Earnings management and accrual anomaly across market states and business cycles

Advances in Accounting, incorporating Advances in International Accounting 28 (2012) 344–352

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Advances in Accounting, incorporating Advances in International Accounting journal homepage: www.elsevier.com/locate/adiac

Earnings management and accrual anomaly across market states and business cycles Samuel Y.M. Ze-To ⁎ Hong Kong Baptist University, Kowloon Tong WLB517, Hong Kong

a r t i c l e

i n f o

Keywords: Earnings management Market state Accrual anomaly

a b s t r a c t This paper examines if the conditioning on market states is important to earnings management behaviors and profitability of accrual hedge strategy. This paper discusses four findings. First, accrual profits are consistently positive across both market states and significantly higher in DOWN markets. Second, while earnings management exists in both market states, the management effort is less effective and short-lived in the DOWN state. Third, this paper finds that the accrual effect exists but varies across industries. Finally, this paper examines how business cycles associate with accrual anomaly and show that accruals mispricing cannot be fully captured by macroeconomic model predicted returns. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Sloan (1996) shows that a hedge strategy of buying low accruals stocks and selling high accruals stocks can generate significant returns in the following year. The predictive power of accruals is presently receiving more attention. Sloan (1996) shows that investors tend to give more weight to accruals when predicting future returns. It turns out that accruals are less persistent. This paper examines the causes of accounting-based accrual anomaly across different market states and business cycles to determine the persistence of the accrual components of annual earnings. The motivation of the paper to investigate whether this level of persistence toward accruals is time-varying and dependent on the market sentiment reflected from the stages of market states. The UP market state reflects the gradual increase of optimism and confidence of investors toward the growth of stock prices and company performance, while the DOWN market state shows the growth of pessimism and annoyance of the market toward future business prospects. This paper questions whether the market states would influence the extent of persistence of accruals and hence the profitability of the accrual hedge strategy. Therefore, during the DOWN market state, investors are generally pessimistic to the future earnings prospect; they hastily search for a firm with high earnings that could be achieved by using higher accrual earnings. Hence, the market overprices these abnormal accruals to a further extent and leads to a higher market's failure to correctly assess the persistence of the accruals. In the UP market state, however, most investors are optimistic toward the economy outlook with plenty supply of firms with high earnings available in the market. Hence, the overpricing of accrual is less severe. The zero investment of buying a low accrual sorted portfolio and selling a high accrual sorted portfolio consistently generates ⁎ Tel.: +852 97232219. E-mail address: [email protected]. 0882-6110/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.adiac.2012.09.011

higher abnormal profits in subsequent years in the DOWN market state in the study. This validates my hypothesis that the market overpricing of the abnormal accruals varies with the market state. Moreover, this paper conducts a similar test of Chordia and Shivakumar (2002) to examine whether the predictability based on the multifactor macroeconomic model can fully explain the profitability of the accrual hedge strategy across different market states. The accrual anomaly has been extensively studied from a number of perspectives. First, some researchers have explored the causes of the accrual anomaly. Xie (2001) examines the discretionary accruals and find that the market overestimates the persistence of discretionary accruals. The overpricing of accruals studied by Sloan (1996) is mainly due to the discretionary accruals. Mashruwala, Rajgopal, and Shevlin (2006) suggest that transaction costs impose barriers to exploiting accruals mispricing. Chan, Chan, Jegadeesh, and Lakonishok (2006) explain that the managerial manipulation of earnings leads to accruals mispricing. Fairfield, Whisenant, and Yohn (2003) propose that accruals mispricing is caused by the growth of net asset value. Desai, Rajgopal, and Venkatachalam (2004) investigate whether the accrual anomaly is a manifestation of the glamour stock phenomenon. They conclude that the operating cash flows scaled by price capture the accrual anomaly. Richardson, Sloan, Soliman, and Tuna (2006) indicate that the temporary accounting distortion, such as the push by accounting standard setters toward “fair value” accounting, is a significant contributing factor to the lower persistence of the accrual component of earnings. Richardson, Sloan, Soliman, and Tuna (2005) confirm that the less reliable accruals lead to lower earnings persistence. The market does not fully anticipate this lower persistence, leading to significant security mispricing. Second, Hribar (2001) analyzes the components of accruals to identify the extent of their contributions to the accrual profits. Chan et al. (2006) also examine the impact of accrual components on the abnormal return of stocks. They find that inventory changes have the largest predictive power for future returns. Third, Teoh, Welch, and Wong

S.Y.M. Ze-To / Advances in Accounting, incorporating Advances in International Accounting 28 (2012) 344–352

(1998a, 1998b) study the relationship between accrual earnings management and long-term performance of IPOs and seasoned equity offerings. Furthermore, Pincus, Rajgopal, and Venkatachalam (2007) extend the test of accrual effects to foreign stock markets and investigate the accrual anomaly in 20 international stock markets. Fourth, some researchers examine the trading strategy using accruals. Collins and Hribar (2000) propose a joint hedge portfolio trading strategy that capitalizes on both the unexpected earnings and accruals mispricing and can generate abnormal returns larger than those based on either earnings surprise or the accrual anomaly alone. Barberis, Huang, and Santos (2001) and Ahmed, Nainar and Zhang (2006) find that the market consistently underestimates the persistence of operating cash flows. The operating cash flow effect is stronger than the accrual effect. A trading strategy based on both the working capital accruals and operating cash flows can earn higher abnormal returns than one based on accruals alone. Shivakumar (2006) also states that unexpected cash flows are more positively related to future returns than unexpected accruals are. This paper contributes to the literature in four aspects. First, the study indicates that market states relate with the profitability of accrual hedge strategy. It is shown that while the accrual anomaly exists in both market states, the accrual hedge profits are relatively higher in DOWN markets. Sloan (1996) indicates that the accrual component of earnings is less persistent while the investors naively fixate on earnings and push the stock prices higher for higher accrual values. Daniel, Hirshleifer and Subrahmanyam (1998) state that investors tend to react asymmetrically to confirming versus disconfirming pieces of news. Hence, during the market state, investors incline to be pessimistic about the future economy outlook and overreact to good news. The overreaction result in overpricing of high accrual firms and subsequent reversal in stock returns. Second, this paper also examines the variation of earnings management across market states. It is shown that the earnings management behavior tends to be more effective in the UP market state. The improvement of earnings by accruals is short lived and difficult to be sustained in the DOWN market state. Third, the paper extends the study to the industry level and finds that the accrual anomaly varies in different industries. Finally, the study also examines the association of business cycle and the accrual anomaly and tests whether the mispricing can be explained by macroeconomic predictor variables. 2. Empirical specification

