49
Accruals Earnings Management
50
& Song, 2014), meet previous year’s earnings (Cupertino et al., 2015; Gunny, 2010; Zang, 2012) and reverse merger (Zhu, Lu, Shan, & Zhang, 2015).
Firms that engage in RPTs can be tagged as suspect firms. This is because previous studies have established that RPTs can serve as a vehicle for earnings management practices (Aharony, Wang, & Yuan, 2010; Beuselinck & Deloof, 2014; Munir, Saleh, Jaffar, & Yatim, 2013).
Insiders determine or can manipulate the terms and conditions of the RPTs even though they are subjected to regulatory scrutiny (Cheung, Qi, Raghavendra Rau, & Stouraitis, 2009; Hwang et al., 2013). Based on this, the study considers firms with RPTs as a suspect for earnings management.
Badertscher (2011) identified and used overvalued firms as a suspect for earnings management.
Similarly, Cupertino et al. (2015) examine the investors’ perception on earnings management, while Abad, Cutillas-Gomariz, Sánchez-Ballesta and Yagüe (2016) investigate the effect of earnings management on information asymmetry among investors. The current study differs from the above studies as it focuses on the effect of earnings management perpetrated through RPTs on the firm value, which is the overall goal of any business venture.
Related Literature and Hypotheses Development
RPTs are regarded as healthy and beneficial transactions to firms all over the world.
Nevertheless, they can be used as a tool to expropriate if not siphons the firms’ resources or to prop up some firms in some cases. Consequently, any of these strategies or a combination of both masks the true results of the operations and bring distortion to the reported earnings.
Hence, it affects the quality of the reported earnings and may be seen as an element of earnings management. Johnson, La Porta, Lopez-de-Silanes and Shleifer (2000) show that even in developed capital market insiders have been using RPTs to transfer profits and assets to other firms for the benefit of other firms. Chen, Lee and Li (2008) provide empirical evidence that many governments spin-off firms in China are assisted by their holding firm to boost their earnings so as to meet capital market regulatory listing requirements.
Ding, Zhang and Zhang (2007) disclose that firms manage earnings through both operating accruals mechanisms and non-operating transactions with related parties. Cheung et al. (2009) demonstrate how firms manipulate earnings through transfer price by acquiring assets at high cost from related entities and disposing it to another related entity at a lower price. Consistently, Jian and Wong (2010) indicate that listed firms prop up their controlling firms through abnormal related party sales. They reveal that most of the sales are cash-based transactions which will amount to significant cash transfer to the controlling entity. Even the frauds that led to the collapse of the giant corporations (Enron, Healthsouth, and others) were perpetrated through RPTs. In many of these frauds, management was identified with many RPTs that amounted to the self-enrichment and misleading financial reports (Kohlbeck & Mayhew, 2010). Hwang et al. (2013) reveal that most of the transaction with offshore affiliate were arranged to provide an avenue for earnings management within the business group. Since RPTs can serve as a tool to perpetrate earnings management conveniently, the study expects that:
H1. Firms perpetrate accruals earnings management through RPTs.
Zhao et al. (2012) discover that apart from target beating, other incentives for earnings management are value-destroying. Earnings management imposes greater costs on the shareholders because of its negative consequences on the firms’ long-term value (Cheng, 2012;
Cohen et al., 2008; Roychowdhury, 2006). Ge (2009) reports that whenever a firm reduces
51
prices as an incentive to show up earnings, although the earnings of the first period may increase, but the overall earnings of the both periods decline. Similarly Kothari et al. (2015) and Cohen and Zarowin (2010) record underperformance of firms during post season equity offering (SOE) when the offering is driven by earnings management. They show that the decline in post-SEO performance for real earnings management is more compared to accruals earnings management. In view of these, the study expects that:
H2. Stock market discounts the value of the firms that engage in accrual earnings management Research Design
This study investigates the extent of earnings management practices through RPTs. In this section, the study explains in detail the methodology adopted to measure and define the relationship among the research variables.
3.1 Measures of earnings Management
To measure the level of earnings management through accrual manipulations, the study uses the modified Jones model to determine the non-discretion portion of (normal) accruals.
TAC/At-1 = α0 + α1(1/At-1) + α2(∆S - ∆R /At-1)+ α3(PPEt/At-1) + εt (1) Where TAC = Total accruals defined as EBXI – CFO. EBXI is the earnings before extraordinary income and preferred shares dividend and CFO is the cashflow from the operating activities. At-1 = total assets in year t-1; S = total sales revenue; ∆S = change in sales revenue from year t-1 to year t; ∆R = change in receivables from year t-1 to year t; PPE = property, plant and equipment.
