Earnings management and abnormal returns - SSRN

Accounting and Finance 43 (2003) 1–19

Earnings management and abnormal returns: Evidence from the 1970 –1972 Price Control Regulations

Blackwell 108 Accounting ACFI © 0810-5391 1M 43 ORIGINAL ??? XXXXXXXXXXXXXXXX 025 0G raphicraft arch The Cowley Accounting 2003 Publishing Limited, & Road, ARTICLE Finance Association Oxford Hong Ltd OX4 Kong of1JF Australia and 350and Main New Street, Zeland, Malden 2002 MA 02148, USA.

Robert G. Bowman, Farshid Navissi School of Business and Economics University of Auckland, Private Bag 92019, Auckland, New Zealand

Abstract We examine the association between abnormal returns and earnings management in the context of price control regulations to test the construct validity of the earnings management model. Abnormal returns are used as a market-based measure, and discretionary accruals are employed to measure earnings management. Our results support the hypotheses that (1) price control regulations affect firms’ security prices negatively, (2) firms make income-decreasing discretionary accruals to increase the likelihood of price increase approval, and (3) firms that are affected most negatively by the regulations manage earnings more aggressively. We conclude that the earnings management model we use in this study is capable of predicting opportunistic discretionary accruals. Key words: Earnings management; Discretionary accruals; Regulation; Price control JEL classification: M4, L50, L65

1. Introduction This study investigates the relation between discretionary accruals as a measure of earnings management and abnormal returns as a market-based measure. We argue that in certain circumstances the abnormal returns can predict earnings management. This relation, if any, can be helpful in assessing the specifications of the earnings management model. For instance, if security returns (as a market-based measure) motivate earnings management, an appropriately specified model of discretionary accruals should generate evidence consistent with this prediction.

We are grateful to the Editor, Margaret Abernethy, Associate Editor, Donald Stokes, and the two anonymous reviewers for their insightful comments. We also thank research seminar participants at the University of Auckland for their valuable comments. © AFAANZ, 2003. Published by Blackwell Publishing.

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R. G. Bowman et al. / Accounting and Finance 43 (2003) 1–19

Because price regulation affects security prices negatively1 and also because earnings management can increase the likelihood of a price increase approval, firms affected more negatively by the price regulation would have stronger incentives for more aggressive earnings management. Prior research (e.g. Jones, 1991; Cahan et al., 1997; Navissi, 1999) has examined earnings management in response to regulatory events but has not addressed the possible relation of earnings management with security returns. This study examines such a link. We argue that in addition to being capable of identifying discretionary accruals, a well-specified earnings management model should be able to generate more discretionary accruals for firms with stronger incentives for earnings management. Recently, studies such as Guay et al. (1996) and Bernard and Skinner (1996) have challenged the credibility of the extant earnings management models. They argue that the current accrual-based models are not well specified and therefore are not capable of isolating discretionary accruals. The link between abnormal returns and discretionary accruals, in certain settings, is one way of testing the overall validity of the earnings management models. Specifically, we examine the introduction of price regulation in New Zealand and its subsequent relaxation. We first conduct an event study to compute abnormal returns around the announcements of regulatory changes. We next conduct an earnings management test to identify discretionary accruals. We then relate earnings management to abnormal returns to test the hypothesis that firms affected more negatively by the price regulation are more likely to manage earnings more aggressively. To increase the external validity of the study we employ and test a control sample of non-manufacturing firms, which were subject to a profit margin regulation but could not apply for price increases under any of the regulatory changes of 1970–1972. In addition to the tests of association between discretionary accruals and security returns, we relate non-discretionary accruals to security returns. If the earnings management model can identify discretionary accruals accurately, we should not observe a systematic relation between non-discretionary accruals and security returns. The remainder of this study is organised as follows. In section 2 we discuss the regulatory background of the price control regulation in New Zealand. Section 3 describes the data. We hypothesise an association between discretionary accruals and security returns and develop models in section 4. Section 5 reports descriptive statistics and the results of our tests. In section 6 the results from robustness tests are reported, and we summarise the paper in section 7. 2. Background and design of the study We examine the introduction of price control regulation in New Zealand in 1970 and subsequent changes to the regulation in 1971 and 1972. A price freeze

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For example see Bowman et al. (2000), and Navissi et al. (1999).