total accruals and discretionary current accruals. The current accruals are defined as: h i h i CACCi;t ¼ Δ CAi;t −CASHi;t −Δ CLi;t −STDi;t

ð2Þ

where: CACC i,t CAi,t CASHi,t CLi,t STDi,t

stands for the current accruals at time t for stock i, is the current assets, is cash, represents the current liabilities and is the portion of long-term debt with current maturity.

This paper runs the cross-sectional regression for the following models: ACCj;t 1 ¼ a0 TAj;t−1 TAj;t−1

! þ a1

ΔSalesj;t TAj;t−1

! þ a2

PPEj;t TAj;t−1

! ð3Þ

þ εj;t

where: j ΔSalesj,t TAi,t − 1 PPEi,t

belongs to the sample for estimation, stands for the change of sales at time t, represents the beginning total assets of the year and is gross property, plant and equipment.

Non-discretionary total accruals (NDTA) are estimated as follows: NDTAi;t ¼ a^ 0

1 TAi;t−1

! þ a^ 1

ΔSalesi;t −ΔTRi;t TAi;t−1

! þ a^ 2

PPEi;t TAi;t−1

! ð4Þ

where: â0, â1, â2 are the estimated slope coefficients for stock i in year t and ΔTRi,t represents the change in trade receivables. Discretionary total accruals (DTA) are represented as DTAi;t ¼ ACCi;t −NDTAi;t :

ð5Þ

Similarly, the discretionary current accruals (NDCA) and discretionary current accruals (DCA) are estimated, respectively, as follows:

2.1. Data and methodology This paper collects monthly data of all firms listed on the NYSE and AMEX markets from Datastream over the period from May 1989 to August 2007. Closed-end funds, investment trusts and foreign companies are excluded. In view of the errors in accrual calculation by using Sloan's (1996) approach for firms involved in mergers and acquisitions (Drtina and Largray, 1985), this paper adopts Collins and Hribar's (2000) method in estimating accruals as follows: ACCi;t ¼ EARNi;t −CFOi;t

345

ð1Þ

NDCAi;t ¼ c^0

DCAi;t ¼

1 TAi;t−1

! þ c^1

ΔSalesi;t −ΔTRi;t TAi;t−1

!

CACCi;t −NDCAi;t TAi;t−1

ð6Þ

ð7Þ

where ĉ0, ĉ1

are the estimated slope coefficients for stock i in year t.

where:

2.2. Market state

stands for the total accruals at time t for stock i, is the net income before extraordinary items as proposed by Pincus et al. (2007) and CFOi,t is the cash flow from continuing operations. This paper scales each item by beginning total assets as in Sloan (1996) to improve cross‐sectional comparability. This paper also adopts the modified Jones Model as proposed by Dechow, Sloan and Sweeney (1995) to estimate the discretionary

To examine how accruals mispricing relates to the state of the market, this paper follows the definition of market states proposed by Cooper, Cutierrez, and Hameed (2004) that the market is in an “UP” state when the lagged three-year market return is positive, while a “DOWN” state occurs when the three-year lagged market return is negative. This paper estimates the annual return of the Dow Jones Industrial Average at the end of May of each year from 1989 to 2007.

ACC i,t EARNi,t

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Table 1 Descriptive statistics and Pearson correlation for firms with required data. Panel A: descriptive statistics US market

Mean

Std. Dev.