The above model was used to estimate the predicted (normal) level of accruals for each industry. To determine the abnormal accruals, the study subtracts the normal accruals (NA) from the total accruals from the corresponding industry regression.
NA/At-1 = α0 + α1(1/At-1) + α2(∆S - ∆R /Ait-1)+ α3(PPE/Ait-1) + εt (2) The study measures the discretionary accruals AR_1 as the difference between the actual total accruals and normal accruals determine from the equation 2 for each industry level, defined as DA = TAC/At-1 - NA.
The second measure for accrual earnings management is used to capture the earnings smoothing through accruals manipulations. Leuz, Nanda and Wysocki (2003) state that managers may use accruals to mitigate economic shocks to the operating cashflow of the firm.
So, if this assertion holds, the standard deviation of reported earnings divided by the standard deviation of operating cashflow becomes smaller (Enomoto, Kimura, & Yamaguchi, 2015;
Leuz et al., 2003).
AR_2=(σ(EBXI))/(σ(CFO)) (3)
Sample Selection and Identification of suspect firms
Relevant data on the research variables are taken from the Thompson Reuters DataStream. The data on the RPT are manually collected from the annual reports of the firms and circulars to the shareholders which are downloaded from the website of the Bursa Malaysia. The full sample of this study comprises 6972 firm-year observations generated from the firms listed on Bursa Malaysia stock exchange from 2009 to 2015. Out of this, 987 observations related to the
52
banks and other financial service, closed-end, REIT and SPAC firms are removed from the population. Similarly, 273 observations related to the dead/delisted and non-equity firms are also removed from the study. Lastly, 582 firm-year observations without complete data are eliminated from the study, making the final sample 5130 firm-year observations as provided in Table 1. Previous studies have identified the firms with RPTs with accrual earnings management. In this study, the study identified the firms with RPTs of up to 20% of its revenue or the firms that engage in more than one form of RPTs in any given year and level them as suspect firm. These firms are firms with high tendencies of earnings management practices.
Table 1
Sample selection procedure and year distribution
Observations Year Observations
Total Firm year observation 6972 2009 741
Banks and other financial service, closed-end, REIT and SPAC (987) 2010 739
Non-highly regulated firm years 5985 2011 735
Dead, delisted and non-equity firms 273 2012 733
Firm years for continuing firms 5712 2013 738
Firms without complete data 582 2014 732
Final observation available for the study 5130 2015 712
Non-suspect firm years 4477
Suspect firm years 653 5130
Regression Model
To test the H1, the study runs the equation 4 with the proxies for earnings management, which is the dependent variable in separate regressions. For the H1, the two proxies for the accrual earnings management determine in section 4.1 (AR_1 and AR_2) are used as dependent variables and estimated separately. To accept the H1, the suspect firm should have the positive relationship with the dependent variable in the firm years with higher accruals or earnings smoothing.
Thus, the model for estimating the earnings management for RPTs firms is as follow:
Y = α0 + α1 (Size) + α2 (MtB) + α3 (ROA) + α4 (Suspect) + εt (4) Where: Y = individual proxy for earnings management (the test will be repeated for each of the eight proxies for earnings management); Size = natural logarithm of book value of total assets; MtB = Market-to-book ratio; ROA = EBXI/Total Assets and Suspect = Dummy variable equals to one for the suspect firm and zero for otherwise. The suspect firm is defined as a firm with RPTs of up to 20% of its revenue or the firms that engage in more than one form of RPTs in any given year.
Measurement of the Firm Value
Capital market can be said to be effective when it responds and incorporates new information into the stock price quickly. These features are lacking in most of the countries with the less developed capital market (Downs et al., 2016). The study used Tobin’s Q as a proxy for firm value to investigate whether the capital market discipline the firms that engage in earnings management (H2). Tobin’s Q as a measure of firm value vis a vis some firms’ characteristics has been widely used in previous studies (Daske, Hail, Leuz, & Verdi, 2008; Downs et al., 2016; Landsman & Shapiror, 1995). It is defined as the market value of equity minus the book value of equity plus total assets scaled by total assets. Fama and French (1998) argued that regression of cross-sectional data has an upper hand over an event study in valuing the firms.
