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Corporate disclosure quality, earnings smoothing, and earnings' timeliness
Journal of Business Research 56 (2003) 1043 – 1050
Corporate disclosure quality, earnings smoothing, and earnings’ timeliness Kenneth W. Shaw* School of Accountancy, College of Business, University of Missouri, 420 Cornell Hall, Columbia, MO 65211, USA Received 3 January 2001; accepted 22 October 2001
Abstract This paper explores the interaction between corporate disclosure and recognition practices by examining the relation between financial analysts’ ratings of disclosure quality, discretionary accruals, and the earnings – return association. The results suggest that firms with higherquality disclosures use discretionary accruals to smooth earnings more aggressively than firms with lower-quality disclosures. As a result, the timeliness with which accounting earnings capture bad news is inversely related to disclosure quality. These results suggest that higherquality disclosure can be accompanied by increased earnings management. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Disclosure; Earnings smoothing; Timeliness; Discretionary accruals
1. Introduction High-quality information is essential to the proper functioning of equity markets. Corporations communicate their financial information to the investing community via two major avenues: disclosure and recognition. Disclosure refers to the process of providing information about items in the financial statements, via footnotes, supplementary schedules, or other means, while recognition refers to the process of formally including items, in numbers, in the financial statements (Financial Accounting Standards Board, 1984, paragraphs 6 and 9). Though investors and financial analysts rely upon financial information in forming investment strategies and/or earnings forecasts, little is known about how firms’ disclosure and recognition strategies interact to influence the usefulness of financial information. That is, are better disclosures complemented by better recognition practices? Do firms with higherquality disclosures engage in less or more earnings smoothing? These questions are particularly pertinent in a climate in which former Securities Exchange Commission Chairman Arthur Levitt (1998) declares ‘‘earnings management is on the rise and the quality of financial reporting is on the decline.’’ Evidence on the relation between disclosure and recognition practices can provide insights that can enable
* Tel.: +1-573-882-5939; fax: +1-573-882-2437. E-mail address: [email protected] (K.W. Shaw).
financial analysts and investors to better use financial information in their decisions. This study investigates the important interaction between corporate disclosure and recognition practices by examining associations between corporate disclosure quality (measured by financial analysts’ ratings of corporate disclosure quality), earnings smoothing activities (measured by discretionary accruals), and the timeliness of earnings’ recognition of value-relevant events (measured through the earnings – return association). Though firms might complement high (low)-quality disclosures with high (low)-quality recognition practices, anecdotal evidence in Bernard and Schipper (1994, p. 9) suggests that ‘‘managers believe they can maintain good relations with analysts and investors by providing highly predictable earnings, . . . and prefer to prepare analysts and investors for losses by providing footnote and other disclosures.’’ This scenario implies that firms’ recognition practices might not always match their disclosure practices, potentially yielding performance measures that suffer from increased earnings management and lack of timeliness. The sample consists of 1113 firm-year observations over the period 1985 – 1989. Discretionary accruals are measured using the cross-sectional variation of the Jones (1991) model, and tests of the relation between financial analysts’ disclosure quality ratings and the level of discretionary accruals are performed. These analyses include controls for other determinants of disclosure quality ratings, including firm size, contemporaneous firm perform-
0148-2963/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. doi:10.1016/S0148-2963(01)00328-9
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K.W. Shaw / Journal of Business Research 56 (2003) 1043–1050
ance, the relation between stock returns and earnings, security issuances (Lang and Lundholm, 1993), and audit firm size (Ahmed, 1995). To investigate the relation between disclosure quality and earnings smoothing, the manner in which the type of news (good or bad) impacts the relation between disclosure quality ratings and discretionary accruals was also examined. Finally, the contemporaneous earnings – return relation was used to examine the timeliness with which accounting earnings recognize value-relevant economic events. The analyses reveal several interesting new findings. Initially, a negative relation between disclosure quality and discretionary accruals was found, suggesting that, on average, firms with higher disclosure quality are more conservative in accruals’ recognition than are firms with lower disclosure quality. However, further analysis reveals that this apparent conservatism is limited to years with good news (years with positive stock returns or cash flow from operations). In years with bad news, disclosure quality is positively related to discretionary accruals, indicating that better disclosing firms use relatively more income-increasing accruals than lower disclosure quality firms do in such years. Together, this set of results suggests that firms with higher-quality disclosures delay the recognition of some value-relevant information in earnings, and smooth reported income more aggressively, via discretionary accruals, than do firms with lowerquality disclosures. To further address earnings’ timeliness, the relation between contemporaneous earnings and stock returns was examined. Like in Lang and Lundholm (1993), it was initially found that the contemporaneous earnings – return relation is inversely related to disclosure quality, which suggests that earnings of high disclosure quality firms lack timeliness. However, the lower earnings –return correlations of high disclosure quality firms are entirely concentrated in bad-news years; there is no relation between the earnings – return correlations and disclosure quality in good news years. Earnings of low disclosure quality firms, however, are considerably more sensitive to bad news than to good news. These analyses reveal that with respect to recognition of bad news, earnings of firms with higher disclosure quality are less timely than earnings of firms with lower disclosure quality. In sum, the results suggest that firms with better disclosures substitute enhanced disclosure for delayed recognition of some value-relevant events in earnings; these firms aggressively manage earnings to smooth extreme news and overcome this delayed recognition by providing better disclosures. This suggests some firms delay recognition of bad news until they ‘‘prepare’’ the market by revising expectations downward through enhanced disclosures. Thus, higher disclosure quality is not always synonymous with less earnings management. Analysts and investors should consider the interaction between disclosure and recognition practices when forming investment strategies
and/or earnings forecasts. In addition, investors should be aware that analysts reward firms that engage in more aggressive earnings smoothing with higher disclosure scores; this implied preference for smooth earnings might differ from investors’ preferences.
2. Data 2.1. Sample selection Sample firms must have total disclosure quality scores available from the Financial Analysts’ Federation (FAF) Committee on Financial Reporting in any of the years 1985 – 1989. Financial institutions and observations with changes in fiscal year-end are deleted. Firms must have financial data available on Compustat and stock return data available on the Center for Research on Security Prices (CRSP) database. Observations where the absolute value of discretionary accruals exceeds 200% of total assets were deleted to mitigate the impact of extreme values. After implementation of these filters, a total of 1113 firm-years remain for analysis. 2.2. Analysts’ disclosure quality ratings FAF reports contain industry-specific analyst evaluations of disclosure quality on three dimensions: (a) annual published information; (b) quarterly and other published information; and (c) analyst relations and related aspects. Within these categories, each industry-specific analyst group prepares a list of important disclosure aspects, weighted to reflect industry information requirements, and assigns a score to each firm. A total company score is then computed as a weighted combination of the three category scores. The total score was used, because it captures the full range of disclosure activities. Considerable research (e.g., Lang and Lundholm, 1996; Healy et al., 1999) suggest that this score captures meaningful variation in disclosure practices across firms. Since each industry is evaluated by a different set of analysts, the disclosure quality score variable was industry-adjusted by subtracting the industry median total disclosure quality score for the year, and this industry-adjusted disclosure quality score was labeled as DQ. 2.3. Discretionary accruals The cross-sectional version of the Jones (1991) model proposed by DeFond and Jiambalvo (1994) to measure discretionary accruals was used. Prior research (e.g., Subramanyam, 1996; Bartov et al., 2000) suggests that this method outperforms the time-series Jones model. This approach estimates nondiscretionary (i.e., normal) accruals as a function of changes in revenue and the level of property, plant, and equipment.
