1998. Earnings Management and the Long-Run Market Performance of Initial Public Offerings. Journal of Finance 53(6):1935-1974. Trueman, B., 1986. Why Do Managers Voluntarily Release Earnings Forecasts? Journal of Accounting and Economics 8(1):53-71. Watts, R. L. and J. L. Zimmerman. 1986. Positive Accounting Theory. New Jersey: Prentice-Hall. Watts, R. L. and J. L. Zimmerman. 1990. Positive Accounting Theory: A Ten Year Perspective. The Accounting Review 65(1):131-156. Table 1: Number of observations (1998-2013) Year n of obs. Year n of obs. 1998 569 2006 745 1999 571 2007 718 2000 562 2008 646 2001 576 2009 602 2002 592 2010 590 2003 645 2011 566 2004 707 2012 532 2005 737 2013 520 Total 9,878 Table 2: Descriptive Statistics Variable Ability Mean Std. Dev. Min Max t-value DA DA Ability Post DEBT Year:2000-2013,n of obs.:5,578(ability value>=0:3450;<0:2128) Low -0.0048 0.1177-0.5367 0.5345 High 0.0070 0.1124-0.4698 0.5345 Low 0.0787 0.0876 0.0000 0.5367 High 0.0788 0.0804 0.0000 0.5345 Low -0.1522 0.0939-0.4950-0.0003 High 0.2600 0.2190 0.0001 0.8019 Low 0.6270 0.4837 0.0000 1.0000 High 0.7754 0.4174 0.0000 1.0000 Low 0.4851 0.2973 0.0128 4.3416 High 0.5365 0.2997 0.0128 4.3416-3.69-0.05-97.03 --- -6.26 ABS_TA Low 0.0112 0.0440 0.0000 0.5924 2.32 15

OUTS LOSS SIZE CFO Proceedings of the Third Middle East Conference on Global Business, Economics, Finance and Banking High 0.0086 0.0349 0.0000 0.5924 Low 0.2058 0.4043 0.0000 1.0000 High 0.1400 0.3471 0.0000 1.0000 Low 0.3951 0.4889 0.0000 1.0000 High 0.2942 0.4558 0.0000 1.0000 Low 4.5930 2.3267-0.7809 11.1905 High 4.6364 2.2872-0.7809 11.3280 Low 0.0108 0.2299-1.4028 0.3612 High 0.0420 0.1619-1.4028 0.4147 1.Variable definitions: DA=Discretionary accruals; ABS_DA=Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.t-statistics significant at the 5 percent level in a two-tailed test are in boldface. 6.22 7.68-0.68-5.47 Table 3: Variance Inflation Factor Variable VIF 1/VIF Ability POST 5.75 0.17 Ability 5.73 0.17 CFO 1.70 0.59 SIZE 1.46 0.68 LOSS 1.46 0.69 ABS_TA 1.44 0.70 DEBT 1.15 0.87 OUTS 1.13 0.89 POST 1.04 0.96 Mean VIF 2.32 16

Table 4: Univariate Correlation (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) DA 1-0.0001-0.0144-0.0647 0.3750 0.1630 0.2590-0.2710-0.2420 (2)Ability -0.0241 1 0.0638 0.1220-0.0388 ** -0.1190-0.1510 0.0245 0.0575 (3)Post 0.0056 0.1080 1-0.0471-0.0532 0.0390 ** 0.0043-0.0205-0.1230 (4)DEBT -0.0048-0.0120 0.0085 1-0.2080-0.1350-0.1290 0.2900 0.1830 (5)ABS_TA 0.1930-0.0204 0.0182 0.3740 1 0.1820 0.4210-0.8910-0.2880 (6)OUTS 0.1880-0.1020 0.0390 ** -0.0098 0.0294 * 1 0.2730-0.1730-0.3000 (7)LOSS 0.2490-0.1280 0.0043 0.0165 0.0919 0.2730 1-0.3870-0.5610 (8)SIZE -0.266 0.0065-0.0078-0.0477-0.2110-0.1690-0.3780 1 0.4060 (9)CFO -0.1110 0.0575-0.0437 ** -0.8530-0.4750-0.1010-0.2250 0.2420 1 1.Variable definitions: DA=Discretionary accruals; ABS_DA=Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.The lower left: Pearson; The upper left: Spearman. 3., ** and* indicate significance level at less than the 1%, 5% and 10% level, respectively. 17