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regulation (PFR) was introduced in New Zealand in November 1970 (The New Zealand Herald, November 16, 1970, p. 1). The government announced that a new regulation would be released in January 1971, which would allow manufacturing firms to apply for price increases. In January 1971 the government issued the Price Justification Scheme (PJS ) and allowed only manufacturers of basic commodities (referred to as First Schedule firms) to apply for price increases (The New Zealand Herald, January 12, 1971, p. 3). A price increase approval was to be granted if First Schedule firms could provide evidence of financial hardship. Financial hardship was defined as insufficient earnings to allow a sustainable business. In March 1972 the government issued the Stabilisation of Prices Regulations (SPR). The SPR continued the right to apply for price increases for First Schedule firms (The New Zealand Gazette, Stabilisation of Prices Regulations, March 1972, 167–185). That is, First Schedule firms could apply for price increases for the second time. The SPR also allowed other manufacturing firms (referred to as Second Schedule firms) to apply for and be granted a price increase if they could meet the ‘financial hardship’ criteria. Specifically, the regulatory setting of this study includes introduction of three regulations as follows: a. Introduction of Price Freeze Regulation (PFR) in November 1970 This regulation covered the prices of all goods and services. The regulation stated that a new regulation in January 1971 would allow manufacturing firms to apply for price increases. The perception, therefore, was that all manufacturing firms would have the opportunity to apply for price increases. b. Introduction of Price Justification Scheme (PJS) in January 1971 This regulation allowed only manufacturers of basic commodities (First Schedule firms) to increase their prices if they could provide evidence of financial hardship. This was in contrast to the expectation that all manufacturing firms would be permitted to apply. c. Introduction of Stabilisation of Prices Regulation (SPR) in March 1972 This regulation extended the right to apply for price increases to First Schedule firms (these firms could apply for price increases for the second time) and for the first time allowed manufacturers of non-basic commodities (Second Schedule firms) to apply for price increases using the financial hardship criteria. We expect that the November 1970 PFR would impact security prices negatively. We first examine the security price reaction of treatment firms in response to the 1970 PFR by testing market-adjusted returns around this event. The January 1971 PJS did not allow Second Schedule firms to apply for price increases. We compute market-adjusted returns for Second Schedule firms around the introduction of the 1971 PJS. Abnormal returns of Second Schedule firms around both the November 1970 and the January 1971 events are combined to determine the overall effect of the regulations on Second Schedule © AFAANZ, 2003

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firms. First Schedule firms’ abnormal returns are only expected to be negative around the November 1970 event. The event window for each regulatory change includes one week surrounding the announcement date of the regulation for computing market-adjusted returns. For each event, we review all media discussion around the event to determine the extent of anticipation. The object is to keep the event window as short as possible while at the same time including as much as possible of the reaction to the event within the event window. We believe the event windows chosen are the best trade off of these conflicting objectives and are sufficiently wide to overcome problems associated with the misspecification of event dates. We argue that because First Schedule firms could obtain price increase approval based on financial hardship criteria in 1971 and 1972, they would have incentives to manage earnings in both years. Second Schedule firms would have incentives for earnings management only in 1972 when they were entitled to apply for price increases. We argue that the magnitude of the earnings management by First Schedule firms (combined in 1971 and 1972) depends on their abnormal returns around the November 1970 regulation. The magnitude of earnings management by Second Schedule firms in 1972 depends on their combined abnormal returns in response to the November 1970 PFR and the January 1971 PJS events. Overall, we expect more aggressive earnings management by firms whose stock price has been hit hardest by the regulations.2 3. Data 3.1. Treatment samples Initially, we select 103 manufacturing firms listed on the New Zealand Stock Exchange from the 1971 Official Record of Stock Exchanges of New Zealand (ORSENZ) and collect data from the University of Otago Data Base and the Francis, Allison, Symes Company Review (1958–72). Because the focus of the regulations was on manufacturing firms, the treatment sample includes only manufacturing firms. Firms must have sufficient accounting information for computations of discretionary accruals as well as data required for computing market-adjusted returns. We identify 85 firms that meet the above criteria. Next, we require each firm to have at least 11 years of time-series data for estimation of non-discretionary accruals. This requirement reduces the treatment sample to

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It can be argued that the earnings management observed may be due to the poor operating performance of firms as opposed to purposeful manipulation of earnings. Jones (1991) refers to this issue as a factor that might have affected her results. Although the setting of this paper is different from Jones, we report detailed descriptive statistics on firms’ earnings, cash flows and total accruals to ensure that the results of this study are not driven by firms’ poor operating performance.

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29 First Schedule and 26 Second Schedule firms for which we can compute both abnormal returns and discretionary accruals. 3.2. Control sample The New Zealand price freeze regulation provides an opportunity for strengthening the research design of this study by selecting a control sample of non-manufacturing firms. The price freeze regulation of 1970 affected firms in the control sample negatively. The results of the tests (not reported in the paper) indicate that these firms experienced negative reaction ( p-value = 0.000) in response to the November 1970 PFR. These firms were subject to a profit margin regulation which did not allow them to apply for price increases under any of the 1971 and 1972 regulatory changes3. Thus, they had no direct incentives for earnings management. Overall, 29 listed non-manufacturing firms (excluding banking, insurance and finance firms) satisfied the criteria for inclusion in the control sample. 4. Research design 4.1. Hypotheses In the setting that we study, we expect that the PFR imposed costs on the sample firms by reducing revenues. However, subsequent legislation permitted firms to provide evidence of financial hardship in support of requests for price increases. If a firm can successfully manage earnings downward to show a financial hardship, it can increase the probability of obtaining a price increase approval and thus improve profitability and cash flows. The harder a firm is hit by the 1970 and 1971 regulations the more incentive it would have for earnings management. Therefore, we expect that the wealth effects of these regulatory changes would explain the firms’ earnings management in a subsequent period. Finally, we expect that the adverse wealth effects would serve as predictors of the earnings management to follows. We test the following sequence of three hypotheses: H1: First and Second Schedule firms subject to the November 1970 PFR and Second Schedule firms subject to the January 1971 PJS will suffer wealth losses. H2: First (Second) Schedule firms will make income-decreasing discretionary accruals in 1971 and 1972 (1972) when they can apply for price increases.