K.W. Shaw / Journal of Business Research 56 (2003) 1043–1050
The following model was estimated separately for each industry and calendar year group having at least six observations, ACCRi;t 1 DREVi;t ¼a þb TAi;tl TAi;tl TAi;tl PPEi;t þg ð1Þ þ ei;t TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t, and ei,t is the error term. Total accruals are defined as income before extraordinary items minus operating cash flows. Nondiscretionary accruals (NDAC) are the fitted values, and discretionary accruals (DAC) are the residuals, from estimation of Eq. (1). 2.4. Control variables Relative to firms with low disclosure quality, firms with higher FAF scores are larger, have better earnings and stock return performance, issue securities more frequently, have weaker relations between earnings and returns (Lang and Lundholm, 1993), and are audited by larger auditor firms Ahmed (1995). Thus, firm size, a measure of the prior earnings – return correlation, existence of debt or equity
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issuances, auditor size, and firm profitability were included as control variables in the regression analyses. Firm size (SIZE) is the market value of common equity (in billion US dollars). Net income before extraordinary items (NIBX) and operating cash flows (OCF) are as defined in Compustat (both variables are then deflated by beginning market value of equity to mitigate heteroskedasticity (Christie, 1987)). Stock returns (RET) are the compounded stock returns over the fiscal year, calculated from the CRSP database. The earnings – return correlation (CRET) is the correlation between annual earnings and annual stock returns computed during the 10 years preceding the current FAF report year. Using information from Securities Data Company’s Global New Issues Database, a dummy variable (ISSUE), which takes on the value of 1 if the firm issues debt or equity securities in the year of the FAF report or the following 2 years, and 0 otherwise, was created. Finally, a dummy variable for auditor type (AUD), which takes on the value of 1 if the firm is a non-Big-8 auditor, and 0 otherwise, was also defined. 2.5. Descriptive statistics Table 1 provides descriptive statistics. Deviations in the total disclosure quality score from industry medians vary considerably, as the standard deviation of DQ equals 0.094, and the values range from a minimum of 0.350 to a maximum of 0.328. Mean and median discretionary
Table 1 Descriptive statisticsa Variable
DQ DAC SIZE RET ARET NIBX OCF CRET ISSUE AUD
Mean
0.004 0.013 3.411 0.188 0.001 0.056 0.169 0.395
Standard deviation
0.094 0.111 6.143 0.316 0.298 0.220 0.252 0.339
Max
0.328 0.366 63.21 2.269 2.093 1.649 6.137 0.999
Quartiles Third
Median
First
0.058 0.037 3.488 0.354 0.157 0.105 0.226 0.634
0.001 0.003 1.484 0.163 0.023 0.077 0.141 0.454
0.060 0.046 0.545 0.001 0.172 0.052 0.081 0.231
Min
#>0
0.350 1.054 0.011 0.824 1.000 4.687 1.853 0.951
557 525 1113 832 510 1005 1034 970 291 19
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. ARET = firm’s market-adjusted stock returns in year t. NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. OCF = firm i’s net operating cash flows in year t, deflated by SIZE. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a Sample size equals 1113.
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accruals are somewhat negative ( 0.013 and 0.003, respectively), consistent with Subramanyam (1996). The sample firms are large, as the mean (median) SIZE is US$3.4 (US$1.48) billion, and their stock returns are positive, as the mean (median) RET equals 0.188 (0.163). Earnings and cash flows are also, on average, positive, as the mean (median) NIBX equals 0.056 (0.077) and the mean (median) OCF equals 0.169 (0.141). The mean earnings – return coefficient, CRET, is 0.395, consistent with Lang and Lundholm (1993). ISSUE takes on a value of 1 in 291 firm-years, while AUD takes on a value of 1 for only 19 firm-years; consistent with the sample being comprised of large firms, few are audited by nonBig-8 auditors.
positively related to auditor size (since AUD takes on positive values for small auditors, a negative correlation indicates a positive relation between audit firm size and disclosure quality). Disclosure quality is negatively related to discretionary accruals, as the Pearson correlation coefficient of .067 is significant at the .02 level. This preliminary result suggests that firms with better disclosure practices might be, on average, more conservative in their recognition choices. However, such a conclusion cannot be drawn until other determinants of disclosure quality are included as control variables in a multivariate analysis. 3.2. Multivariate regression results Ordinary least squares was used to estimate the equation,
3. Disclosure quality and earnings’ smoothing DQi;t ¼ a0 þ a1 DACi;t þ a2 SIZEi;t þ a3 RETi;t 3.1. Univariate correlation results
þ a4 CRETi;t þ a5 ISSUEi;t
Table 2 presents Pearson correlation coefficients between disclosure quality, discretionary accruals, and control variables. Consistent with Lang and Lundholm (1993), disclosure quality is positively related to firm size, firm performance measured by earnings, cash flows or contemporaneous stock returns (both raw and market adjusted), and the existence of security issuances, while it is negatively related to the earnings –return correlation over the preceding 10 years. Disclosure quality is also
þ a6 AUDi;t þ e i;t
ð2Þ
where all variables are as described earlier. The coefficient a1 captures the relation between industryadjusted disclosure quality scores and discretionary accruals, after controlling for other determinants of disclosure quality. Table 3 reports results of estimating Eq. (2). The coefficient on DAC is negative (a1 = .052) and is sig-
Table 2 Pearson correlation resultsa Variable
DAC
SIZE
RET
ARET
DQ DAC SIZE RET ARET NIBX OCF CRET ISSUE
.067 (.02)
.064 (.03) .032 (.29)
.069 (.02) .095 (.00) .129 (.00)
.074 .094 .124 .929
(.01) (.00) (.00) (.00)
NIBX
OCF
.187 (.00) .110 (.00) .062 (.04) .232 (.00) .240 (.00)
.008 .263 .003 .064 .058 .163
CRET (.78) (.00) (.92) (.00) (.05) (.00)
.066 .026 .053 .089 .081 .025 .042
ISSUE (.03) (.38) (.08) (.00) (.01) (.42) (.05)
.075 .046 .028 .002 .018 .043 .044 .076
AUD (.01) (.13) (.36) (.93) (.54) (.15) (.14) (.01)
.070 .025 .034 .027 .018 .012 .006 .004 .025
(.02) (.39) (.26) (.37) (.57) (.69) (.84) (.90) (.40)
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. ARET = firm’s market-adjusted stock returns in year t. NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. OCF = firm i’s net operating cash flows in year t, deflated by SIZE. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a Numbers in parentheses are P values for two-tailed tests of the hypothesis that the correlation differs from zero. Sample size equals 1113.