53
This is because it is based on the long-term effect of the firm characteristics on the firm value.
Moreover, Morck, Yeung and Yu (2000) documents that most of the stock in emerging economies move together. Thus, the study formulates the following model:
Y = α0 + α1 (Tobin’s Q * Suspect) + α2 (Tobin’sQ) + α3 (Suspect) + α4 (Size) + α5
(ROA) + α6 (Leverage) + α7 (Audit) + α8 (Dividend) + α9(∆NI) + α10 (Liquidity) + α11
(Ind_ dumm) + εt (5)
Where Y = earnings management proxies (two proxies for accruals earnings management and six proxies for real earnings management); Tobin’s Q = market value of equity + book value of total liabilities, scaled by total assets; Suspect = dummy variable equals to one for the suspect firm and zero for otherwise. Suspect firm is defined as a firm with RPTs of up to 20% of its revenue or the firms that engage in more than one form of RPTs in a given year; Size = natural logarithm of total assets; ROA = earnings before extraordinary incomes (EBXI), scaled by total assets; Leverage = total debt, scaled by total assets; Audit = audit fee, scaled by total asset;
Dividend = declared dividend, scaled by total assets; ∆NI = change in a firm’s EBXI, scaled by total asset in year t-1; Ind_dumm = an industry indicator variable which equals 1 if the firm belongs to industry m. The subscript m equals 1, 2...or m-1, where m represents the number of unique industries based on the bursa Malaysia industry classification.
The equation 5 is run for both of earnings management proxies separately, making two separate regressions to assess the stock market valuation of the firms that perpetrate earnings management. The first variable of interest in the above equations is the interaction of suspect with the value measurements (Tobin’s Q). The study expects the coefficients of these variables to be negative in the measures. The second variable of interest is the coefficient of Tobin’s Q.
This variable is expected to have the same sign but with lower coefficients than that of interaction variables. The expected signs will serve as robust to the interaction variable. This is because of the suspect firms and other firms that might have been in earnings management through any other means (apart of RPTs) that are included in Tobin’s Q of the full sample. The coefficients are expected to be lower than that of suspect firms because the full sample comprises firms with earnings management (through RPTs and other forms) and those firms that do not involve in any form of earnings management will leverage the figure and make it lower than that of the suspect firms.
Several control variables are included in the equation 5 to control for their effects on the respective earnings management measures. The study included ROA to control for the measurement error in calculating abnormal accruals and other proxies for real earnings management for its positive correlation with the performance (Dechow, Sloan, & Sweeney, 1995; Gunny, 2010). The study used Audit to control for audit quality based on the extent of literature that proved big audit firms constrain the extent of accruals earnings management (Ho, Liao, & Taylor, 2015; Zang, 2012). The study then considered controlling the following variables as used in the previous studies. Dividend and Liquidity (Healy & Wahlen, 1999;
Jaggi, Leung, & Gul, 2009) and change in income (Chi et al., 2011). In addition to the above, the study included the Size to control for firm relative size in the industry, Leverage for capital structure and industry to account for the industry variations (Cohen & Zarowin, 2010; Ho et al., 2015; Kuo et al., 2014).
54 Empirical Results
Descriptive Statistics Firm Characteristics
Table 3 panel A- C provide the descriptive statistics for all variables in the models related to the full sample, suspect firm and non-suspect firm years respectively. The mean total assets of the suspect firms is 1.82 million which is higher than that of the full sample and non-suspect firms 1.60 million and 1.56 million respectively. This indicates that bigger firms are more involve in RPTs because of their size and mostly are holding companies with a number of subsidiaries and other related firms to engage with. Similarly, the mean ROA of the suspect firms is 4.50% which is greater than the average of the full sample and the non-suspect firms 3.30% and 3.12% respectively. This signals that suspect firms use RPTs to boost their reported earnings as the extent of literature does not support the notion that large firms are more profitable than smaller firms. Though suspect firms are characterized by the higher profitability, but the situation is not the same as to the market valuation. The means of market value per asset of the suspect firm is 0.88 which is lower than that of the full sample and non-suspect firm 0.94 and 0.95. This indicates that the stock market is skeptical with the reported earnings of the suspect firms. The Tobin’s Q of the suspect firms1.22 is greater than 1.17 and 1.16 for the full sample and non-suspect firm respectively. This is connected to the debt element present in arriving at Tobin’s Q. The extent of the debt can be seen as the mean leverage of the suspect firms 20.29 is higher than 18.85 and 18.64 for the full sample and non-suspect firms respectively. Interestingly, the means of the eight proxies for earnings management of suspect firms are in direct opposite with that of the non-suspect firms.