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Under the profit margin regulation, an increase in the output price of a non-manufacturing firm is only allowed when there is an increase in the firm’s input (purchase) price charged by the suppliers (manufacturers). The price increase should not result in an increase in the profit margin. © AFAANZ, 2003

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H3: Firms experiencing more negative abnormal returns in response to the November 1970 PFR and the January 1971 PJS will make more aggressive income-decreasing discretionary accruals in the year(s) they can apply for price increases. To test these hypotheses we need to compute market-adjusted returns and discretionary accruals (earnings management). The next section discusses the models we employ to produce market-adjusted returns and discretionary accruals. 4.2. Models To compute market adjusted returns (AR) we use the following model4: ARi,t = Ri,t − Rm,t

(1)

where ARi,t = market adjusted return for firm i in time t, Ri,t = security return for firm i in time t, and Rm,t = market return in time t. For Second Schedule firms, the market adjusted returns are pooled ( Σ AR) across the November 1970 PFR and the January 1971 PJS event times as follows: Σ ARi,n&j = ARi,n + ARi, j

(2)

where Σ ARi,n&j = pooled market adjusted returns for (Second Schedule) firm i across November 1970 and January 1971 event times, ARi,n = abnormal return for (Second Schedule) firm i around November 1970 PFR, and ARi, j = abnormal return for (Second Schedule) firm i around January 1971 PJS. To compute discretionary accruals (DA) we use the model suggested in Dechow et al. (1995), which is a modified version of the model developed in Jones (1991). We first model total accruals (Acct /TAt−1) for each firm. Acct /TAt−1 = (∆CAt − ∆CASHt)/TAt−1 − (∆CLt − ∆STDt )/TAt−1 − DEPNt /TAt−1 (3)

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For example see Bosch and Lee (1994), Bowen et al. (1983), Dowdell et al. (1992), and Fields et al. (1990), Navissi et al. (1999), and Bowman et al. (2000).

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where Acct TAt−1 ∆CAt ∆CASHt ∆CLt ∆STDt DEPNt

= = = = = = =

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total accruals in year t, lagged total assets, change in current assets, change in cash and cash equivalents, change in current liabilities, change in current portion of long term debt, and depreciation and amortization expense.

Next we estimate the non-discretionary accruals portion of total accruals by fitting equation 4 for each firm in the estimation period5. Consistent with Navissi (1999) we include a variable (∆CPI) to capture the impact of inflation. This variable ensures that the observed earnings management is attributed to the price controls, not the inflationary environment that caused regulators to introduce price controls. Acct /TAt−1 = β1 (1/TAt−1) + β2 (∆REVt /TAt−1) + β3 (PPEt /TAt−1) + β4 (∆CPIt ) + εt (4) where ∆REVt PPEt ∆CPIt εt

= = = =

revenues in year t less revenues in year t−1, gross property, plant and equipment in year t, percentage change in Consumers’ Price Index,6 error term in year t.

All variables in equation (4) (other than CPI, which is measured as a percentage change) are scaled by lagged total assets to reduce heteroskedasticity. The average explanatory power (adjusted R2) of the models fitted in the estimation period is 0.3298 and the average F-statistics is 7.4000. Using estimates of non-discretionary accruals from equation (4) we compute discretionary accruals (earnings management) from the prediction errors (νt) of equation (5) in the event years 1971 and 1972. νt = Acct /TAt−1 − [b1 (1/TAt−1) + b2 (∆REVt /TAt−1 − ∆RECt /TAt−1) + b3 (PPEt /TAt−1) + b4 (∆CPIt )],

(5)

where

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The time period used for estimating non-discretionary accruals ranges from 14 years to 18 years.

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Our final results are not sensitive to exclusion of this variable from the model.