10%

25%

Median

75%

90%

RET EARN ACC CFO SG MV PBV PE CA CL

0.388 −0.436 −0.247 −0.194 0.070 5.785 6.214 31.631 0.577 0.369

27.661 33.817 11.269 27.954 3.431 2.540 151.257 224.934 8.182 8.087

−0.300 −0.037 −0.139 −0.014 −0.060 2.650 0.820 8.900 0.000 0.000

−0.094 0.012 −0.081 0.032 0.000 4.199 1.290 12.400 0.097 0.082

0.066 0.043 −0.042 0.084 0.038 5.993 1.900 17.200 0.364 0.196

0.293 0.086 −0.008 0.139 0.141 7.510 3.060 25.000 0.613 0.314

0.633 0.144 0.035 0.207 0.307 8.785 5.340 42.000 0.832 0.458

Panel B: Pearson correlation coefficients US market

RET

EARN

ACC

CFO

SG

MV

PBV

PE

CA

EARN ACC CFO SG MV PBV PE CA CL

−0.1240 −0.0420 −0.1340 −0.0004 −0.0590 −0.0003 0.0001 0.1210 0.0700

0.649 0.952 0.0022 0.020 −0.016 −0.001 −0.714 −0.466

0.3850 0.0005 0.0340 −0.0203 −0.0060 −0.2340 −0.2820

0.0024 0.0100 −0.0110 0.0030 −0.7770 −0.4500

0.006 0.001 −0.008 0.002 0.005

−0.011 −0.001 −0.007 −0.023

−0.001 0.003 0.010

0.001 0.001

0.6550

The paper intends to test if the market state and earnings management behavior are related. The stages of business cycle and condition of the market state are normally not concurrent. The market is forward looking. At the UP market state, it is relatively easy for the firms to attain growth in earnings due to the favorable economic environment. At the DOWN market state, however, market sentiment turns bearish before the deterioration of the economic condition, and the management may resort to a more aggressive earnings manipulation behavior in order to avoid the earnings decline or assure the earnings growth to please the analysts and investors. The average returns of a hedge strategy of buying stocks with low accruals and selling stocks with high accruals after both UP markets and DOWN markets are calculated. To allow for better comparisons, the monthly returns are used. The average accrual hedge returns after forming the portfolio (in May of year t) are estimated for five periods: years t − 2, t − 1, t, t + 1, and t + 2, where t represents the portfolio formation year. 2.3. Business cycle The paper adopts the information of widely accepted National Bureau of Economic Research (NBER) for timing the stages of a business cycle. NBER offers objective measurements of the economic business cycle and identifies the start and end of economic contraction and expansion. The study utilizes the NBER data of the business cycle expansion and contraction from 1989 to 2007. The contractions of the US business cycle begin at the peak of a business cycle and end at the trough. Expansions start from the trough of the previous business cycle and end at the peak. The calculation is based on the various periods of expansion and contracted by NBER's business cycle dating committee that could be found from its website (http://www.nber. org/cycles.html). 3. Empirical results and analysis 3.1. Descriptive statistics Table 1 presents the summary statistics and Pearson correlations of all selected variables. The sample comprises 358,258 firm-month observations from 1989 to 2007. Panel A reports the descriptive

statistics. It is shown that the accruals tend to be negative with mean and median values of − 0.24 and − 0.04, respectively. The median earnings are 4.3% of the total assets. Panel B reports the Pearson correlations among the variables. It is shown that the earnings and accruals are negatively related. The CFO is also positively related with sales growth. 3.2. Variation of characteristics of accrual ranked portfolio across market states Table 2 shows the variation of characteristics of accrual sorted portfolios over time in the two market states. The characteristics under study include the accrual hedge profit, earnings before extraordinary items, operating cash flow, sales growth and accrual components. This paper compares the variation of these characteristics between UP and DOWN market states. 3.2.1. Accrual hedge profit The result is consistent with the findings of Sloan (1996) that accruals value is negatively related to its future return. In addition, the monthly average accrual returns in both the UP and DOWN markets are significantly positive in all five periods. The table also provides the t-statistics for testing the equality of the profits across UP and DOWN market states for the post-ranking periods. When comparing the profits of the two market states, it is shown that the average returns are significantly different from each other. The accrual return in the DOWN market is significantly larger than that in the UP market (monthly return in period t—UP:DOWN state, 0.8%:4.5%). One of the possible reasons for this is the higher overreaction of the market toward the accrual anomaly in DOWN market states, where investors suffer loss from consistently lower lagged stock returns and become pessimistic. On one hand, in order to avoid the decline of earnings, management may manipulate the earnings by increasing the accrual component that has a much lower persistence of earnings performance. On the other hand, the high extent of risk aversion of investors intensifies the purchase of firms with this high accrual earnings and the selling of low accrual firms. Hence, the market extends the good past earnings in view of high accruals into future performance, but it only finds out that the future return is poorer than expected because of accrual reversals.

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Table 2 Variation of characteristics of stock portfolios sorted by accruals across market states. UP state

DOWN state

Monthly return

Acc. low 1 2 3 4 Acc. high 5 Low–high t-Statistic Test for equality

t− 2

t−1

t

t+ 1

t+2

0.034 0.011 0.011 0.014 0.020 0.013 3.113

0.030 0.010 0.011 0.013 0.023 0.007 1.880

0.025 0.013 0.010 0.012 0.017 0.008 2.621

0.026 0.014 0.011 0.013 0.015 0.011 6.172

0.021 0.013 0.012 0.014 0.017 0.004 3.185

t−2

t− 2

t−1

t

t+ 1

t+2

−2.613 0.055 0.090 0.046 0.049 −2.662 −3.901

−2.560 0.053 −0.009 0.061 0.027 −2.587 −3.909

−3.147 0.051 0.053 0.062 0.111 −3.259 −5.168

−1.051 0.009 0.044 0.055 0.100 −1.152 −6.441

−0.156 −0.058 0.043 0.051 0.059 −0.215 −6.341

t− 2

t−1

t

t+ 1

t+2

t−2

0.100 0.090 0.072 0.050 0.102 −0.002 −0.387

0.104 0.097 0.071 0.066 0.106 −0.002 −0.509

0.092 0.088 0.078 0.066 0.121 −0.029 −6.560

0.110 0.077 0.068 0.057 0.145 −0.035 −3.075

0.034 0.090 0.061 0.056 0.108 −0.074 −2.099

0.086 0.039 0.045 0.136 0.041 0.045 2.543 −12.812

0.037 0.015 0.015 0.045 0.225 −0.188 −1.758 9.223

t−1

t

0.354 0.039 0.022 0.099 0.051 0.303 2.674 −12.805

0.084 0.021 0.017 0.015 0.039 0.045 1.787 −7.253

t+1

t+ 2

0.018 0.015 0.014 0.012 0.014 0.003 2.851 25.428

0.011 0.014 0.013 0.012 0.010 0.001 2.301 15.850

t+1

t+ 2

Earnings

Acc. low 1 2 3 4 Acc. high 5 Low–high t-Statistic Test for equality

t−2 −2.524 0.033 −0.014 −0.243 −0.350 −2.174 −8.610 −5.648

t−1 −2.010 −0.064 0.035 −0.435 −0.092 −1.918 −6.870 −7.591

t −2.442 0.043 0.041 0.038 0.110 −2.552 −18.864 −10.134

−1.058 0.042 0.043 0.036 −0.221 −0.837 −5.937 −9.267

−0.714 0.053 0.051 0.043 −0.018 −0.696 −4.039 13.598

Sales growth

Acc. low 1 2 3 4 Acc. high 5 Low–high t-Statistic Test for equality

This paper finds that this accrual effect is more robust in DOWN markets. In addition, the time series of stocks' accrual values are regressed on their corresponding future returns. The results of this crosssectional regression are summarized in Table 7. The slopes of the accrual values of stocks with their future returns are negative for all UP and DOWN market states for all post-ranking periods. The result is consistent with the findings of Sloan (1996). There is no significant difference in this negative correlation between UP and DOWN states, as indicated in the test of equality. As proposed by Benko (2001), for two independent samples: X1,1, X1,2,..X1,n1 being N(μ1,σ12) distributed and X2,1, X2,2,..X1,n2 being N(μ2,σ22)distributed, the t‐statistics for testing the equality is expressed as: − −  X 1 −X 2 t ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi : σ 21 n1