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Table 3 Relation between disclosure quality ratings and discretionary accrualsa DQi,t = a0 + a1DACi,t + a2SIZEi,t + a3RETi,t + a4CRETi,t + a5ISSUEi,t + a6AUDi,t + ei,t Intercept (a0)
DAC (a1)
SIZE (a2)
RET (a3)
CRET (a4)
ISSUE (a5)
AUD (a6)
R2 %
.006 (.29)
.052 (.04)
.000 (.09)
.016 (.08)
.016 (.06)
.011 (.09)
.048 (.02)
1.73
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
nificant at the .04 level. This negative relation between disclosure quality and discretionary accruals is robust to the inclusion of the other determinants of disclosure quality, and the control variables exhibit relations with disclosure quality consistent with those in prior research. Untabulated sensitivity analyses using alternative proxies for firm performance (market-adjusted stock returns or operating cash flows) are qualitatively similar to those reported in the paper. These results suggest that, on average, firms with higher-quality disclosures are more conservative in their accrual choices. However, merely examining the sign of the relation between disclosure quality and discretionary accruals cannot address whether disclosure quality is related to the extent to which firms smooth income. The next section investigates potential earnings smoothing by expanding the analysis to include type of news.
3.3. Multivariate regression results incorporating type of news To test for income smoothing, the sample, on the basis of type of news, was partitioned, and the association between DQ and DAC within each news partition was examined: Income smoothing would suggest that firms decrease earnings during periods with good news and increase earnings during periods with bad news (see DeFond and Park, 1997 for an analogous approach). The sample was partitioned into good news and bad news categories based on the sign of contemporaneous stock returns (RET). Good news (bad news) firm-years are those with positive (negative) stock returns; a total of 832 firm-years are classified as good news. A dummy variable, NEWS, which equals 1 for bad news firm-years, and 0 otherwise, was defined, and NEWS and the interaction of DAC and NEWS were included in the
Table 4 Relation between disclosure quality ratings and discretionary accruals by news typea DQi,t = b0 + b1NEWSi,t + b2DACi,t + b3DACi,t NEWSi,t + b4 RETi,t + b5CRETi,t + b6SIZEi,t + b7ISSUEi,t + b8AUDi,t + e1,t Intercept (b0)
NEWS (b1)
DAC (b2)
DAC NEWS (b3)
RET (b4)
CRET (b5)
SIZE (b6)
ISSUE (b7)
AUD (b8)
R2 %
.008 (.20)
.003 (.72)
.106 (.00)
.229 (.00)
.015 (.19)
.014 (.08)
.000 (.07)
.012 (.06)
.050 (.02)
2.86
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. NEWS = A dummy variable which equals 1 if RET is negative and zero otherwise. 832 (281) of the observations are classified as good (bad) news. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
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model. The coefficient on this interaction variable captures the incremental impact of bad news on the relation between DQ and DAC. Then, ordinary least squares was used to estimate the model, DQi;t ¼ b0 þ b1 NEWSi;t þ b2 DACi;t þ b3 DACi;t NEWSi;t þ b4 RETi;t þ b5 CRETi;t þ b6 SIZEi;t þ b7 ISSUEi;t þ b8 AUDi;t þ ei;t
ð3Þ
where all the variables are as defined above. Table 4 presents the details of estimating Eq. (3). The coefficient on DAC for good news years (b2) is negative ( .106) and significant at the .01 level; in good news years, disclosure quality scores are inversely related to discretionary accruals. The coefficient capturing the difference in the DQ –DAC relation between bad news and good news years (b3) is positive (.229) and significant at the .01 level. The coefficient capturing the disclosure quality – discretionary accruals relation in bad news years (b2 + b3 =.123) is significant at the .05 level; in bad news years, disclosure quality scores are positively related to discretionary accruals. The results in Table 4 imply that higher disclosure quality firms adopt relatively more income-decreasing accruals during good news years and relatively more income-increasing accruals during bad news years. That is, firms with higher disclosure quality smooth income more aggressively than do firms with low disclosure quality, and thus delay recognition of some value-relevant events in earnings. In the next section, the impact of these smoothing activities on the timeliness of earnings’ recognition is directly examined.
4. Disclosure quality and earnings’ timeliness Consistent with prior research (e.g., Warfield and Wild, 1992; Dechow, 1994), the earnings – return association was used to measure the timeliness with which earnings recognize economic events. Under the assumption that stock prices lead earnings in capturing economic events, a higher contemporaneous correlation between earnings and stock returns indicates that earnings are capturing more of the
value-relevant events that are being reflected in stock returns in the same period, thus implying more timely earnings. To examine the issue of earnings’ timeliness, Beaver et al. (1980) and Basu (1997) were followed, and ‘‘reverse regressions’’ of earnings were estimated on returns and interactions of returns with variables capturing disclosure quality level and news type. A dummy variable, DUMDQ, which equals 1 when a firm’s disclosure quality rating is above the industry median (high quality), and 0 otherwise (low quality), was defined. Then, NEWS, DUMDQ, and interactions of both DUMDQ and NEWS with RET were included in the model. The model also includes the interaction of RET, DUMDQ, and NEWS together to capture the joint effect of disclosure quality level and news type on the earnings –return relation. The full model is then, NIBXi;t ¼ g0 þ g1 DUMDQi;t þ g2 NEWSi;t þ g3 RETi;t þ g4 RETi;t DUMDQi;t þ g5 RETi;t NEWSi;t þ g6 RETi;t NEWSi;t DUMDQi;t þ ei;t
ð4Þ
where all variables are defined above. The model in Eq. (4) includes variables to address Lang and Lundholm’s (1993) finding that the earnings –return relation is decreasing in disclosure quality (DUMDQ, RET, and DUMDQ RET), variables to address Basu’s (1997) finding that earnings are more sensitive to bad news than to good news (NEWS, RET, and NEWS RET), and a variable (RET NEWS DUMDQ), which captures the joint effects of disclosure quality and news type. Untabulated results of estimating Eq. (4) on subsets of the independent variables confirm the findings in Lang and Lundholm (1993) and Basu (1997). In particular, the earnings –return relation is lower for high disclosure quality firms (the coefficient g4 is negative and significant in a regression of NIBX on DUMDQ, RET, and DUMDQ RET), and earnings are more sensitive to bad news than to good news (the coefficient g5 is positive and significant in a regression of NIBX on NEWS, RET, and NEWS RET). The results of estimating the full model in Eq. (4) are reported in Table 5. The coefficients capturing the impact of
Table 5 Reverse regressions of earnings on returns, partitioned on the basis of disclosure quality and news typea NIBXi,t = g0 + g1DUMDQi,t + g2NEWSi,t + g3RETi,t + g4RETi,t DUMDQi,t + g5RETi,t NEWSi,t + g6RETi,t NEWSi,t DUMDQi,t + ei,t Intercept (g0)
DUMDQ (g1)
NEWS (g2)
RET (g3)
RET DUMDQ (g4)
RET NEWS (g5)
RET NEWS DUMDQ (g6)
R2 %
.073 (.00)
.020 (.28)
.028 (.18)
.045 (.19)
.029 (.56)
1.151 (.00)
1.163 (.00)
17.68
NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. DUMDQ = a dummy variable that equals 1 if a firm’s disclosure score in year t is above the sample median for firm i’s industry in year t, and zero otherwise. RET = firm i’s compounded stock returns in year t. NEWS = a dummy variable, which equals 1 if RET is negative, and zero otherwise. A total of 832 (281) of the observations are classified as good (bad) news. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
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5. Sensitivity analyses on discretionary accruals estimates
Fig. 1. Earnings – return coefficients contingent on disclosure quality and news type. This figure presents combinations of the earnings – return coefficients computed from the regression results reported in Table 5. Low disclosure quality/good news Low disclosure quality/bad news High disclosure quality/good news High disclosure quality/bad news
= = = =
g3 g3+g5 g3+g4 g3+g4+g5+g6
= = = =
0.045 1.196 0.074 0.062
LO (HI) disclosure quality firms are those with disclosure quality scores below (above) the sample median. GOOD (BAD) news years are firm-years with positive (negative) compounded stock returns over the fiscal year.