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νt = prediction errors, ∆RECt = receivables in year t less receivables in year t−1, and b1, b2, b3 and b4 = estimates of coefficients from equation (4). We use the following regression model to test the hypothesis that more negative abnormal returns motivate more aggressive earnings management. We include two variables to control for size and risk:7 ΣDAi,71&72 = β0 + β1 ΣARi,n&j + β2 BE/MEi,70 + β3 LOGSIZEi,70 + εi

(6)

where ΣDAi,71&72 = discretionary accruals for firm i. First Schedule firms’ discretionary accruals are pooled across 1971 and 1972. Second Schedule firms’ discretionary accruals are only for 1972, ΣARi,n&j = market adjusted return for firm i. First Schedule firms’ returns are only for 1970. Second Schedule firms’ returns are pooled across 1970 and 1971, BE/MEi,70 = book value of equity to market value of equity for firm i in 1970, and LOGSIZEi,70 = natural logarithm of total assets for firm i in 1970. 5. Results 5.1. Descriptive statistics Tables 1, 2 and 3 report descriptive statistics on changes in earnings, total accruals, and cash flows scaled by lagged total assets for the partitioned samples (First and Second Schedule firms) from Year −5 to Year +1 relative to the year in which firms could first apply for price increases. A purpose of the descriptive statistics is to ensure that the results are not driven by the poor operating performances of the firms. For example, Jones (1991, p. 203) refers to this problem as one of the limitations of her study. She states that ‘. . . financial performance of the affected firms may be so bad that managers do not need to use accounting choices to manage earnings.’ Scaled earnings changes of First and Second Schedule firms are reported in table 1. Panels A and B show that earnings changes in Year 0 are negative for both First and Second Schedule firms and only significant for First Schedule firms. The earnings change for First Schedule firms in Year +1 is not significant. The earnings change for Second Schedule firms in Year +1 is positive and significant, which may be due to approved price increases. Overall, the

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See Fama and French (1992).

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Table 1 Scaled changes in earnings by year relative to the year in which firms could first apply for price increases Year −5

Year − 4

Year −3

Year −2

Year −1

Year 0

Year +1

Panel A: First Schedule firms Mean 0.0373 Median 0.0405 Std dev 0.0475 N 29 p-value (t-test) 0.000 p-value (Sign rank) 0.000

− 0.0063 − 0.0098 0.0390 29 0.390 0.103

0.0276 0.0030 0.1110 29 0.191 0.323

0.0147 − 0.0010 0.0525 29 0.143 0.254

− 0.0028 0.0003 0.0256 29 0.554 0.665

− 0.0214 − 0.0124 0.0633 29 0.078 0.011

− 0.0281 0.0035 0.1535 29 0.332 0.958

Panel B: Second Schedule firms Mean − 0.0030 Median − 0.0069 Std dev 0.0420 N 26 p-value (t-test) 0.710 p-value (Sign rank) 0.911

− 0.0033 − 0.0033 0.0219 26 0.448 0.234

− 0.0082 0.0008 0.0297 26 0.168 0.516

0.0074 0.0039 0.0295 26 0.213 0.255

− 0.0037 0.0038 0.0586 26 0.749 0.757

− 0.0027 0.0032 0.0476 26 0.773 0.701

0.0225 0.0147 0.0447 26 0.016 0.001

Changes in earnings are scaled by lagged total assets. First Schedule firms include manufacturers of basic commodities and Second Schedule firms include other manufacturing firms.

time-series of earnings statistics do not indicate a systematic poor performance by firms. Scaled total accruals changes are reported in table 2. Panel A reports the changes for First Schedule firms. Total accruals changes are negative and significant in Year 0. Note that this is not a refined test of earnings management, because total accruals include a non-discretionary component for which controls have not yet been made. The change in total accruals in Year +1 is not significant. The result could be due to the offsetting effects of the Year 0 reversals and/or the possible price increase approvals granted to these firms. Panel B, table 2, reports the changes in total accruals for Second Schedule firms. The change in Year 0 is income decreasing and significant. Note that the earnings change in Year 0 (as reported in table 1) was also negative. The change in Year +1 is income increasing and significant. A possible explanation for the positive change could be the reversal of Year 0 accruals that could have been further enhanced by price increase approvals obtained by these firms. Table 3 reports scaled changes in cash flows. Following Jones (1991), this study defines cash flows as earnings less accruals. Panel A indicates that the change for First Schedule firms in Year 0 is positive but not significant. The cash flow change for Second Schedule firms in Year 0 is positive and significant. Cash flow changes over the period Year −5 to Year +1 do not show evidence of systematic poor performance by First and Second Schedule firms. © AFAANZ, 2003

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Table 2 Scaled changes in total accruals by year relative to the year in which firms could first apply for price increases Year −5

Year − 4

Year −3

Year −2

Year −1

Year 0

Year +1


0.0152 − 0.0063 0.1567 29 0.604 0.792

− 0.0639 − 0.0155 0.2092 29 0.111 0.245

0.0852 0.0525 0.1845 29 0.019 0.030

− 0.0120 0.0111 0.1667 29 0.700 0.874

− 0.0648 − 0.0377 0.2493 29 0.172 0.081

0.0715 0.0025 0.2912 29 0.196 0.561

Panel B: Second Schedule firms Mean − 0.0300 Median 0.0090 Std dev 0.1602 N 26 p-value (t-test) 0.347 p-value (Sign rank) 0.544

− 0.0216 − 0.0129 0.1345 26 0.419 0.757

0.0923 0.0370 0.1614 26 0.007 0.007

− 0.0342 − 0.0094 0.1614 26 0.289 0.391

− 0.0032 − 0.0018 0.1213 26 0.893 0.872

− 0.0766 − 0.0810 0.1126 26 0.001 0.001

0.1237 0.0889 0.1805 26 0.001 0.000

Changes in total accruals are scaled by lagged total assets. First Schedule firms include manufacturers of basic commodities and Second Schedule firms include other manufacturing firms.