σ2

ð8Þ

þ n2 2

3.3. Earnings and sales growth Table 2 illustrates the variation of earnings over time across the market states. Portfolios with low accruals ranked stocks tend to have lower average earnings. In the UP market states, the average earnings of the portfolio with the lowest accruals stock drops from period t − 2 to its lowest value in period t and then increases until period t + 2. In the same period, however, the portfolio with the highest accruals stock increases in value from period t − 1 (2.7% of total assets) to its largest value in period t (11% of total assets) and then decreases gradually until period t+ 2 (5.9%). On the other hand, in DOWN market states, the average earnings of portfolios with the highest accruals

t−1 −0.183 0.039 0.027 0.025 0.116 −0.300 −2.429 11.825

t

t+1

t+ 2

0.083 0.077 0.070 0.062 −0.464 0.548 3.681 −18.983

0.149 0.109 0.081 0.085 −0.197 0.346 2.443 −13.166

0.128 0.110 0.081 0.094 0.201 −0.072 −3.883 −0.333

increase from period t − 2 to their maximum value at period t + 2, while the earnings of the lowest accruals ranked portfolio increase from period t − 2 to their highest at period t and then decrease afterward. The difference of sales growth between lowest accruals and highest accruals portfolios remains negative in UP market state. That means the firms with high accrual earnings normally have high sales growth. It indicates the sign of earnings management. In addition, the difference of sales growth increases continuously from period t to period t + 2. The management utilizes the accrual components to sustain the growth of earnings in the UP market state. 3.4. Earnings management Table 3 shows the overall variation of characteristics of stocks over time across market states. The pattern of accruals, discretionary total accruals and discretionary current accruals gives hints of management manipulations of earnings to maintain a favorable sentiment for purchasing the company stocks. Comparing the patterns of the UP and DOWN market states, it is obvious that the managers in the UP market state generally use a higher fraction of accruals in manipulating the company earnings. In the two years before and after UP market formation, the accruals decrease from period t− 2 to their lowest value in period t. Then, in order to maintain the earnings growth, the managers make extra efforts in earnings manipulation. The accruals increase from period t + 1 to t + 2. This reflects the management's attempt to manipulate earnings so as to report positive profits and sustain good financial performance in order to avoid any drastic impact on stock prices. A similar pattern is found in DOWN markets, where the managers increase the use of accruals after period t, with accruals value increasing from −0.39 in period t to −0.23 in period t+ 2, in order to generate greater earnings growth.

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Table 3 Overall variation of characteristics of stocks over time across market states. UP state

Monthly return Earnings Accruals Operating cash flow NDTA DTA DCA

DOWN state

t−2

t− 1

t

t+ 1

t+ 2

t− 2

t− 1

t

t+ 1

t+ 2

0.017 −0.298 −0.115 −0.186 −0.085 −0.030 0.015

0.018 −0.320 −0.129 −0.192 −0.116 −0.013 0.019

0.015 −0.393 −0.180 −0.213 −0.183 0.004 0.009

0.016 −0.100 −0.128 0.028 −0.104 −0.025 0.002

0.016 0.011 −0.054 0.064 −0.028 −0.025 0.023

0.054 −0.438 −0.306 −0.133 −0.226 −0.080 −0.015

0.073 −0.543 −0.343 −0.200 −0.107 −0.236 −0.009

0.037 −0.593 −0.392 −0.201 −0.194 −0.198 0.081

0.018 −0.268 −0.186 −0.083 −0.206 0.020 0.110

0.014 −0.256 −0.232 −0.026 −0.310 0.078 0.087

The discretionary total accruals also indicate the extent of management manipulation of earnings. This variable increases from period t − 2 to t in UP market states. This shows that managers continue boosting the accrual earnings to avoid underperformance. Then, the discretionary total accruals value drops afterward, showing the difficulty of maintaining high accruals. In the DOWN market state, however, the discretionary total accruals value decreases over time beginning in period t − 2 and then increases again from period t to t + 2. This indicates that the management has used its discretion in increasing the accrual earnings to boost the earnings. The discretionary current accruals, being a proxy of earnings management, show a similar pattern. Table 4 shows the monthly return of one year ahead and earnings of the US portfolios sorted by the discretionary total accruals. The result is consistent with that of total accruals. The difference in monthly returns between the lowest and highest ranked portfolios is positive over time. The effect of mispricing in the DOWN market is more prominent. It is shown that the spread by discretionary total accruals is 3.4% in period t in the DOWN market. This reflects the strong predictive power of discretionary accruals of future return. In addition, the association of the discretionary accrual and earnings is significant higher in the DOWN state. That means the earning manipulation is more obvious in the Down market. The management is more aggressive in manipulating its accruals to increase the firm's earning figure to attract investors.