disclosure quality and type of news on the earnings – return relation are calculated from the results shown in Table 5 and presented in Fig. 1. Fig. 1 shows that disclosure quality has little impact on the earnings –return relation in good news years; while the earnings – return coefficient for high disclosure quality firms in good news years is positive (g3 + g4 = .074) and marginally significant (.06 level), it is not economically nor statistically different from the earnings – return coefficient for low disclosure quality firms in good news years (g3 =.045). In bad news years, however, disclosure quality significantly impacts the earnings – return coefficients; the coefficient for the low disclosure quality/bad news partition (g3 + g5) is 1.196, and it is almost 20 times larger than the earnings – return coefficient for the high disclosure quality/bad news partition, which although positive (g3 + g4 + g5 + g6 = .062), is not statistically significant. The difference between these coefficients ( 1.134) is significant at the .01 level, indicating that the timeliness with which earnings recognize bad news is inversely related to disclosure quality. In addition, Fig. 1 indicates that only within the low disclosure quality partition are earnings more sensitive to bad news than to good news; here, the coefficient on bad news (1.196) is 20 times higher than that for good news (.045), and this difference (1.151) is statistically significant at better than .01 level. In sum, these results are consistent with high disclosure quality firms delaying recognition of some value relevant bad news in earnings, in comparison with firms with lower-quality disclosures.
The tests reported in Tables 3 and 4 require discretionary accruals, which are unobservable. Dechow et al. (1995) argue that tests of earnings management using extant estimation methods are likely of low power, and modify the Jones (1991) model to include the change in accounts receivable. Eq. (2) was reestimated after subtracting the change in accounts receivable from year t 1 to t from the change in sales revenue over the same period. The Pearson (Spearman) correlation between the discretionary accruals estimates from the cross-sectional Jones and modified crosssectional Jones models equals .97 (.98), and all of the study’s inferences are unaffected by use of this alternative measure of discretionary accruals. Guay et al. (1996) argue that extant methods yield discretionary and nondiscretionary accrual estimates that are indistinguishable from a random partition of accruals. To further examine the issue of measurement error, total accruals were decomposed in a random manner as suggested by Guay et al. (p. 101), and 25 replications of Eqs. (3) and (4) were estimated. If the results maintain after this random decomposition, then it might be possible to attribute them simply to noise in the discretionary accruals proxy. The mean and median coefficient estimates and t statistics on the random discretionary accruals variables in Eqs. (3) and (4) are not different from zero, and in only one of the estimations does either coefficient estimate approach statistical significance at even the .10 level. Thus, the results are not replicated using these random measures of discretionary accruals, mitigating the concern that the results may be attributable solely to noise in the discretionary accruals proxy.
6. Summary and conclusions Disclosure and recognition are the two primary methods by which firms communicate their financial results to the investing community. This study investigates the interaction between corporate disclosure and recognition practices by examining the association between corporate disclosure quality (measured by financial analysts’ ratings of corporate disclosure quality), earnings smoothing activities (measured by discretionary accruals), and the timeliness of earnings’ recognition of value-relevant events (measured through the earnings – return association). It was found, on average, that disclosure quality ratings are inversely related to discretionary accruals, suggesting that firms with better disclosures are more conservative in accruals recognition. However, further analysis reveals that this apparent conservatism is limited only to good news years; in bad news years, disclosure quality ratings are positively related to discretionary accruals. This set of results suggests that high disclosure quality firms aggressively smooth extreme earnings news via discretionary accruals.
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Further tests indicate that this income smoothing behavior impacts the timeliness with which accounting earnings recognize some value-relevant events. Prior research shows that earnings recognize bad news on a more timely basis than good news (Basu, 1997). My tests reveal that earnings of firms with high-quality disclosures do not exhibit this asymmetric timeliness to news, but earnings of firms with lower-quality disclosures do. In particular, the earnings – return coefficients for high disclosure quality firms are essentially the same in good and bad news years, while for the low disclosure quality firms, the earnings – return coefficient in bad news years is over 20 times the size of the earnings – return coefficient in good news years. Likewise, the sensitivity of earnings to bad news for low disclosure quality firms is over 15 times the sensitivity of earnings of high disclosure quality firms to any type of news. In sum, the results suggest that firms with better disclosures substitute enhanced disclosure for delayed recognition of some value-relevant events in earnings; these firms aggressively manage earnings to smooth extreme news and overcome this delayed recognition by providing better disclosures. This suggests that some firms delay recognition of bad news until they have ‘‘prepared’’ the market by revising expectations downward through enhanced disclosures. Thus, higher disclosure quality is not always synonymous with less earnings management.
Acknowledgements The helpful comments from the seminar participants at the University of Maryland, Harlan Platt (the editor), and two anonymous referees are gratefully acknowledged. Mark Lang and Russell Lundholm generously provided the disclosure quality score data.
References Ahmed K. The effect of corporate characteristics on disclosure quality in corporate annual reports. Working paper, Victoria University of Wellington, New Zealand, 1995.
Bartov E, Gul F, Tsui J. Discretionary-accruals models and audit qualifications. J Account Econ 2000;30(3):421 – 52. Basu S. The conservatism principle and the asymmetric timeliness of earnings. J Accoun Econ 1997;24(1):3 – 37. Beaver W, Lambert R, Morse D. The information content of security prices. J Accoun Econ 1980;2:3 – 28. Bernard V, Schipper K. Recognition and disclosure in financial reporting. Working paper prepared for 1994 FASB Financial Reporting Research Conference, 1994. Christie A. On cross-sectional analysis in accounting research. J Accoun Econ 1987;9:231 – 58. Dechow P. Accounting earnings and cash flows as measures of firm performance. J Accoun Econ 1994;18(1):3 – 42. Dechow P, Sloan R, Sweeney A. Detecting earnings management. Accoun Rev 1995;70(2):192 – 226. DeFond M, Jiambalvo J. Debt covenant violation and manipulation of accruals: accounting choice in troubled companies. J Accoun Econ 1994;17(1, 2):145 – 76. DeFond M, Park C. Smoothing income in anticipation of future earnings. J Accoun Econ 1997;23(2):115 – 40. Financial Accounting Standards Board, Statement of Financial Accounting Concepts No. 5. Recognition and measurement in financial statements of business enterprises. Stamford, CT, 1984. Guay W, Kothari SP, Watts R. A market-based evaluation of discretionary accrual models. J Accoun Res 1996;34:83 – 105. Healy P, Hutton A, Palepu K. Stock price performance and intermediation changes surrounding sustained increases in disclosure. Contemp Accoun Res 1999;16(3):485 – 520. Jones J. Earnings management during import relief investigations. J Accoun Res 1991;29(2):193 – 228. Lang M, Lundholm L. Cross-sectional determinants of analyst ratings of corporate disclosure. J Accoun Res 1993;31(2):246 – 71. Lang M, Lundholm R. Corporate disclosure policy and analyst behavior. Accoun Rev 1996;71(4):467 – 92. Levitt A. The ‘‘numbers game’’. Remarks by Chairman Arthur Levitt to the New York University Center for Law and Business. September 28, 1998. URL: http://www.sec.gov/news/speech/speecharchive/1998/ spch.txt. Subramanyam KR. The pricing of discretionary accruals. J Accoun Econ 1996;22:249 – 82. Warfield T, Wild J. Accounting recognition and the relevance of earnings as an explanatory variable for returns. Accoun Rev 1992;67(4): 821 – 42.