Table 3 Scaled changes in cash flows by year relative to the year in which firms could first apply for price increases Year −5

Year − 4

Year −3

Year −2

Year −1

Year 0

Year +1


− 0.0215 0.0029 0.1678 29 0.494 0.605

0.0915 0.0351 0.2247 29 0.036 0.070

− 0.0705 − 0.0622 0.1997 29 0.067 0.066

0.0092 − 0.0249 0.1715 29 0.774 0.824

0.0433 0.0307 0.2543 29 0.366 0.195

− 0.0942 0.0121 0.3359 29 0.141 0.941

Panel B: Second Schedule firms Mean 0.0331 Median 0.0098 Std dev 0.1519 N 26 p-value (t-test) 0.276 p-value (Sign rank) 0.419

0.0183 0.0075 0.1351 26 0.495 0.795

− 0.1006 − 0.0564 0.1643 26 0.004 0.000

0.0416 0.0440 0.1580 26 0.190 0.147

− 0.0005 − 0.0176 0.1265 26 0.984 0.683

0.0891 0.0856 0.1010 26 0.000 0.000

− 0.0832 − 0.0770 0.1893 26 0.034 0.026

Changes in cash flows are scaled by lagged total assets. First Schedule firms include manufacturers of basic commodities and Second Schedule firms include other manufacturing firms. © AFAANZ, 2003


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On the whole, the results from descriptive statistics are consistent with income-decreasing accruals in Year 0. Analysis of cash flow changes shows that they do not decline, which is indirect evidence of earnings management8. 5.2. Abnormal returns 9 Table 4 reports the results from tests of abnormal returns. Because we have selected firms for which these regulations impose price freezes, we hypothesise negative wealth effects. In table 4, column 1, the pooled abnormal returns in response to the November 1970 PFR (for First and Second Schedule firms) and January 1971 PJS (for Second Schedule firms) indicate a negative and significant response to these regulatory changes. The abnormal returns around the November 1970 PFR for First and Second Schedule firms (reported in column 2) indicate that the mean market adjusted returns is in the predicted direction and is significant. In column 3, similar negative and significant results are observed for Second Schedule firms in response to the January 1971 PJS. These results confirm our Hypothesis 1. 5.3. Earnings management Table 5 reports the results from tests of discretionary accruals for firms in the treatment sample. Prior research has used discretionary accruals as a proxy for earnings management. In this context, negative (positive) discretionary accruals indicate opportunistic income decreasing (increasing) management. Table 5, column 1, reports pooled discretionary accruals for 29 First Schedule firms in 1971 and 1972, and 26 Second Schedule firms in 1972. Note that First Schedule firms have incentives for earnings management in 1971 and 1972 whereas Second Schedule firms have incentives for earnings management only in 1972. The mean (median) of discretionary accruals is –0.7644 (−0.4130) which is income decreasing and significant at the 0.001 level. We also report separately discretionary accruals of 29 First Schedule firms in 1971 (column 2) and discretionary accruals of 29 First Schedule firms and 26 Second Schedule firms in 1972 (column 3). Discretionary accruals reported in columns 2 and 3 are income decreasing and significant at the 0.008 and 0.001 levels, respectively10.

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We also examine firms’ characteristics such as total assets, earnings per share and book value of equity across treatment and control samples. We take these figures from the year immediately prior to Year –1. The results (not reported in the paper) do not indicate any significant differences across the samples. We conclude that it is less likely that the results are driven by firms’ characteristics.

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A more extensive reporting of these results is in Bowman et al. (2000).

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The coefficients are significant only when using one-tailed tests. Since the hypotheses are directional, the use of such tests is appropriate. © AFAANZ, 2003

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Table 4 Descriptive statistics on abnormal returns (AR). In all cases the predicted sign is negative.