3.5.1. Stock return Times series regressions of stock returns against the accruals, sales growth and operating cash flow are conducted to examine the variation of these associations over time in different market states using the following equation: RETtþ1 ¼ Ra Accrualt þ Rs SalesGrowtht þ Ro OperatingCashflowt :

ð9Þ

Panel A of Table 5 shows the result of a regression of one year ahead return against the sales growth, operation cash flow and accruals. It is shown that, in period t of an UP market, the coefficients for accruals and operating cash flow are −0.131 (t-statistic: −5.44) and 0.198 (t-statistic: 8.14), respectively, while the slope coefficient for sales growth is 0.049 (t-statistic: 2.49). The operating cash flow has the highest predictive power. The slope for operating cash flow decreases gradually over time to 0.062 in period t + 2, while the predictive power of sales growth increases, with a slope rising to 0.069 in period t + 2. In a DOWN market, the pattern is similar but more extensive. The sales growth increases from a slope coefficient of −0.46 in period t to 0.037 in period t+ 1, while the predictive power of the accruals and operating cash flow decreases over time. 3.5.2. Earnings The one year ahead earnings are regressed against the current sales growth, operating cash flow and accruals in the following model: EARNtþ1 ¼ Ea Accrualt þ Es SalesGrowtht þ Eo OperatingCashflowt : ð10Þ

3.5. Cross-sectional regressions where: Time series regressions are conducted in order to examine the variations of associations of the stock return, earnings manipulation and various types of accruals over time.

EARN

is the earnings before extraordinary items. Other variables have been defined previously.

Table 4 Variation of one year ahead returns and earnings sorted by discretionary total accruals across market states. UP state

DOWN state

Monthly return

DTA low 1 3 DTA high 5 Low–high t-Statistic Test for equality

t−2

t−1

t

t+1

t+2

t−2

0.031 0.013 0.018 0.014 4.126

0.024 0.012 0.023 0.001 0.324

0.020 0.012 0.020 0.001 0.072

0.022 0.015 0.015 0.006 4.454

0.018 0.014 0.017 0.002 1.879

0.028 0.213 0.061 −0.033 −1.969 6.810

t−2

t−1

t

t+1

t+2

t−2

−0.589 0.049 0.078 −0.668 −4.524

−2.521 0.025 0.043 −2.564 −3.913

−1.052 0.011 −1.833 0.782 1.854

−0.341 −0.025 −0.486 0.145 2.244

−0.084 0.041 −0.092 0.008 0.233

−1.073 0.004 −1.550 0.476 0.970 −5.690

t−1

t

t+ 1

t+ 2

0.079 0.016 0.045 0.034 1.373 −3.383

0.017 0.013 0.015 0.002 0.998 6.817

0.011 0.013 0.011 0.001 0.454 2.217

t−1

t

t+ 1

t+ 2

−0.817 0.029 −0.841 0.024 0.142 −27.107

−1.562 0.041 −0.514 −1.048 −4.223 16.633

−0.062 0.041 −1.181 1.120 5.043 −10.721

−0.034 0.044 −0.292 0.257 1.955 −4.629

0.037 0.044 0.284 −0.247 −2.594 6.377

Earnings

DTA low 1 3 DTA high 5 Low–high t-Statistic Test for equality

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349

Table 5 Regression study of one year ahead return against sales growth, operating cash flow and accruals. Panel A: one year ahead stock return (dependent variable)

Sales growth t-Stat Accruals t-Stat Operating cash flow t-Statistic

t−2

t− 1

t

t+ 1

t+2

t−2

t−1

t

t+1

t+ 2

0.052 2.587 −0.094 −3.021 0.354 14.553

0.054 3.093 −0.078 −3.032 0.242 9.745

0.049 2.499 −0.131 −5.437 0.198 8.139

0.049 2.558 −0.111 −4.850 0.122 3.709

0.070 4.496 −0.097 −4.312 0.062 1.916

0.002 0.146 0.041 2.748 −0.286 −2.940

0.0001 0.007 0.014 1.371 −0.085 −1.256

−0.465 −1.784 −0.053 −2.007 0.019 1.848

−0.017 −2.866 0.058 2.026 −0.080 −6.423

0.038 1.247 −0.004 −1.395 −0.018 −1.644

Panel B: one year ahead earnings (dependent variable)

Sales growth t-Stat Accruals t-Stat Operating cash flow t-Statistic

t−2

t− 1

t

t+ 1

t+2

t−2

t−1

t

t+1

t+ 2

0.032 0.890 0.732 5.208 1.103 5.746

0.074 1.755 0.599 4.139 1.754 6.741

0.023 0.544 0.597 4.226 2.263 7.645

0.004 0.093 0.672 5.068 2.384 8.388

0.093 1.805 0.598 4.457 2.306 8.013

0.041 0.296 −0.313 −3.649 5.960 7.273

0.119 1.024 −0.200 −2.256 2.520 3.068

0.562 4.173 −0.206 −3.014 0.172 4.246

0.453 3.445 −0.067 −3.152 0.095 3.889

0.028 1.409 −0.003 −0.865 0.070 2.738

EARNtþ1 ¼ EDCA DCAt þ Eo OperatingCashflowt þ ENDTA NDCAt :

Panel B of Table 5 shows that while the three variables are positively associated with the future earnings in the UP market state, only accruals and operating cash flow are significantly related with future earnings. The coefficient of accrual decreases from 0.73 in period t − 2 and remains steady at around 0.6 in periods t − 1 and t. The slope then increases to 0.67 in period t + 1. This reflects the increase of the importance of accruals in predicting the future earnings in UP markets. The coefficient of operating cash flow increases continuously from 1.10 in period t − 2 to 2.30 in period t + 2. In the DOWN market, both the sales growth and operating cash flow are positively associated with future earnings. The coefficient of sales growth increases from 0.12 in period t − 1 to 0.56 in period t and 0.45 in period t + 1. This shows the importance of the predictive power of sales growth in a DOWN market. Accrual becomes less and less associated with future earnings, as indicated by the decline of its coefficient of slope over time. The negative coefficient value of accruals reflects the characteristic of low persistence of accrual component with the future earnings. The higher accruals firms are more likely to have their earnings declined in the future. The regressions of future earnings on the discretionary total accruals and discretionary current accruals are also tested using the following expressions: EARNtþ1 ¼ EDTA DTAt þ Eo OperatingCashflowt þ ENDTA NDTAt