Corporate disclosure quality, earnings smoothing, and earnings’ timeliness Kenneth W. Shaw* School of Accountancy, College of Business, University of Missouri, 420 Cornell Hall, Columbia, MO 65211, USA Received 3 January 2001; accepted 22 October 2001
Abstract This paper explores the interaction between corporate disclosure and recognition practices by examining the relation between financial analysts’ ratings of disclosure quality, discretionary accruals, and the earnings – return association. The results suggest that firms with higherquality disclosures use discretionary accruals to smooth earnings more aggressively than firms with lower-quality disclosures. As a result, the timeliness with which accounting earnings capture bad news is inversely related to disclosure quality. These results suggest that higherquality disclosure can be accompanied by increased earnings management. D 2002 Elsevier Science Inc. All rights reserved. Keywords: Disclosure; Earnings smoothing; Timeliness; Discretionary accruals
1. Introduction High-quality information is essential to the proper functioning of equity markets. Corporations communicate their financial information to the investing community via two major avenues: disclosure and recognition. Disclosure refers to the process of providing information about items in the financial statements, via footnotes, supplementary schedules, or other means, while recognition refers to the process of formally including items, in numbers, in the financial statements (Financial Accounting Standards Board, 1984, paragraphs 6 and 9). Though investors and financial analysts rely upon financial information in forming investment strategies and/or earnings forecasts, little is known about how firms’ disclosure and recognition strategies interact to influence the usefulness of financial information. That is, are better disclosures complemented by better recognition practices? Do firms with higherquality disclosures engage in less or more earnings smoothing? These questions are particularly pertinent in a climate in which former Securities Exchange Commission Chairman Arthur Levitt (1998) declares ‘‘earnings management is on the rise and the quality of financial reporting is on the decline.’’ Evidence on the relation between disclosure and recognition practices can provide insights that can enable
* Tel.: +1-573-882-5939; fax: +1-573-882-2437. E-mail address: [email protected] (K.W. Shaw).
financial analysts and investors to better use financial information in their decisions. This study investigates the important interaction between corporate disclosure and recognition practices by examining associations between corporate disclosure quality (measured by financial analysts’ ratings of corporate disclosure quality), earnings smoothing activities (measured by discretionary accruals), and the timeliness of earnings’ recognition of value-relevant events (measured through the earnings – return association). Though firms might complement high (low)-quality disclosures with high (low)-quality recognition practices, anecdotal evidence in Bernard and Schipper (1994, p. 9) suggests that ‘‘managers believe they can maintain good relations with analysts and investors by providing highly predictable earnings, . . . and prefer to prepare analysts and investors for losses by providing footnote and other disclosures.’’ This scenario implies that firms’ recognition practices might not always match their disclosure practices, potentially yielding performance measures that suffer from increased earnings management and lack of timeliness. The sample consists of 1113 firm-year observations over the period 1985 – 1989. Discretionary accruals are measured using the cross-sectional variation of the Jones (1991) model, and tests of the relation between financial analysts’ disclosure quality ratings and the level of discretionary accruals are performed. These analyses include controls for other determinants of disclosure quality ratings, including firm size, contemporaneous firm perform-
0148-2963/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. doi:10.1016/S0148-2963(01)00328-9
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ance, the relation between stock returns and earnings, security issuances (Lang and Lundholm, 1993), and audit firm size (Ahmed, 1995). To investigate the relation between disclosure quality and earnings smoothing, the manner in which the type of news (good or bad) impacts the relation between disclosure quality ratings and discretionary accruals was also examined. Finally, the contemporaneous earnings – return relation was used to examine the timeliness with which accounting earnings recognize value-relevant economic events. The analyses reveal several interesting new findings. Initially, a negative relation between disclosure quality and discretionary accruals was found, suggesting that, on average, firms with higher disclosure quality are more conservative in accruals’ recognition than are firms with lower disclosure quality. However, further analysis reveals that this apparent conservatism is limited to years with good news (years with positive stock returns or cash flow from operations). In years with bad news, disclosure quality is positively related to discretionary accruals, indicating that better disclosing firms use relatively more income-increasing accruals than lower disclosure quality firms do in such years. Together, this set of results suggests that firms with higher-quality disclosures delay the recognition of some value-relevant information in earnings, and smooth reported income more aggressively, via discretionary accruals, than do firms with lowerquality disclosures. To further address earnings’ timeliness, the relation between contemporaneous earnings and stock returns was examined. Like in Lang and Lundholm (1993), it was initially found that the contemporaneous earnings – return relation is inversely related to disclosure quality, which suggests that earnings of high disclosure quality firms lack timeliness. However, the lower earnings –return correlations of high disclosure quality firms are entirely concentrated in bad-news years; there is no relation between the earnings – return correlations and disclosure quality in good news years. Earnings of low disclosure quality firms, however, are considerably more sensitive to bad news than to good news. These analyses reveal that with respect to recognition of bad news, earnings of firms with higher disclosure quality are less timely than earnings of firms with lower disclosure quality. In sum, the results suggest that firms with better disclosures substitute enhanced disclosure for delayed recognition of some value-relevant events in earnings; these firms aggressively manage earnings to smooth extreme news and overcome this delayed recognition by providing better disclosures. This suggests some firms delay recognition of bad news until they ‘‘prepare’’ the market by revising expectations downward through enhanced disclosures. Thus, higher disclosure quality is not always synonymous with less earnings management. Analysts and investors should consider the interaction between disclosure and recognition practices when forming investment strategies
and/or earnings forecasts. In addition, investors should be aware that analysts reward firms that engage in more aggressive earnings smoothing with higher disclosure scores; this implied preference for smooth earnings might differ from investors’ preferences.
2. Data 2.1. Sample selection Sample firms must have total disclosure quality scores available from the Financial Analysts’ Federation (FAF) Committee on Financial Reporting in any of the years 1985 – 1989. Financial institutions and observations with changes in fiscal year-end are deleted. Firms must have financial data available on Compustat and stock return data available on the Center for Research on Security Prices (CRSP) database. Observations where the absolute value of discretionary accruals exceeds 200% of total assets were deleted to mitigate the impact of extreme values. After implementation of these filters, a total of 1113 firm-years remain for analysis. 2.2. Analysts’ disclosure quality ratings FAF reports contain industry-specific analyst evaluations of disclosure quality on three dimensions: (a) annual published information; (b) quarterly and other published information; and (c) analyst relations and related aspects. Within these categories, each industry-specific analyst group prepares a list of important disclosure aspects, weighted to reflect industry information requirements, and assigns a score to each firm. A total company score is then computed as a weighted combination of the three category scores. The total score was used, because it captures the full range of disclosure activities. Considerable research (e.g., Lang and Lundholm, 1996; Healy et al., 1999) suggest that this score captures meaningful variation in disclosure practices across firms. Since each industry is evaluated by a different set of analysts, the disclosure quality score variable was industry-adjusted by subtracting the industry median total disclosure quality score for the year, and this industry-adjusted disclosure quality score was labeled as DQ. 2.3. Discretionary accruals The cross-sectional version of the Jones (1991) model proposed by DeFond and Jiambalvo (1994) to measure discretionary accruals was used. Prior research (e.g., Subramanyam, 1996; Bartov et al., 2000) suggests that this method outperforms the time-series Jones model. This approach estimates nondiscretionary (i.e., normal) accruals as a function of changes in revenue and the level of property, plant, and equipment.