N Mean Median Std. dev. Minimum Maximum %pos/neg t-statistic ( p-value) Sign rank ( p-value)

(1) AR of First Schedule firms around 1970 PFR pooled with combined AR of Second Schedule firms around 1970 PFR and 1971 PJS

(2)

(3)

AR of First and Second Schedule firms around 1970 PFR

AR of Second Schedule firms around 1971 PJS

55 − 0.0222 − 0.0238 0.0660 − 0.1728 0.3465 30/70 − 3.1289 (0.002) − 903 (0.001)

55 − 0.0283 − 0.0195 0.0602 − 0.1728 0.0575 42/58 −2.3981 (0.024) − 83 (0.03)

26 − 0.0375 − 0.0267 0.0446 − 0.1390 0.0463 16/84 − 4.6079 (0.001) −183 (0.001)

To compute market adjusted returns (AR) we use the following model: ARi,t = Ri,t – Rm,t where ARi,t = market adjusted return for firm i in time t, Ri,t = security return for firm i in time t, and Rm,t = market return in time t. Pooled abnormal returns (ΣAR) are computed as follows: Σ ARi,n&j = ARi,n + ARi, j where Σ ARi,n&j = pooled market adjusted returns for (Second Schedule) firm i across November 1970 and January 1971 event times, ARi,n = abnormal return for (Second Schedule) firm i around November 1970 PFR, and ARi, j = abnormal return for (Second Schedule) firm i around January 1971 PJS.

The results from tests of discretionary accruals confirm our Hypothesis 2 that firms make income decreasing discretionary accruals in the years they can apply for price increases. The reduced earnings increase the likelihood that a price increase application will be approved for the firms. We replicate the tests of discretionary accruals for a sample of 29 control firms that could not apply for price increases. Because these firms were not allowed to increase their prices, we expect they would not have incentives for earnings management. We report the results in table 6. © AFAANZ, 2003


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Table 5 Results of tests of discretionary accruals (DA) for treatment firms (1) Combined DA of First Schedule firms in 1971 and 1972 pooled with DA of Second Schedule firms in 1972 N Mean Median Std. dev. Minimum Maximum %pos/neg t-statistic ( p-value) Sign rank ( p-value)

(2)

(3)

DA of First Schedule firms in 1971

DA of First and Second Schedule firms in 1972

55

29

− 0.7644 − 0.4130 1.2494 − 6.2740 1.1430 30/70 −5.4724 (0.001) −994 (0.001)

− 0.6972 − 0.3087 1.1272 −2.9940 1.1413 35/65 −3.1539 (0.004) −99 (0.008)

55 − 0.9002 − 0.5470 1.4542 − 6.274011 0.8510 20/80 −3.3334 (0.002) −154 (0.001)

To compute discretionary accruals, we first model total accruals (Acct /TAt −1) for each firm. Acct /TAt −1 = (∆CAt − ∆CASHt )/TAt−1 − (∆CLt − ∆ STDt )/TAt −1 − DEPNt /TAt −1 where Acct TAt −1 ∆CAt ∆CASHt ∆CLt ∆STDt DEPNt

= = = = = = =


Next we model the non-discretionary accruals portion of total accruals. For each firm we regress: Acct /TAt −1 = β1(1/TAt−1) + β2 (∆REVt /TAt −1) + β3 (PPEt /TAt−1) + β4(∆CPIt ) + εt where ∆REVt = ∆RECt = PPEt = ∆CPIt = εt =

revenues in year t less revenues in year t−1, receivables in year t less receivables in year t−1, gross property, plant and equipment in year t, percentage change in Consumers’ Price Index, error term in year t.

Using estimates of non-discretionary accruals we compute earnings management (νt ) from the following model: νt = Acct / TAt −1 − [b1(1/ TAt −1) + b2 (∆REVt / TAt −1 − ∆RECt / TAt −1) + b3(PPEt / TAt −1) + b4(∆CPIt)], where

νt = prediction errors, ∆RECt = receivables in year t less receivables in year t−1, b1, b2, b3 and b4 = estimates of coefficients from equation (4).

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To determine the sensitivity of the results to this extreme observation we replicate the tests after excluding this observation from the data set. The mean discretionary accruals, which is reduced to −0.7082, remains significant and in the same direction. © AFAANZ, 2003

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Table 6 Results of tests of discretionary accruals (DA) for 29 control firms DA of control firms in 1971 Mean Median t-statistic Sign rank

DA of control firms in 1972

0.0892

−0.1654

0.1760 0.4455 40.50

−0.2440 −0.9261 −50.50

To compute discretionary accruals, we first model total accruals (Acct /TAt −1) for each firm. Acct /TAt −1 = (∆CAt − ∆CASHt )/TAt−1 − (∆CLt − ∆ STDt)/TAt −1 − DEPNt /TAt−1 where Acct = TAt−1 = ∆CAt = ∆CASHt = ∆CLt = ∆STDt = DEPNt =


Next we model the non-discretionary accruals portion of total accruals. For each firm we regress: Acct / TAt−1 = β1(1/TAt−1) + β2 (∆ REVt / TAt−1) + β3 (PPEt / TAt−1) + β4 (∆CPIt ) + εt where ∆REVt = ∆RECt = PPEt = ∆CPIt = εt =

revenues in year t less revenues in year t−1, receivables in year t less receivables in year t−1, gross property, plant and equipment in year t, percentage change in Consumers’ Price Index, error term in year t.