ð12Þ

Table 6 summarizes the regression results of the above two models. The findings are basically consistent with the results for accruals. It is shown that both the discretionary total accruals and discretionary current accruals are significantly and positively associated with future earnings in the UP state. The coefficient for the discretionary total accruals remains at 0.40 or above over the five years and decreases gradually from 0.65 in period t − 2 to 0.40 in period t + 2 in the UP market state. The coefficient of discretionary current accruals also gradually declines over time. The use of discretionary accruals in manipulating the earnings is less effective, however, in DOWN markets. The discretionary accruals are negatively associated with the future earnings in the DOWN state. This indicates that accrual component is less persistent. Thus, a high discretionary accruals value may not lead to higher future earnings in DOWN market states. 3.5.3. Analysis of accrual anomaly over years and across states at industry level Table 7 illustrates the variation of accruals and accrual return at the industry level over time. In period t, the technology industry (UP: −0.66, DOWN: −1.06), oil and gas (UP: −0.29, DOWN: −1.15) and healthcare industries (UP: −1.28, DOWN: −1.17) have lower accruals, while consumer services (UP: −0.07, DOWN: −0.08),

ð11Þ

Table 6 Regression study of one year ahead earnings against discretionary total accruals, non‐discretionary total accruals and operating cash flow. UP state

DOWN state

Panel A: one year ahead earnings (dependent variable)

DTA t-Stat NDTA t-Stat Operating cash flow t-Statistic

t− 2

t−1

t

t+ 1

t+2

t−2

t−1

t

t+1

t+ 2

0.649 7.913 2.871 3.068 1.354 5.549

0.531 6.108 3.565 3.495 1.888 6.661

0.526 6.163 3.841 3.859 2.363 7.815

0.523 6.429 4.249 4.380 2.495 8.633

0.403 4.730 4.328 4.471 2.388 8.098

−0.139 −1.215 3.571 1.699 5.054 6.589

−0.294 −2.510 −0.095 −0.975 2.170 2.949

−0.543 −7.156 0.630 4.174 −0.068 −1.221

−0.160 −2.065 0.660 4.599 −0.115 −2.521

0.138 4.276 0.130 1.427 0.039 1.208

Panel B: one year ahead earnings (dependent variable)

DCA t-Stat NDCA t-Stat Operating cash flow t-Statistic

t− 2

t−1

t

t+ 1

t+2

t−2

t−1

t

t+1

t+ 2

0.086 7.091 1.040 3.089 0.893 3.302

0.168 6.464 0.497 0.552 1.427 4.667

0.671 3.609 0.770 0.850 2.107 6.086

0.332 1.461 0.149 0.167 2.487 8.360

−0.127 −0.392 −2.145 −1.943 2.411 7.983

2.217 2.343 −0.863 −0.207 5.682 6.768

−0.202 −0.155 −0.375 −0.222 2.889 3.070

−3.780 −3.613 −7.854 −2.054 −0.204 −1.888

−2.252 −2.151 −7.739 −2.057 −0.198 −1.801

−0.053 −6.152 3.739 1.737 −0.007 −0.068

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Table 7 Overall variation of accrual across state at industry level. Accruals

Basic materials Consumer goods Consumer services Healthcare Industrials Oil and gas Technology

UP state

DOWN state

t−2

t− 1

t

t+1

t+2

t−2

t−1

t

t+1

t+ 2

−0.052 −0.027 −0.059 −0.837 0.066 −0.085 −0.630

−0.065 −0.015 −0.064 −0.971 0.065 −0.092 −0.702

−0.187 −0.015 −0.071 −1.284 0.051 −0.297 −0.661

−0.063 −0.027 −0.072 −1.545 0.007 −0.567 −0.668

−0.066 −0.051 −0.073 −1.522 −0.064 −0.575 −0.736

−0.098 −0.072 −0.090 −2.720 −0.138 −1.794 −0.215

−0.120 −0.179 −0.086 −2.297 −0.138 −1.895 −0.514

−1.195 −0.179 −0.085 −1.171 −0.091 −1.145 −1.066

−2.338 −0.131 −0.095 −0.220 −0.059 −0.099 −1.037

−2.338 −0.038 −0.091 −0.153 −0.050 0.008 −0.706

consumer goods (UP: −0.015, DOWN: −0.17) and industrials (UP: 0.05, DOWN: −0.09) have higher accruals in both UP and DOWN market states. One of the possible reasons for this finding is that industries like oil and gas belong to heavy industry and have a higher proportion of long-term assets and hence a higher depreciation expense relative to the total assets. This leads to relatively lower accrual value compared with other industries like consumer services. Table 8 illustrates the future return of portfolio of each industry sorted by accrual value. Industries of basic materials, consumer goods, consumer services, healthcare, industrials, oil and gas and

technology exhibit the accrual anomaly in both UP and DOWN markets. Among them, companies in the industries of technology, healthcare and consumer goods yield the highest accrual profits in period t (technology: 9.7%, healthcare: 7.7%, consumer goods: 2.5%) in UP market states, while healthcare, oil and gas and consumer goods generate the highest accrual profits in DOWN markets in period t (healthcare: 6.0%, oil and gas: 5.6%, consumer goods: 4.1%). In addition, it is shown that the accrual profits decrease gradually from period t − 2 to t + 2 in both states. They all exhibit positive accrual profits.