K.W. Shaw / Journal of Business Research 56 (2003) 1043–1050
The following model was estimated separately for each industry and calendar year group having at least six observations, ACCRi;t 1 DREVi;t ¼a þb TAi;tl TAi;tl TAi;tl PPEi;t þg ð1Þ þ ei;t TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t, and ei,t is the error term. Total accruals are defined as income before extraordinary items minus operating cash flows. Nondiscretionary accruals (NDAC) are the fitted values, and discretionary accruals (DAC) are the residuals, from estimation of Eq. (1). 2.4. Control variables Relative to firms with low disclosure quality, firms with higher FAF scores are larger, have better earnings and stock return performance, issue securities more frequently, have weaker relations between earnings and returns (Lang and Lundholm, 1993), and are audited by larger auditor firms Ahmed (1995). Thus, firm size, a measure of the prior earnings – return correlation, existence of debt or equity
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issuances, auditor size, and firm profitability were included as control variables in the regression analyses. Firm size (SIZE) is the market value of common equity (in billion US dollars). Net income before extraordinary items (NIBX) and operating cash flows (OCF) are as defined in Compustat (both variables are then deflated by beginning market value of equity to mitigate heteroskedasticity (Christie, 1987)). Stock returns (RET) are the compounded stock returns over the fiscal year, calculated from the CRSP database. The earnings – return correlation (CRET) is the correlation between annual earnings and annual stock returns computed during the 10 years preceding the current FAF report year. Using information from Securities Data Company’s Global New Issues Database, a dummy variable (ISSUE), which takes on the value of 1 if the firm issues debt or equity securities in the year of the FAF report or the following 2 years, and 0 otherwise, was created. Finally, a dummy variable for auditor type (AUD), which takes on the value of 1 if the firm is a non-Big-8 auditor, and 0 otherwise, was also defined. 2.5. Descriptive statistics Table 1 provides descriptive statistics. Deviations in the total disclosure quality score from industry medians vary considerably, as the standard deviation of DQ equals 0.094, and the values range from a minimum of 0.350 to a maximum of 0.328. Mean and median discretionary
Table 1 Descriptive statisticsa Variable
DQ DAC SIZE RET ARET NIBX OCF CRET ISSUE AUD
Mean
0.004 0.013 3.411 0.188 0.001 0.056 0.169 0.395
Standard deviation
0.094 0.111 6.143 0.316 0.298 0.220 0.252 0.339
Max
0.328 0.366 63.21 2.269 2.093 1.649 6.137 0.999
Quartiles Third
Median
First
0.058 0.037 3.488 0.354 0.157 0.105 0.226 0.634
0.001 0.003 1.484 0.163 0.023 0.077 0.141 0.454
0.060 0.046 0.545 0.001 0.172 0.052 0.081 0.231
Min
#>0
0.350 1.054 0.011 0.824 1.000 4.687 1.853 0.951
557 525 1113 832 510 1005 1034 970 291 19
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. ARET = firm’s market-adjusted stock returns in year t. NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. OCF = firm i’s net operating cash flows in year t, deflated by SIZE. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a Sample size equals 1113.
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accruals are somewhat negative ( 0.013 and 0.003, respectively), consistent with Subramanyam (1996). The sample firms are large, as the mean (median) SIZE is US$3.4 (US$1.48) billion, and their stock returns are positive, as the mean (median) RET equals 0.188 (0.163). Earnings and cash flows are also, on average, positive, as the mean (median) NIBX equals 0.056 (0.077) and the mean (median) OCF equals 0.169 (0.141). The mean earnings – return coefficient, CRET, is 0.395, consistent with Lang and Lundholm (1993). ISSUE takes on a value of 1 in 291 firm-years, while AUD takes on a value of 1 for only 19 firm-years; consistent with the sample being comprised of large firms, few are audited by nonBig-8 auditors.
positively related to auditor size (since AUD takes on positive values for small auditors, a negative correlation indicates a positive relation between audit firm size and disclosure quality). Disclosure quality is negatively related to discretionary accruals, as the Pearson correlation coefficient of .067 is significant at the .02 level. This preliminary result suggests that firms with better disclosure practices might be, on average, more conservative in their recognition choices. However, such a conclusion cannot be drawn until other determinants of disclosure quality are included as control variables in a multivariate analysis. 3.2. Multivariate regression results Ordinary least squares was used to estimate the equation,
3. Disclosure quality and earnings’ smoothing DQi;t ¼ a0 þ a1 DACi;t þ a2 SIZEi;t þ a3 RETi;t 3.1. Univariate correlation results
þ a4 CRETi;t þ a5 ISSUEi;t
Table 2 presents Pearson correlation coefficients between disclosure quality, discretionary accruals, and control variables. Consistent with Lang and Lundholm (1993), disclosure quality is positively related to firm size, firm performance measured by earnings, cash flows or contemporaneous stock returns (both raw and market adjusted), and the existence of security issuances, while it is negatively related to the earnings –return correlation over the preceding 10 years. Disclosure quality is also
þ a6 AUDi;t þ e i;t
ð2Þ
where all variables are as described earlier. The coefficient a1 captures the relation between industryadjusted disclosure quality scores and discretionary accruals, after controlling for other determinants of disclosure quality. Table 3 reports results of estimating Eq. (2). The coefficient on DAC is negative (a1 = .052) and is sig-
Table 2 Pearson correlation resultsa Variable
DAC
SIZE
RET
ARET
DQ DAC SIZE RET ARET NIBX OCF CRET ISSUE
.067 (.02)
.064 (.03) .032 (.29)
.069 (.02) .095 (.00) .129 (.00)
.074 .094 .124 .929
(.01) (.00) (.00) (.00)
NIBX
OCF
.187 (.00) .110 (.00) .062 (.04) .232 (.00) .240 (.00)
.008 .263 .003 .064 .058 .163
CRET (.78) (.00) (.92) (.00) (.05) (.00)
.066 .026 .053 .089 .081 .025 .042
ISSUE (.03) (.38) (.08) (.00) (.01) (.42) (.05)
.075 .046 .028 .002 .018 .043 .044 .076
AUD (.01) (.13) (.36) (.93) (.54) (.15) (.14) (.01)
.070 .025 .034 .027 .018 .012 .006 .004 .025
(.02) (.39) (.26) (.37) (.57) (.69) (.84) (.90) (.40)
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. ARET = firm’s market-adjusted stock returns in year t. NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. OCF = firm i’s net operating cash flows in year t, deflated by SIZE. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a Numbers in parentheses are P values for two-tailed tests of the hypothesis that the correlation differs from zero. Sample size equals 1113.