Using estimates of non-discretionary accruals we compute earnings management (νt ) from the following model: νt = Acct / TAt−1 − [b1(1/ TAt −1) + b2 (∆REVt / TAt −1 − ∆RECt / TAt −1) + b3 (PPEt / TAt −1) + b4(∆CPIt )], where νt = prediction errors, ∆RECt = receivables in year t less receivables in year t−1, b1, b2, b3 and b4 = estimates of coefficients from equation (4). The statistics are not significant at the 15 per cent level or better.

The results in table 6 indicate that the control firms did not engage in significant earnings management in either 1971 or 1972. Note that we reported significant income-decreasing discretionary accruals in these years for the treatment firms. These results are not likely to be attributable to differences in the characteristics of the firms in the treatment and control samples. The results of © AFAANZ, 2003


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Table 7 Regression of pooled discretionary accruals against pooled abnormal returns. Pooled discretionary accruals refers to combined discretionary accruals of First Schedule firms in 1971 and 1972 pooled with discretionary accruals of Second Schedule firms in 1972. Pooled abnormal returns refers to combined returns of Second Schedule firms around the November 1970 PFR and the January 1971 PJS pooled with the returns of First Schedule firms around the November 1970 PFR

Intercept Σ AR LOGSIZE RISK F-statistic = 1.3220 p-value = 0.273 Adjusted R2 = 0.011

Parameter estimates

Standard error

t-statistic

p-value (One-tailed)

0.8562 4.1358 − 0.0940 − 0.1588

2.0035 2.2388 0.1203 0.2786

0.4270 1.8470 − 0.7820 − 0.5700

0.335 0.035 0.218 0.285

We use the following regression model: Σ DAi,71&72 = β0 + β1Σ ARi,n&j + β2BE/MEi,70 + β3 LOGSIZEi,70 + εi where Σ DAi,71&72 = Σ ARi,n&j = BE/MEi,70 = LOGSIZEi,70 =

pooled discretionary accruals for firm i in 1971 and /or 1972, pooled market adjusted return for firm i in 1970 and /or 1971 event periods, book value of equity to market value of equity for firm i at the beginning of 1970, and natural logarithm of total assets for firm i at the beginning of 1970.

tests of discretionary accruals for the control sample provide additional support for the construct validity of the earnings management model used in this study. 5.4. Relationship between abnormal returns and earnings management We test the main hypothesis of the study in tables 7 and 8. We hypothesise that the firms experiencing the most negative wealth effects as a result of the 1970 and 1971 regulatory changes are likely to make more aggressive incomedecreasing discretionary accruals. That is, in our setting we expect abnormal returns to predict earnings management. If the earnings management model used in the study is capable of producing reliable discretionary accruals we should observe a positive association between discretionary accruals and abnormal returns. The more negative the abnormal returns, the more aggressive the earnings management. Table 7 reports the results from the regression of pooled discretionary accruals against pooled abnormal returns, size and risk12. 12

Pooled discretionary accruals refers to combined discretionary accruals of First Schedule firms in 1971 and 1972 pooled with discretionary accruals of Second Schedule firms in 1972. Pooled abnormal returns refers to combined returns of Second Schedule firms around the November 1971 PFR and the January 1971 PJS pooled with abnormal returns of First Schedule firms around the November 1970 PFR. © AFAANZ, 2003

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Table 8 Separate regressions of discretionary accruals (DA) against abnormal returns (AR) Parameter estimate

Standard error

t-statistic


Panel A: Regression of DA of First Schedule firms in 1971 against abnormal returns of First Schedule firms around the November 1970 PFR Intercept 3.0673 2.1964 1.3960 0.088 Σ AR 8.4832 3.4082 2.4890 0.010 Risk − 0.3345 0.3179 −1.0520 0.152 Size − 0.2111 0.1385 −1.5240 0.075 N = 29 firms F-statistic = 3.0050 p-value = 0.052 Adjusted R2 = 0.193 Panel B: Regression of Σ DA against Σ AR. Σ DA is discretionary accruals of 29 First and 26 Second Schedule firms in 1972. Σ AR is abnormal returns of 29 First Schedule firms around the November 1970 PFR and 26 Second Schedule firms around the November 1970 PFR and the January 1971 PJS Intercept −1.3008 4.2368 − 0.3070 0.385 Σ AR 12.5921 6.5040 1.9360 0.027 Size 0.0314 0.2510 0.1250 0.455 Risk 0.3677 0.6106 0.6020 0.276 N = 55 firms F-statistic = 1.2800 p-value = (0.302) Adjusted R2 = 0.0292 We use the following regression model: Σ DAi,71&72 = β0 + β1Σ ARi,n&j + β2 BE/MEi,70 + β3 LOGSIZEi,70 + εi where Σ DAi,71&72 = Σ ARi,n&j = BE/MEi,70 = LOGSIZEi,70 =

pooled discretionary accruals for firm i in 1971 and /or 1972, pooled market adjusted return for firm i in 1970 and /or 1971 event periods, book value of equity to market value of equity for firm i at the beginning of 1970, and natural logarithm of total assets for firm i at the beginning of 1970.