Table 3
Panel A Descriptive statistics (full sample)
Panel B Descriptive statistics
(suspect) Panel C Descriptive statistics (non-suspect)
Variable Mean Median Stand.
Dev. Mean Median Stand.
Dev. Mean Median Stand.
Dev.
AR_1 0.000000 -0.000013 0.249477 0.001082 0.002571 0.214325 -0.000158 -0.000350 0.254219 AR_2 0.000000 -0.000013 0.249477 -0.004109 -0.000806 0.203867 0.000599 0.000186 0.255464 Size 12.66544 12.55600 1.593870 13.24780 13.19500 1.515850 12.58050 12.45100 1.587340 MtB 1.334401 0.822000 3.318126 1.589816 0.923000 2.619564 1.297147 0.811000 3.406716 ROA 3.295497 3.930000 21.399370 4.503874 4.270000 11.235900 3.119247 3.900000 22.496820 MVET 0.940364 0.488018 7.327301 0.883801 0.521718 1.206682 0.948615 0.484267 7.830032 Tobinsq 1.165098 0.871183 2.637914 1.222879 0.919421 1.141735 1.156671 0.862172 2.789862 Leverage 18.84835 15.74000 18.08144 20.29455 19.54000 17.65434 18.63741 15.40000 18.13519 Audit 1556.939 194.187 28277.770 532.157 223.000 1516.008 1706.443 190.835 30262.280 Dividend 0.021661 0.004444 0.127227 0.027451 0.006636 0.098477 0.020817 0.003863 0.130882 ChangeNI 0.061640 0.003749 2.390241 0.004993 0.002763 0.098934 0.069903 0.003858 2.558279 Liquidity 2.947661 1.840000 4.663646 2.619877 1.690000 3.376304 2.995470 1.870000 4.821219 TA 1596486.0 283818.0 6266279.0 1818096.0 537497.0 4056255.0 1564162.0 255543.0 6526094.0 Ind_dumm 0.667252 1.000000 0.471243 0.782542 1.000000 0.412833 0.650436 1.000000 0.476886 AR_1 = Abnormal discretionary accruals; AR_2 = Earnings smoothing, defined as (σ(EBXI))/(σ(CFO)); Ab_CFO = Abnormal cashflow from operating activities;Size = natural logarithm of total assets; MtB = market-to-book value; ROA = earnings before extraordinary incomes (EBXI), scaled by total assets; MVET = market value of equity scaled by total assets; Tobin’s Q = market value of equity + book value of total liabilities, scaled by total assets; Leverage = total debt, scaled by total assets; Audit = audit fee, scaled by total asset; Dividend
= declared dividend, scaled by total assets; ∆NI = change in a firm’s EBXI, scaled by total asset in year t-1; Liquidity = current ratio; TA
= total asset; Ind_dumm = an industry; other variables as previously defined
55 Estimation Models
Table 2 reports the estimation results from the regression of the equations that are used to determine the normal levels. The equations are estimated for each industry throughout the study period (2009 to 2015). The reported values are the mean of the coefficients and R2 across the industry. Most of the coefficients are significant with the predicted values. The t-values are determined by dividing the mean coefficients with their respective standard errors of the mean coefficients. The explanatory power of the models are high.
Table 2
Estimation Models
Accruals/TA-1
Intercept 0.1022***
(5.62)
1/TA-1 1973
(0.25)
Δsales-Rec/TA-1 -0.0803
(-0.83)
PPE/TA-1 -0.3732***
(-14.39)
R2 0.3936
*Significant at the 10% level. ** Significant at the 5% level. *** Significant at the 1% level.
Model 1 = TAC/At-1 = α0 + α1(1/At-1) + α2(∆S - ∆R /At-1)+ α3(PPEt/At-1) + εt
Multiple Regression Results
Table 4 presents the coefficients of the regressions for testing the earnings management activities by suspect firms. None of the accrual earnings management measures indicates a significant relationship with the suspect firms.