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Table 3 Relation between disclosure quality ratings and discretionary accrualsa DQi,t = a0 + a1DACi,t + a2SIZEi,t + a3RETi,t + a4CRETi,t + a5ISSUEi,t + a6AUDi,t + ei,t Intercept (a0)
DAC (a1)
SIZE (a2)
RET (a3)
CRET (a4)
ISSUE (a5)
AUD (a6)
R2 %
.006 (.29)
.052 (.04)
.000 (.09)
.016 (.08)
.016 (.06)
.011 (.09)
.048 (.02)
1.73
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
nificant at the .04 level. This negative relation between disclosure quality and discretionary accruals is robust to the inclusion of the other determinants of disclosure quality, and the control variables exhibit relations with disclosure quality consistent with those in prior research. Untabulated sensitivity analyses using alternative proxies for firm performance (market-adjusted stock returns or operating cash flows) are qualitatively similar to those reported in the paper. These results suggest that, on average, firms with higher-quality disclosures are more conservative in their accrual choices. However, merely examining the sign of the relation between disclosure quality and discretionary accruals cannot address whether disclosure quality is related to the extent to which firms smooth income. The next section investigates potential earnings smoothing by expanding the analysis to include type of news.
3.3. Multivariate regression results incorporating type of news To test for income smoothing, the sample, on the basis of type of news, was partitioned, and the association between DQ and DAC within each news partition was examined: Income smoothing would suggest that firms decrease earnings during periods with good news and increase earnings during periods with bad news (see DeFond and Park, 1997 for an analogous approach). The sample was partitioned into good news and bad news categories based on the sign of contemporaneous stock returns (RET). Good news (bad news) firm-years are those with positive (negative) stock returns; a total of 832 firm-years are classified as good news. A dummy variable, NEWS, which equals 1 for bad news firm-years, and 0 otherwise, was defined, and NEWS and the interaction of DAC and NEWS were included in the
Table 4 Relation between disclosure quality ratings and discretionary accruals by news typea DQi,t = b0 + b1NEWSi,t + b2DACi,t + b3DACi,t NEWSi,t + b4 RETi,t + b5CRETi,t + b6SIZEi,t + b7ISSUEi,t + b8AUDi,t + e1,t Intercept (b0)
NEWS (b1)
DAC (b2)
DAC NEWS (b3)
RET (b4)
CRET (b5)
SIZE (b6)
ISSUE (b7)
AUD (b8)
R2 %
.008 (.20)
.003 (.72)
.106 (.00)
.229 (.00)
.015 (.19)
.014 (.08)
.000 (.07)
.012 (.06)
.050 (.02)
2.86
DQ = firm i’s total disclosure quality score in year t, minus the median total disclosure quality score for firm i’s industry in year t. DAC = discretionary accruals, measured as the residuals from OLS estimation (by-year within two-digit SIC codes) of: ACCRi;t 1 DREVi;t PPEi;t ¼a þb þg þ ei;t TAi;tl TAi;tl TAi;tl TAi;tl where ACCRi,t is total accruals for firm i in year t, TAi,t 1 is total assets for firm i at the end of year t 1, DREVi,t is firm i’s level of sales revenue in year t less its level of sales revenue in year t 1, and PPEi,t is property, plant, and equipment for firm i in year t. SIZE = firm i’s market value of common equity at the beginning of year t (in billion US dollars). RET = firm i’s compounded stock returns in year t. CRET = the correlation between firm i’s earnings and returns over the 10 years preceding year t. ISSUE = a dummy variable, which equals 1 if firm i issues debt or equity securities in year t or either of the 2 years following year t, and 0 otherwise. AUD = a dummy variable, which equals 1 for non-Big-8 auditors, and 0 otherwise. NEWS = A dummy variable which equals 1 if RET is negative and zero otherwise. 832 (281) of the observations are classified as good (bad) news. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
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model. The coefficient on this interaction variable captures the incremental impact of bad news on the relation between DQ and DAC. Then, ordinary least squares was used to estimate the model, DQi;t ¼ b0 þ b1 NEWSi;t þ b2 DACi;t þ b3 DACi;t NEWSi;t þ b4 RETi;t þ b5 CRETi;t þ b6 SIZEi;t þ b7 ISSUEi;t þ b8 AUDi;t þ ei;t
ð3Þ
where all the variables are as defined above. Table 4 presents the details of estimating Eq. (3). The coefficient on DAC for good news years (b2) is negative ( .106) and significant at the .01 level; in good news years, disclosure quality scores are inversely related to discretionary accruals. The coefficient capturing the difference in the DQ –DAC relation between bad news and good news years (b3) is positive (.229) and significant at the .01 level. The coefficient capturing the disclosure quality – discretionary accruals relation in bad news years (b2 + b3 =.123) is significant at the .05 level; in bad news years, disclosure quality scores are positively related to discretionary accruals. The results in Table 4 imply that higher disclosure quality firms adopt relatively more income-decreasing accruals during good news years and relatively more income-increasing accruals during bad news years. That is, firms with higher disclosure quality smooth income more aggressively than do firms with low disclosure quality, and thus delay recognition of some value-relevant events in earnings. In the next section, the impact of these smoothing activities on the timeliness of earnings’ recognition is directly examined.
4. Disclosure quality and earnings’ timeliness Consistent with prior research (e.g., Warfield and Wild, 1992; Dechow, 1994), the earnings – return association was used to measure the timeliness with which earnings recognize economic events. Under the assumption that stock prices lead earnings in capturing economic events, a higher contemporaneous correlation between earnings and stock returns indicates that earnings are capturing more of the
value-relevant events that are being reflected in stock returns in the same period, thus implying more timely earnings. To examine the issue of earnings’ timeliness, Beaver et al. (1980) and Basu (1997) were followed, and ‘‘reverse regressions’’ of earnings were estimated on returns and interactions of returns with variables capturing disclosure quality level and news type. A dummy variable, DUMDQ, which equals 1 when a firm’s disclosure quality rating is above the industry median (high quality), and 0 otherwise (low quality), was defined. Then, NEWS, DUMDQ, and interactions of both DUMDQ and NEWS with RET were included in the model. The model also includes the interaction of RET, DUMDQ, and NEWS together to capture the joint effect of disclosure quality level and news type on the earnings –return relation. The full model is then, NIBXi;t ¼ g0 þ g1 DUMDQi;t þ g2 NEWSi;t þ g3 RETi;t þ g4 RETi;t DUMDQi;t þ g5 RETi;t NEWSi;t þ g6 RETi;t NEWSi;t DUMDQi;t þ ei;t
ð4Þ
where all variables are defined above. The model in Eq. (4) includes variables to address Lang and Lundholm’s (1993) finding that the earnings –return relation is decreasing in disclosure quality (DUMDQ, RET, and DUMDQ RET), variables to address Basu’s (1997) finding that earnings are more sensitive to bad news than to good news (NEWS, RET, and NEWS RET), and a variable (RET NEWS DUMDQ), which captures the joint effects of disclosure quality and news type. Untabulated results of estimating Eq. (4) on subsets of the independent variables confirm the findings in Lang and Lundholm (1993) and Basu (1997). In particular, the earnings –return relation is lower for high disclosure quality firms (the coefficient g4 is negative and significant in a regression of NIBX on DUMDQ, RET, and DUMDQ RET), and earnings are more sensitive to bad news than to good news (the coefficient g5 is positive and significant in a regression of NIBX on NEWS, RET, and NEWS RET). The results of estimating the full model in Eq. (4) are reported in Table 5. The coefficients capturing the impact of
Table 5 Reverse regressions of earnings on returns, partitioned on the basis of disclosure quality and news typea NIBXi,t = g0 + g1DUMDQi,t + g2NEWSi,t + g3RETi,t + g4RETi,t DUMDQi,t + g5RETi,t NEWSi,t + g6RETi,t NEWSi,t DUMDQi,t + ei,t Intercept (g0)
DUMDQ (g1)
NEWS (g2)
RET (g3)
RET DUMDQ (g4)
RET NEWS (g5)
RET NEWS DUMDQ (g6)
R2 %
.073 (.00)
.020 (.28)
.028 (.18)
.045 (.19)
.029 (.56)
1.151 (.00)
1.163 (.00)
17.68
NIBX = firm i’s net income before extraordinary items in year t, deflated by SIZE. DUMDQ = a dummy variable that equals 1 if a firm’s disclosure score in year t is above the sample median for firm i’s industry in year t, and zero otherwise. RET = firm i’s compounded stock returns in year t. NEWS = a dummy variable, which equals 1 if RET is negative, and zero otherwise. A total of 832 (281) of the observations are classified as good (bad) news. a The model was estimated using ordinary least squares. The table reports parameter estimates, P values (in parentheses) for two-tailed tests of the hypothesis that the coefficient is different from zero, and adjusted R2 percentages. Sample size equals 1113.