The coefficient of pooled abnormal return from the regression model is positive and significant as hypothesised. The size and risk control variables are not significant. We also test discretionary accruals in 1971 (for First Schedule firms) and in 1972 (for First and Second Schedule firms) separately and report the results in table 8. Panel A reports the results from the regression of income-decreasing discretionary accruals of year 1971 on the independent variables. The coefficient on abnormal return is again positive and significant as hypothesised. Similar results are reported in panel B where we regress income-decreasing discretionary accruals of the year 1972 on abnormal returns and control variables. All of these results support our Hypothesis 3 that there is a positive relation between abnormal returns and discretionary accruals in the setting that we © AFAANZ, 2003


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study. This indicates that the abnormal returns of firms are capable of predicting income-decreasing discretionary accruals by the affected firms in the price control environment. 6. Robustness tests In this section we report results from sensitivity tests where we regress nondiscretionary accruals on abnormal returns and control variables. If the earnings management model can accurately extract discretionary accruals from total accruals, the non-discretionary accruals should not have any systematic pattern. That is, non-discretionary accruals are expected to be insignificant and have no systematic relation with abnormal returns. To compute non-discretionary accruals, we run equation (4) for each firm during the estimation period and then compute non-discretionary accruals for each firm as follows: NDA = b1(1/TAt−1) + b2(∆REVt /TAt−1 − ∆RECt /TAt−1) + b3(PPEt /TAt−1) + b4(∆CPIt ),

(7)

where b1, b2, b3 and b4 are the estimates of coefficients from equation (4). The descriptive statistics on non-discretionary accruals (not reported in the paper) indicate a positive mean (median) of 0.0111 (0.0050), which is not significant. We then use the following model to test the association between non-discretionary accruals and abnormal returns: ΣNDAi,71&72 = β0 + β1ΣARi,n&j + β2 BE/MEi,70 + β3 LOGSIZEi,70 + εi

(8)

where ΣNDAi,71&72 = Pooled non-discretionary accruals for firm i in 1971 and/or 1972. The results reported in table 9 indicate that there is no significant association between non-discretionary accruals and abnormal returns. The control variables (size and risk) are also not significant. These results further support the robustness of our tests of the hypothesis that more negative abnormal returns motivate more aggressive earnings management. 7. Summary This study examines the use of discretionary accruals, as measured by the modified Jones model, in a high regulatory environment. We use a market-based model and examine whether wealth effects of price control regulations can predict income-decreasing discretionary accruals. We expect firms with more negative wealth effects in response to the introduction of price controls in 1970 to © AFAANZ, 2003

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Table 9 Regression of pooled non-discretionary accruals against pooled abnormal returns. Pooled nondiscretionary accruals refers to combined non-discretionary accruals of First Schedule firms in 1971 and 1972 pooled with non-discretionary accruals of Second Schedule firms in 1972. Pooled abnormal returns refers to combined returns of Second Schedule firms around the November 1970 PFR and the January 1971 PJS pooled with the returns of First Schedule firms around the November 1970 PFR Parameter Estimate Intercept Σ AR Risk Size

Standard error

t-statistic


0.1246

0.1848

0.6740

0.252

− 0.0839 − 0.0205 − 0.0074

0.0719 0.0202 0.0126

−1.1660 −1.0160 − 0.6600

0.126 0.158 0.257

F-statistic = 0.8440 p-value = 0.4809 Adjusted R2 = − 0.0148 We use the following regression model: Σ NDAi,71&72 = β0 + β1Σ ARi,n&j + β2 BE/MEi,70 + β3 LOGSIZEi,70 + εi where Σ NDAi,71&72 = ΣARi,n&j = BE/MEi,70 = LOGSIZEi,70 =

pooled non-discretionary accruals for firm in 1971 and /or 1972, pooled market adjusted return for firm i in 1970 and /or 1971 event periods, book value of equity to market value of equity for firm i at the beginning of 1970, and natural logarithm of total assets for firm i at the beginning of 1970.

be more aggressive with their income-decreasing earnings management. This is because lower profit increases the probability that a firm’s price increase application would be approved. Our results support a series of related hypotheses. The imposition of price control regulations has negative wealth effects on the affected firms. Firms negatively impacted by the regulations will tend to engage in earnings management through the use of discretionary accruals. The abnormal returns that measure the wealth impact of the imposition of price controls will predict the subsequent use of earnings management by the firms. We conducted a number of robustness tests. We identified a sample of 29 non-manufacturing firms that were not allowed to apply for price increases and tested them against the treatment sample and for a relationship between their abnormal returns and earnings management. We also conducted sensitivity tests using non-discretionary accruals for the treatment firms. All of our tests support the robustness of our results. References Bernard, V. and D. Skinner, 1996, What motivates managers’ choice of discretionary accruals?, Journal of Accounting and Economics 22, 313–325. © AFAANZ, 2003


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