Table 8 Overall variation of accrual hedge return across state at industry level. UP state t+1

t+2

t−2

t− 1

t

t+1

t+ 2

0.077 0.007 0.009 0.068 1.093

0.019 0.008 0.006 0.014 3.981

0.016 0.005 0.009 0.007 3.385

0.013 0.012 0.008 0.004 3.519

0.053 0.011 0.017 0.036 1.654

0.037 0.010 0.014 0.024 2.526

0.034 0.008 0.009 0.025 3.846

0.031 0.011 0.011 0.021 7.543

0.025 0.016 0.009 0.016 6.305

0.027 0.011 0.014 0.013 4.161

0.029 0.019 0.020 0.009 3.251

0.026 0.014 0.016 0.010 5.072

0.029 0.015 0.016 0.013 8.729

0.025 0.014 0.013 0.012 6.769

0.241 0.018 0.037 0.204 3.139

0.334 0.025 0.020 0.314 4.587

0.093 0.027 0.015 0.077 4.125

0.091 0.041 0.029 0.062 3.782

0.051 0.023 0.016 0.035 4.761

0.014 0.011 0.013 0.001 0.611

0.008 0.012 0.016 −0.009 −8.863

0.014 0.012 0.010 0.004 3.519

0.022 0.015 0.012 0.009 5.196

0.020 0.014 0.014 0.007 4.010

0.037 0.016 0.016 0.021 3.622

0.037 0.013 0.014 0.023 2.634

0.026 0.011 0.017 0.010 2.162

0.020 0.018 0.017 0.003 1.166

0.028 0.027 0.015 0.013 2.977

0.129 0.028 0.022 0.107 1.268

0.121 0.016 0.015 0.105 1.444

0.116 0.022 0.019 0.097 1.547

0.065 0.031 0.023 0.042 5.439

0.044 0.026 0.018 0.025 5.525

0.099 0.016 0.013 0.086 4.666 −12.99 0.038 0.023 0.012 0.026 2.845 −6.57 0.016 0.017 0.010 0.006 3.665 3.95 0.107 0.242 0.020 0.087 3.989 1.35 0.047 0.016 0.021 0.026 1.156 −2.38 0.041 0.018 0.018 0.023 2.108 6.22 0.093 0.006 0.025 0.068 2.531 −1.15

2.183 0.024 0.015 2.168 3.727 −0.17 0.065 0.018 0.015 0.049 2.505 −6.77 0.019 0.016 0.015 0.004 1.426 3.31 0.093 0.021 0.003 0.090 3.128 5.43 0.044 0.015 0.019 0.025 4.137 −4.97 0.194 0.020 0.034 0.160 4.530 −7.16 0.033 0.008 0.024 0.009 1.540 14.14

1.700 0.027 0.018 1.681 2.496 −9.06 0.054 0.029 0.013 0.041 4.385 6.01 0.032 0.024 0.019 0.014 4.266 −3.30 0.087 0.037 0.027 0.060 3.946 −0.95 0.043 0.026 0.021 0.022 3.695 −12.37 0.098 0.048 0.043 0.056 6.756 −3.23 0.054 0.047 0.031 0.024 1.846 11.67

0.056 0.021 0.023 0.033 4.323 −13.57 0.022 0.017 0.009 0.012 3.599 0.79 0.021 0.013 0.021 0.000 0.107 4.51 0.013 0.018 0.013 0.000 −0.005 7.47 0.019 0.020 0.021 −0.002 −0.592 5.76 0.063 0.051 0.035 0.029 3.598 −0.80 0.049 0.033 0.015 0.034 1.997 4.52

0.030 0.023 0.022 0.009 1.847 −5.92 0.010 0.008 0.006 0.003 1.415 1.53 0.010 0.021 0.019 −0.009 −3.742 10.82 0.023 0.005 0.004 0.019 2.931 −5.25 0.015 0.019 0.020 −0.005 −4.646 16.51 0.059 0.034 0.029 0.030 2.841 6.77 0.003 0.007 0.022 −0.019 −4.777 20.69

Month return

t− 2

Basic materials

Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality Acc. low 1 3 Acc. high 5 Low–high t-Statistic Test for equality

0.037 0.009 0.011 0.026 0.973

Consumer goods

Consumer services

Healthcare

Industrials

Oil and gas

Technology

DOWN state t

Industry

t−1

S.Y.M. Ze-To / Advances in Accounting, incorporating Advances in International Accounting 28 (2012) 344–352 Table 9 Average monthly accrual return across business cycles. Periods of expansion

Average monthly accrual return

Periods of contraction

Average monthly accrual return

Apr 1991–Mar 2000

0.027 6.40

Aug 1990–Mar 1991

0.021 10.73 0.042 7.30 0.031 10.55 0.328

Apr 2000–Nov 2001 Mean

0.016 6.61

Mean Test for equality

351

Table 10 Returns for portfolios sorted by macroeconomic factor predicted returns and then accrual hedge profits across market states (3 year horizon). Quintiles based on predicted returns Panel A: average monthly returns across 3-years UP markets High 5 Acc. low 1 2 3 4 Acc. high 5 Low–high t-Stat (low–high)

