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Accounting restatements and information risk

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Abstract

We examine the association between accounting restatements and the pricing of information risk. Using the Fama and French three-factor model augmented with discretionary and innate information risk factors, we find a significant increase in the factor loadings on the discretionary information risk factor for restatement firms after a restatement announcement. The increase in factor loadings results in an increase in the estimated cost of capital, which is cross-sectionally associated with the short-window price reaction to restatements. We study several potential determinants of the change in information risk pricing and find evidence consistent with the restatement initiator (auditor vs. firm management) and the number of times a firm restates affecting the change in the pricing of discretionary information risk. We also find an increase, of smaller magnitude, in the pricing of discretionary information risk for non-restatement firms in the same industries as the restatement firms, consistent with an information transfer effect.

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Notes

  1. Following Francis et al. (2005) and Chen et al. (2007), we use the term “pricing of information risk” to refer to the factor loadings (the OLS regression coefficient estimate in an asset pricing returns model) on information risk factor returns.

  2. We refer to Francis et al.’s AQ factor as the information risk (IR) factor in this paper because we implement the AQ factor as a proxy for information risk.

  3. Lambert et al. (2007) present a model in which the average precision of information risk affects firms’ cost of capital in the context of the one-factor capital asset pricing model. We use a multifactor asset pricing approach by including an information risk factor in the Fama-French three factor model.

  4. The GAO (2002) report notes a total loss of $100 billion in market capitalization in the immediate period after the announcement of restatements for their sample of restatement firms.

  5. While we do not have any ex ante expectations that these other risk factors will change we do find some statistically significant changes.

  6. Francis et al. (2005) assert that restatement firms arguably have poor earnings quality and document a decline in quality, as measured by their returns-based representation of earnings quality in the years preceding restatements.

  7. Francis et al. (2005) separate innate from discretionary accruals quality in some of their tests.

  8. Nichols (2006) also argues that the IR factor captures fundamental risk, not information risk, although he does not provide a theoretical explanation for the type of fundamental risk that is being captured by the IR factor or why the market, size, and book to market factors would not capture fundamental risk. Nichols’s (2006) conclusion that the IR factor captures fundamental risk arises mostly because a large part of the explanatory power of the IR factor derives from the innate portion of the IR factor. Ogneva (2008) argues that the Core et al. (2008) pricing tests are misspecified because of a correlated omitted variable–future cash flow shocks. When she controls for these shocks, the IR factor loading is associated with future returns in the second stage regressions.

  9. To calculate implied cost of capital Hribar and Jenkins (2004) use (1) Gebhardt et al. (2001) methodology based on the Ohlson (1995) residual income model; (2) Gode and Mohanram’s (2002) implementation of the Ohlson and Juettner-Nauroth (2005) model; and (3) Easton and Monahan (2005) price to forward earnings model.

  10. Moore and Pfeiffer (2004) regress total accruals on the change in revenue and receivables; property, plant and equipment; book to market ratio; past sales growth; return on assets; industry; and firm size. The estimated intercept is their measure of total conditional accruals.

  11. As a result, we do not have a prediction for SEC-initiated restatements.

  12. Research on intra-industry information transfers generally focuses on the stock price reactions of non-announcing firms around the information release date of another firm in the industry. Prior research includes studies of earnings announcements (Foster 1981; Clinch and Sinclair 1987; Han and Wild 1990; Freeman and Tse 1992; Ramnath 2002), management forecasts of earnings (Baginski 1987; Pyro and Lustgarten 1990), bankruptcy (Lang and Stulz 1992), public securities offerings (Szewczyck 1992) and nuclear accidents (Bowen et al. 1983).

  13. Durnev and Mangan (2009) offer and provide results consistent with an alternative explanation: “the information transfer at least partly occurs because restatements have implications for competitors’ investments” (p. 5). Our explanation is also offered as part of the explanation for the intra-industry information transfer surrounding restatements.

  14. Excluded from the sample were restatements related to mergers and acquisitions where appropriate accounting methods were used, discontinued operations, stock splits, issuance of stock dividends, currency-related issues, change in business segment definitions, changes due to transfers of management, changes made for presentation purposes, general accounting changes under GAAP, litigation settlements, and arithmetic and general bookkeeping errors.

  15. The requirement of 73 months of returns eliminates firms that have delisted for bankruptcy. We expect these firms would experience an increase in information risk and thus not including these firms biases against our finding results.

  16. Twenty-three firms had two restatements, and three firms had three restatements. The period between the first and last restatement for 18 firms was less than 1 year. The period between restatements for the remaining eight firms ranged from 13 to 34 months.

  17. Cumulative abnormal returns are calculated as the sum of the difference between firm i’s daily return and the daily value-weighted market return for the period from day −3 to day +3.

  18. Results are qualitatively similar if we exclude month 0 from the post-restatement period.

  19. ROA is multiplied by 100 to be consistent with the dependent variable.

  20. Variables are winsorized at the 1st and 99th percentiles to reduce the effect of outliers.

  21. Gleason et al. (2008) eliminate 314 small restatements, 35% of their full restatement sample.

  22. For 88 restatements CRSP did not have return data in the seven-day window surrounding the restatement announcement.

  23. We also require each industry to have a minimum of five non-restatement peer firms and ensure that none of the restatement firms in the original GAO sample is included in the non-restatement sample.

  24. FASB-initiated restatements are not required by FASB but result from FASB’s issuance of pronouncements.

  25. We also estimate this equation using the total information risk factor, IR, and find that the factor loading on IR significantly increases in the post-restatement period.