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5. Sensitivity analyses on discretionary accruals estimates
Fig. 1. Earnings – return coefficients contingent on disclosure quality and news type. This figure presents combinations of the earnings – return coefficients computed from the regression results reported in Table 5. Low disclosure quality/good news Low disclosure quality/bad news High disclosure quality/good news High disclosure quality/bad news
= = = =
g3 g3+g5 g3+g4 g3+g4+g5+g6
= = = =
0.045 1.196 0.074 0.062
LO (HI) disclosure quality firms are those with disclosure quality scores below (above) the sample median. GOOD (BAD) news years are firm-years with positive (negative) compounded stock returns over the fiscal year.
disclosure quality and type of news on the earnings – return relation are calculated from the results shown in Table 5 and presented in Fig. 1. Fig. 1 shows that disclosure quality has little impact on the earnings –return relation in good news years; while the earnings – return coefficient for high disclosure quality firms in good news years is positive (g3 + g4 = .074) and marginally significant (.06 level), it is not economically nor statistically different from the earnings – return coefficient for low disclosure quality firms in good news years (g3 =.045). In bad news years, however, disclosure quality significantly impacts the earnings – return coefficients; the coefficient for the low disclosure quality/bad news partition (g3 + g5) is 1.196, and it is almost 20 times larger than the earnings – return coefficient for the high disclosure quality/bad news partition, which although positive (g3 + g4 + g5 + g6 = .062), is not statistically significant. The difference between these coefficients ( 1.134) is significant at the .01 level, indicating that the timeliness with which earnings recognize bad news is inversely related to disclosure quality. In addition, Fig. 1 indicates that only within the low disclosure quality partition are earnings more sensitive to bad news than to good news; here, the coefficient on bad news (1.196) is 20 times higher than that for good news (.045), and this difference (1.151) is statistically significant at better than .01 level. In sum, these results are consistent with high disclosure quality firms delaying recognition of some value relevant bad news in earnings, in comparison with firms with lower-quality disclosures.
The tests reported in Tables 3 and 4 require discretionary accruals, which are unobservable. Dechow et al. (1995) argue that tests of earnings management using extant estimation methods are likely of low power, and modify the Jones (1991) model to include the change in accounts receivable. Eq. (2) was reestimated after subtracting the change in accounts receivable from year t 1 to t from the change in sales revenue over the same period. The Pearson (Spearman) correlation between the discretionary accruals estimates from the cross-sectional Jones and modified crosssectional Jones models equals .97 (.98), and all of the study’s inferences are unaffected by use of this alternative measure of discretionary accruals. Guay et al. (1996) argue that extant methods yield discretionary and nondiscretionary accrual estimates that are indistinguishable from a random partition of accruals. To further examine the issue of measurement error, total accruals were decomposed in a random manner as suggested by Guay et al. (p. 101), and 25 replications of Eqs. (3) and (4) were estimated. If the results maintain after this random decomposition, then it might be possible to attribute them simply to noise in the discretionary accruals proxy. The mean and median coefficient estimates and t statistics on the random discretionary accruals variables in Eqs. (3) and (4) are not different from zero, and in only one of the estimations does either coefficient estimate approach statistical significance at even the .10 level. Thus, the results are not replicated using these random measures of discretionary accruals, mitigating the concern that the results may be attributable solely to noise in the discretionary accruals proxy.
6. Summary and conclusions Disclosure and recognition are the two primary methods by which firms communicate their financial results to the investing community. This study investigates the interaction between corporate disclosure and recognition practices by examining the association between corporate disclosure quality (measured by financial analysts’ ratings of corporate disclosure quality), earnings smoothing activities (measured by discretionary accruals), and the timeliness of earnings’ recognition of value-relevant events (measured through the earnings – return association). It was found, on average, that disclosure quality ratings are inversely related to discretionary accruals, suggesting that firms with better disclosures are more conservative in accruals recognition. However, further analysis reveals that this apparent conservatism is limited only to good news years; in bad news years, disclosure quality ratings are positively related to discretionary accruals. This set of results suggests that high disclosure quality firms aggressively smooth extreme earnings news via discretionary accruals.
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Further tests indicate that this income smoothing behavior impacts the timeliness with which accounting earnings recognize some value-relevant events. Prior research shows that earnings recognize bad news on a more timely basis than good news (Basu, 1997). My tests reveal that earnings of firms with high-quality disclosures do not exhibit this asymmetric timeliness to news, but earnings of firms with lower-quality disclosures do. In particular, the earnings – return coefficients for high disclosure quality firms are essentially the same in good and bad news years, while for the low disclosure quality firms, the earnings – return coefficient in bad news years is over 20 times the size of the earnings – return coefficient in good news years. Likewise, the sensitivity of earnings to bad news for low disclosure quality firms is over 15 times the sensitivity of earnings of high disclosure quality firms to any type of news. In sum, the results suggest that firms with better disclosures substitute enhanced disclosure for delayed recognition of some value-relevant events in earnings; these firms aggressively manage earnings to smooth extreme news and overcome this delayed recognition by providing better disclosures. This suggests that some firms delay recognition of bad news until they have ‘‘prepared’’ the market by revising expectations downward through enhanced disclosures. Thus, higher disclosure quality is not always synonymous with less earnings management.
Acknowledgements The helpful comments from the seminar participants at the University of Maryland, Harlan Platt (the editor), and two anonymous referees are gratefully acknowledged. Mark Lang and Russell Lundholm generously provided the disclosure quality score data.
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