Table 5: Earnings Quality and Managerial Ability DA =α 0+α 1Ability+α 2Post+α 3Ability Post+α 4DEBT+α 5ABS_TA +α 6OUTS+α 7LOSS+α 8SIZE+α 9CFO+ε Variables Predicted sign Coefficient t-value Coefficient t-value Intercept 0.0618 5.11 0.0821 21.84 Ability? 0.0198 1.70 * 0.0441 4.34 Post? -0.0100-3.62-0.0046-2.04 ** Ability Post - -0.0262-2.12 ** -0.0423-3.77 DEBT - -0.0036-0.32-0.0104-1.86 * ABS_TA + 0.6836 7.13 0.6429 9.11 OUTS + 0.0137 3.80 0.0206 6.03 LOSS + 0.0103 2.77 0.0273 8.73 SIZE? 0.0027 1.07-0.0032-6.36 CFO - 0.0143 0.98 0.0059 0.60 Total Observations 5,578 5,578 Adj. R-squared 33.52% 20.22% Firm Fixed Effects Included Excluded 1.Variable definitions: DA =Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.t-values are the results of White's (1980) robust estimate. 3.all data are winsorized at the 1% and 99% levels. 4., ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively. 18

Table 6: Earnings Quality and High Managerial Ability DA =α0+α1ability_h+α2post+α3ability_h Post+α4DEBT+α5ABS_TA +α6outs+α7loss+α8size+α9cfo+ε Variables Predicted sign Coefficient t-value Coefficient t-value Intercept 0.0586 4.79 0.0748 19.29 Ability_H? 0.0103 2.01 ** 0.0210 4.77 Post? -0.0065-2.14 ** 0.0019 0.71 Ability_H Post - -0.0110-1.92 * -0.0192-3.80 DEBT - -0.0037-0.33-0.0107-1.90 * ABS_TA + 0.6842 7.12 0.6437 9.09 OUTS + 0.0137 3.78 0.0206 6,00 LOSS + 0.0103 2.79 0.0273 8.73 SIZE? 0.0027 1.06-0.0031-6.28 CFO - 0.0143 0.97 0.0058 0.59 Total Observations 5,578 5,578 Adj. R-squared 33.52% 20.30% Firm Fixed Effects Included Excluded 1.Variable definitions: DA =Absolute value of discretionary accruals; Ability_H= Dummy variable that takes the value of 1 if managerial ability is positive and 0 otherwise; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.t-values are the results of White's (1980) robust estimate. 3.all data are winsorized at the 1% and 99% levels. 4., ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively. 19

Table 7: Earnings Quality and Managerial Ability-The deciles rank DA _Decile =α 0+α 1Ability_Decile+α 2Post+α 3Ability Decile Post+α 4DEBT +α 5ABS_TA+α 6OUTS+α 7LOSS+α 8SIZE+α 9CFO+ε Variable Predicted sign Coefficient t-value Coefficient t-value Intercept 5.0539 12.16 5.1553 25.14 Ability_Decile? 0.1012 2.97 0.1406 5.05 Post? 0.0554 0.26 0.4528 2.45 ** Ability_ Decile Post - -0.0919-2.46 ** -0.1152-3.64 DEBT - -0.0079-0.03-0.1480-1.06 ABS_TA + 11.4822 6.76 10.3061 9.02 OUTS + 0.3688 3.28 0.5838 5.70 LOSS + 0.3962 3.38 0.9230 9.74 SIZE? -0.0288-0.37-0.1653-8.95 CFO - 0.6772 1.91 * 0.0527 0.22 Total Observations 5,578 5,578 Adj. R-squared 33.52% 20.30% Firm Fixed Effects Included Excluded 1.Variable definitions: DA =Absolute value of discretionary accruals; DA_Decile= The decile rank (by industry and year) of absolute value of discretionary accruals; Ability_Decile=The decile rank (by industry and year) of the managerial ability; Post=Dummy variable that takes the value of 1 after year 2005 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.t-values are the results of White's (1980) robust estimate. 3.all of data winsorized at the 1% and 99% levels except DA and Ability. 4., ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively. 20