  26. See Francis et al. (2005), Table 4.

  27. The market return and other factors are in excess return format (the risk free rate is subtracted from each in its calculation), the cost of equity effect we calculate is the equity risk premium part of the cost of capital. Our approach follows that used by Grullon et al. (2002). We also estimated the equity risk premium for each firm by multiplying each factor loading from Table 2 by the factor return over the respective estimation (pre/post) periods. This approach also resulted in an 0.86% increase in the risk premium.

  28. We also allow for a nonlinear decline by including t and t 2, but neither estimated coefficient is significant.

  29. In unreported results, we also find that the total IR premium is associated with CAR.

  30. When the interaction of InnateIR with the CORE dummy variable is not included in the regression, the factor loading on the discretionary information risk factor is significantly greater for core restatements than for noncore restatements.

  31. Controlling for the number of days between the first and last restatement of firms with multiple restatements does not affect our results.

  32. We define large restatements as restatement announcements associated with a short-window CAR of less than or equal to −1%.

  33. Variables are winsorized at the 1st and 99th percentiles when estimating Eq. 6 to reduce the effect of outliers.

  34. We winsorize returns at the 1st and 99th percentiles before we form portfolios in order to reduce the effect of outliers.

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Acknowledgments

We appreciate the helpful comments and suggestions from the editor, Katherine Schipper, an anonymous referee, Shuping Chen, Shiva Rajgopal, Andy Call, Max Hewitt, Rick Mergenthaler, Shamin Mashruwala, and workshop participants at the University of Washington. We are also grateful to Lew Thorson for his programming assistance. Shevlin acknowledges financial support from the Paul Pigott/PACCAR Professorship.

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Authors and Affiliations

  1. University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080-3021, USA

    Todd Kravet

  2. Department of Accounting, Michael G. Foster School of Business, University of Washington, P.O. Box 353200, Seattle, WA, 98195, USA

    Terry Shevlin

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Correspondence to Terry Shevlin.

Appendix

Appendix

The IR factor captures the information risk factor mimicking returns to a portfolio that takes a long position in the poorest AQ stocks and a short position in the best AQ stocks. The AQ metric is calculated based on the cross-sectional Dechow and Dichev (2002) model, augmented with the fundamental variables, property, plant, and equipment, and change in revenues, from the Jones (1991) model:

$$ {\text{TCA}}_{i,t} = \theta_{0,t} + \theta_{1,t} {\text{CFO}}_{i,t - 1} + \theta_{2,t} {\text{CFO}}_{i,t} + \theta_{3,t} {\text{CFO}}_{i,t + 1} + \theta_{4,t} \Updelta {\text{REV}}_{i,t} + \theta_{5,t} {\text{PPE}}_{i,t} + \upsilon_{i,t} $$
(5)

where TCA is total current accruals; CFO is cash flow from operations; ΔREV is change in revenue; and PPE is the level of property, plant, and equipment, all scaled by average total assets. Equation 5 is estimated for each of Fama and French’s (1997) 48 industry groups with at least 20 firms in year t. We use firms listed on NYSE, AMEX, and NASDAQ and exclude financial firms. The time-series standard deviation of the residuals, \( \sigma (\hat{\upsilon })_{i,t} \), calculated over t − 4 to t, is the AQ measure. Larger standard deviations of residuals indicate a poorer mapping of accruals into cash flows and lower earnings quality. The AQ metric calculated in fiscal year t is used in portfolio formation for a 12-month period beginning in fiscal year t + 1.

We calculate the innate and discretionary components of the AQ metric similar to Francis et al. (2005) and Dechow and Dichev (2002). The innate and discretionary components of AQ are calculated from annual cross-sectional, industry-specific estimations of the following equation:

$$ \begin{gathered} {\text{AQ}}_{i,t} = \lambda_{0} + \lambda_{1} {\text{Size}}_{i,t} + \lambda_{2} \sigma ({\text{CFO}})_{i,t} + \lambda_{3} \sigma ({\text{Sales}})_{i,t} + \lambda_{4} {\text{OperCycle}}_{i,t} \\ + \lambda_{5} {\text{NegEarn}}_{i,t} + \mu_{i,t} \\ \end{gathered} $$
(6)

where Size i,t is the log of total assets; σ(CFO) i,t is the standard deviation of firm i’s cash flow from operations, calculated over the past 10 years; σ(Sales) i,t is the standard deviation of firm i’s sales, calculated over the past 10 years; OperCycle i,t is the log of firm i’s operating cycle, measured as the sum of days accounts receivable and days inventory; and NegEarn i,t is the number of years, out of the past 10, where firm i reported net income before extraordinary items less than zero.Footnote 33 The residual, \( \hat{\mu } \) i,t , from Eq. 6 is the estimate of the discretionary component of firm i’s accrual quality in year t, DiscAQ i,t  = \( \hat{\mu } \) i,t . The predicted value from Eq. 6 is the estimate of the innate component of firm i’s accrual quality in year t, InnateAQ i,t . The DiscAQ and InnateAQ metrics calculated in fiscal year t are used in portfolio formation for a 12-month period beginning in fiscal year t + 1.

Firms are independently ranked each month into quintiles based on their most recent AQ, DiscAQ, and InnateAQ values.Footnote 34 The average monthly excess returns are calculated for each quintile. The information risk factor mimicking returns for AQ, DiscAQ, and InnateAQ are each equal to the difference between the monthly excess returns of the top two quintiles and bottom two quintiles.

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Kravet, T., Shevlin, T. Accounting restatements and information risk. Rev Account Stud 15, 264–294 (2010). https://doi.org/10.1007/s11142-009-9103-x

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