Table 4
Results for testing H1
AR_1 Coefficient (t-value)
AR_2 Coefficient (t-value)
_cons -0.0112 0.0622**
(-0.4) (2.19)
Size 0.0005 -0.0048**
(0.24) (-2.14)
MtB -0.0032*** -0.0006
(-3.09) (-0.58)
ROA 0.0027*** -0.00004
(16.87) (-0.24)
Suspect -0.0020 -0.0013
(-0.19) (-0.12)
Based on the above findings, the study rejects the H1, which states that firms perpetrate accruals earnings management through RPTs. These findings are consistent with Zang (2012), that managers trade off the earnings management methods (accrual earnings management and real earnings management) and use one as a substitute for other. This is because each method has its own costs and the costs of mixing the two methods may be heavy to the firm. Several studies have proved that managers switch to real earnings management as a result of tight
56
regulations,1 which constrain the accruals earnings management practices. So frequent improvements in Malaysian corporate governance code (2000, 2007, 2012, 2016), activities of audit oversight board from 2010, mandatory adoption of international financial reporting standard in 2012, improved listing requirement, minority shareholders watchdog group may trigger the managers to shift from accrual to real earnings management that can conducted conveniently through RPTs.
Table 5 presents the coefficients of the regressions run to test for the second hypothesis (H2).
The coefficients and statistical significance of the first variable of interest (TQSuspect) are in line with the predictions across the earnings management proxies. Similarly, the coefficients of the second variable of interest (Tobin’s Q) are as the study predicted with the exception of abnormal discretionary expenditure with the positive sign against the prediction. The coefficient of TQSuspect in AR_2 (measure for earnings smoothing) is -0.0219 and significant at 5% (t-value -2.5). This implies that the market value of the suspect firm will decrease by 2.19% should the firm engage in smoothing while a decrease in Tobin’s Q of the full sample is just 0.02%. Though, the results for the test for H1 does not provide evidence on accrual management by suspect firms.
Table 5 Results for testing H2
AR_1 Coefficient
(t-value)
AR_2 Coefficient (t-value)
_cons 0.0121 0.0399
(0.43) (1.34)
TQSuspect -0.0030 -0.0219**
(-0.36) (-2.5)
TobinsQ -0.0101*** -0.0002
(-6.86) (-0.12)
Suspect 0.0021 0.0241
(0.15) (1.61)
Size -0.0023 -0.0035
(-1.04) (-1.5)
ROA 0.0028*** -0.0001
(12.52) (-0.4)
Leverage 0.0010*** -0.0003
(4.89) (-1.29)
Audit -0.0001 -0.0001
(-0.18) (-0.4)
Dividend 0.2767*** 0.0184
(8.8) (0.55)
ChangeNI 0.0320*** 0.0011
(12.02) (0.38)
Liquidity 0.0004 0.0014*
(0.48) (1.74)
Industry dummies Yes Yes
R2 0.1067 0.0038
1 Both mandatory regulations (Chi et al., 2011; Ho et al., 2015) and incentive-based regulations (Kuo et al., 2014) have motivated managers to switch from accruals earnings management to real earnings management.
Similarly, (Cupertino et al., 2015) attributed the shift to the investors awareness and their ability to detect earnings management conducted through real activity manipulations.
57 Conclusion
This study contributes to the earnings management literature in several ways. The study examined whether firms perpetrate accruals earnings management through RPTs. The study used modified Jones model to capture the accruals earnings management and (Leuz et al., 2003) to measure earnings smoothing. The study also examined the implication of earnings management on the market value of a firm.
The study contributed to the earnings management literature through the following. First, the study extends the current literature by identifying and examining the RPTs firms as a suspect for earnings management. Previous studies focus on firms that meet previous earnings, beat analysts’ forecast, small positive profits, during IPO or SEO. Second, the study documented that earnings management in Malaysia is beyond the use of accounting method. Third, and most important, the study documented that stock market discounts the value of the firms that engage in earning management. This finding implies that investors are aware of the existence of real earnings management and act accordingly.
The results have implications for auditors, and researchers. For auditors, it implies that auditors should incorporate the risk of earnings management in audit planning and extend the scrutiny to possible real earnings management instances. For researchers, it implies that the field of earnings needs thorough investigation as there may be many possible forms of real earnings management yet to be discovered. The study recommends future researchers to investigate the effect of real earnings management on the firm value and through the use of firms with other forms of incentive for earnings management as the suspect firms. The study also recommends future researchers to investigate the cause of the shift from accruals earnings management to prove whether the shift was due to the tight regulation or to other cause.
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