3.6. Accruals mispricing and the business cycle

4

0.0414 0.0311 0.0290 0.0319 0.0385 0.0028 1.51

3 0.0166 0.0154 0.0151 0.0156 0.0128 0.0038 8.13

2

0.0108 0.0093 0.0099 0.0097 0.0059 0.0049 8.68

Low 1 0.0061 0.0059 0.0062 0.0052 0.0026 0.0034 5.63

−0.0033 −0.0013 −0.0003 −0.0015 −0.0062 0.0030 4.40

Panel B: average monthly returns across 3-years DOWN markets

The relationship of the accrual anomaly and business cycles is examined, following the definition of periods of expansion and contraction determined by NBER. The NBER's Business Cycle Dating Committee measures the business cycles and announces the beginning or the end of expansions and recessions. The sample is classified into expansionary and contractionary periods accordingly, and the change of accrual hedge profits across each of these stages is analyzed. Table 9 shows the average monthly accrual hedge profits over different periods of expansion and contraction. The results suggest that the average accrual returns are positive in both the expansionary and contractionary periods. Three expansionary and four contractionary periods are measured from 1991 to 2001. The contractionary periods under study are relatively shorter in duration, but the accrual hedge profits are higher. Overall, the average monthly accrual hedge returns for expansionary and contractionary periods are 0.16% and 0.31%, respectively. However, the t-statistic for the test of equality indicates that there is no significant difference in returns between the two periods. Hence, the accrual anomaly cannot be fully explained by the business cycle. 3.7. Macroeconomic predictor variables and accrual anomaly The source of accrual hedge profit is explored by examining the degree of importance of macroeconomic and firm-specific factors in contributing to the accrual anomaly. The test used by Chordia and Shivakumar (2002) is conducted to examine whether a macroeconomic factor model can explain the accrual hedge returns. Chordia and Shivakumar (2002) predict stock returns using macroeconomic variables of the lagged market dividend yield (DIV), default yield (DEF), defined as the difference of yield between BAA-rated and AAA-rated bonds, average yield of three-month T-bills (YLD) and term spread (TERM) measured by the yield spread between ten-year Treasury bonds and three-month T-bills. These factors are related to the business cycle. The return is forecasted using the following regression: Rkt ¼ ak0 þ ak1 DIVt−1 þ ak2 DEFt−1 þ ak3 YLDt−1 þak4 TERMt−1 :

ð15Þ

The parameters are estimated on a monthly basis by regressing with the returns of the previous 60 months. The one month ahead return for each stock is then predicted using the estimated parameters and is compounded to 12-month values. To examine if such a macroeconomic factor model can capture the accrual anomaly across market states, this paper adopts the method proposed by Cooper et al. (2004) to conduct a two-way sort. The stocks are first sorted by their predicted returns into quintiles. Each quintile portfolio is further sorted based on its accrual values. Table 10 presents the average monthly accrual hedge profits after adjusting the predicted returns. Panels A and B indicate that the predicted component of returns generated by the macroeconomic model cannot fully explain the accruals mispricing. All average monthly profits by buying stocks of lowest accruals and selling stock of highest

Acc. low 1 2 3 4 Acc. high 5 Low–high t-Stat (low–high) Test for equality

High 5

4

3

2

Low 1

0.1111 0.0397 0.0340 0.0383 0.0526 0.0585 5.14 −0.40

0.0279 0.0228 0.0224 0.0205 0.0221 0.0059 3.93 −0.11

0.0196 0.0166 0.0158 0.0137 0.0152 0.0044 4.02 0.03

0.0164 0.0117 0.0119 0.0099 0.0088 0.0076 5.81 −0.24

0.0143 0.0103 0.0064 0.0075 0.0095 0.0048 2.82 −0.08

accruals exceed 0.28% for UP markets and 0.44% for DOWN markets. They are significantly different from zero. When the average monthly profits are compared for UP and DOWN market states, there is no significant difference as indicated by the test of equality. Hence, the market states have little influence on the results under test. 4. Conclusion This paper contributes to the literature by investigating the accrual anomaly in four aspects. First, the study follows a hedge strategy proposed by Sloan (1996) of buying stocks with low accruals and selling those with high accruals. The paper examines if the level of persistence of accrual components varies with the market sentiment reflected from the stages of market states. The UP market state exhibits a high state of optimism and confidence of investors toward the earnings growth of the company and economic outlook, while the DOWN market state shows the increase of pessimism and annoyance of the market toward gloomy business prospects. This paper hypothesizes that the market states would influence the extent of persistence of accruals and hence the profitability of the accrual hedge strategy. Therefore, during the DOWN market state, investors who are pessimistic to the future economic growth would hastily search for those firms maintaining high earnings that could normally be attained by using higher accrual earnings. Hence, the market overprices the accruals further. In the UP market state, however, most investors are optimistic toward the growth of the economy and are in a market with a good supply of firms with high earnings. The overpricing of accrual is therefore less severe. It is found from the study that the accrual hedge profits are relatively higher in DOWN markets. This supports my hypothesis that the market overpricing of the abnormal accruals varies with the market state. Second, the variation of earnings manipulation in different market states is examined. Sorting procedures are performed for accruals, discretionary accruals with earnings and returns before and prior to the UP and DOWN markets, and cross-sectional regressions of earnings and returns against accrual components are formulated. The evidence reflects management manipulation of earnings in both the UP and DOWN market states. However, in the DOWN state, the future earnings of the firms are negatively associated with their accruals. This indicates that the accrual components of earnings are less persistent in the DOWN state. There are signs that the management strives to manipulate the accrual earnings, such as by increasing the accounts

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receivable and inventory so as to attract the investors. The earnings are improved, but the sales growth cannot be sustained in the year of the DOWN state. The earnings quickly deteriorate in the following years. Third, this paper also implements the study of variation of the accrual effect at the industry level across market states. Instead of a uniform effect of accruals at the industry level, it is shown that the accrual anomaly varies across industries. It seems that the nature of business affects the accounting methods and the use of accrual components, like inventory and accounts payable, varying their accrual effects. Comparing the accruals mispricing, it is shown that the accrual returns are still generally higher in DOWN states. This paper also examines the accrual profits in the stages of the business cycle announced by NBER. The findings indicate that the accrual hedge returns are relatively higher in contractionary than expansionary periods. Finally, this paper explores the role of a rational pricing model for explaining the accrual hedge profits by applying a four-factor macroeconomic pricing model used by Chordia and Shivakumar (2002) to predict the stock returns. Our findings suggest that the accrual hedge profits in both market states cannot be fully captured by the set of macroeconomic variables related to the business cycle. References Ahmed, A. S., Nainar, S. M. K., & Zhang, X. F. (2006). Further evidence on analyst and investor misweighting of prior period cash flows and accruals. The International Journal of Accounting, 41, 51–74. Barberis, Nicholas, Huang, M., & Santos, T. (2001). Prospect theory and asset prices. Quarterly Journal of Economics, 116, 1–53. Benko, M. (2001). Testing the equality of means and variances across populations and implementation in XploRe. Wirtschaftwissenschaftliche Fakultät Humboldt Universität zu Berlin (2001, March). Chan, K., Chan, K. C., Jegadeesh, N., & Lakonishok, J. (2006). Earnings quality and stock returns. Journal of Business, 79(31), 1041–1082.

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