Table 8: Earnings Quality and Managerial Ability-Excluding data of year 2005 DA =α 0+α 1Ability +α 2Post+α 3Ability Post+α 4DEBT+α 5ABS_TA+α 6OUTS +α 7LOSS+α 8SIZE+α 9CFO+ε Variable Predicted sign Coefficient t-value Coefficient t-value Intercept 0.0649 5.07 0.0817 21.17 Ability? 0.0192 1.60 0.0438 4.30 Post? -0.0100-3.37-0.004-1.87 * Ability Post - -0.0281-2.19 ** -0.0432-3.82 DEBT - -0.0024-0.21-0.0083-1.44 ABS_TA + 0.6148 6.32 0.6001 8.50 OUTS + 0.0124 3.27 0.0191 5.41 LOSS + 0.0113 2.96 0.0279 8.67 SIZE? 0.0021 0.78-0.0032-6.22 CFO - 0.0097 0.62 0.0014 0.14 Total Observations 5,200 5,200 Adj. R-squared 32.99% 19.54% Firm Fixed Effects Included Excluded 1.Variable definitions: DA =Absolute value of discretionary accruals; Ability=Managerial ability; Post=Dummy variable that takes the value of 1 after year 2006 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets ;CFO=Cash flows from operating activities, deflected by total assets. 2.t-values are the results of White's (1980) robust estimate. 3.all data are winsorized at the 1% and 99% levels. 4., ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively. 21

Table 9: Earnings Quality and Managerial Ability-Excluding data of year 2005 DA =α 0+α 1Ability_H+α 2Post+α 3Ability_H Post+α 4DEBT+α 5ABS_TA +α 6OUTS+α 7LOSS+α 8SIZE+α 9CFO+ε Variable Predicted sign Coefficient t-value Coefficient t-value Intercept 0.0613 4.74 0.0743 18.66 Ability_H? 0.0108 2.07 ** 0.0209 4.74 Post? -0.0057-1.72 * 0.0026 0.95 Ability_H Post - -0.0136-2.30 ** -0.0202-3.94 DEBT - -0.0026-0.22-0.0085-1.47 ABS_TA + 0.6159 6.32 0.6017 8.49 OUTS + 0.0123 3.24 0.0190 5.37 LOSS + 0.0114 2.98 0.0279 8.66 SIZE? 0.0021 0.81-0.0031-6.14 CFO - 0.0096 0.62 0.0013 0.13 Total Observations 5,200 5,200 Adj. R-squared 33.00% 19.62% Firm Fixed Effects Included Excluded 1.Variable definitions: DA =Absolute value of discretionary accruals; Ability_H= Dummy variable that takes the value of 1 if managerial ability is positive and 0 otherwise; Post=Dummy variable that takes the value of 1 after year 2006 and 0 otherwise; DEBT=Rate of interest bearing liabilities; ABS_TA= Absolute value of total accruals, deflected by total assets; OUTS= Dummy variable that takes the value of 1 if outstanding stocks change over 10% and 0 otherwise; LOSS= Dummy variable that takes the value of 1 if firm s bottom line is loss and 0 otherwise; SIZE=The natural log of total assets; CFO=Cash flows from operating activities, deflected by total assets. 2.t-values are the results of White's (1980) robust estimate. 3.all data are winsorized at the 1% and 99% levels. 4., ** and* indicate significance level for a two-sided test at less than the 1%, 5% and 10% level, respectively. 22