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Management bias across multiple accounting estimates

Abstract

We examine whether managers appear to aggregate bias in multiple subjective accrual estimates to meet or just beat analyst expectations. We also consider whether the updated language in recent PCAOB auditing standards, focusing auditors on the potential for bias across multiple estimates, impacted this method of managing earnings. Using hand-collected data from a sample of manufacturers, we find that meeting or just beating the most recent consensus analyst earnings forecast is positively associated with income-increasing bias aggregated from multiple accounting estimates. We also find that this relation attenuates in the years following the issuance of PCAOB auditing standards. Further analyses reveal that, after these standards were released, firms increased the use of income-increasing, unexpected non-GAAP exclusions to meet or just beat expectations, an alternative technique subject to less auditor scrutiny.

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Notes

  1. 1.

    After the PCAOB’s reorganization and renumbering of its auditing standards in 2016, these standards are now included in AS 1101, 2101, 1201, 2105, 2110, 2301, 2810, and 1105.

  2. 2.

    According to Staff Accounting Bulletin 99, “Among the considerations that may well render material a quantitatively small misstatement of a financial statement item are … whether the misstatement masks a change in earnings or other trends … [and] whether the misstatement hides a failure to meet analysts’ consensus expectations for the enterprise.”

  3. 3.

    Our main results are based on one-tailed p-values, given that we have directional predictions for our hypotheses.

  4. 4.

    The AICPA’s current AU 312 (AICPA 2006), authoritative for private company audits, makes similar statements (see paragraphs 56–58).

  5. 5.

    This additional discretion may not be as beneficial to firms that already beat benchmarks or that would need more “help” than manipulation of multiple estimates would allow (e.g., large adjustments to estimates that would be more likely to draw auditor scrutiny). As such, we examine firms that meet or just exceed analyst earnings forecasts.

  6. 6.

    Previously, although AICPA-based standards alerted auditors to the potential for bias in the subjective aspects of accounting estimates, the language to consider the potential for bias aggregated across estimates was arguably less direct, explicit, or both. For instance, one of the PCAOB’s (AICPA-based) interim standards (PCAOB 2002, AU 312, ¶36) states: “However, if the auditor believes the estimated amount included in the financial statements is unreasonable, he or she should treat the difference between that estimate and the closest reasonable estimate as a likely misstatement. The auditor should also consider whether the difference between estimates best supported by the audit evidence and the estimates included in the financial statements, which are individually reasonable, indicate a possible bias on the part of the entity’s management. For example, if each accounting estimate included in the financial statements was individually reasonable, but the effect of the difference between each estimate and the estimate best supported by the audit evidence was to increase income, the auditor should reconsider the estimates taken as a whole.” As another example, see AS 5, Appendix B, ¶B8 (PCAOB 2007b).

  7. 7.

    Anecdotal evidence suggests that PCAOB inspectors are examining firms’ compliance with the risk assessment standards. For example, PCAOB Release No. 2015–007 highlights inspection observations related to the risk assessment standards. It notes that the 2012 and 2013 inspections found noncompliance in 26 and 27% of the audits inspected (PCAOB 2015). Additionally, the inspection report of one accounting firm states: “The Firm failed to evaluate whether the issuer’s significant adjustments to the accruals during the year under audit, the total of which represented more than one quarter of the issuer’s income before taxes resulted from errors or indicated possible management bias” (PCAOB 2012).

  8. 8.

    As discussed by Cassell et al. (2015), Accounting Standards Codification (ASC) Topic 330 (FASB 2015) requires firms to present their inventories at the lower of inventory cost or market (LCM) and firms typically use one of two methods (direct or indirect) to comply with this requirement. Our sample selection procedures identify firms that use the indirect method, which entails setting “a valuation allowance … to record any decline in value below cost, with the associated loss recorded against COGS” and is the less common method (Cassell et al. 2015, footnote 13, p. 28). SEC staff has stated “that a write-down of inventory to the LCM at the close of a fiscal period creates a new cost basis that subsequently cannot be marked up based on changes in underlying facts and circumstances” (SEC 1999). The income-increasing discretion in the inventory valuation allowance captured by our measure of discretion would not result from marking up inventory that had been reserved for but rather from recording a smaller valuation allowance than our model would predict is necessary.

  9. 9.

    Overall, one of the authors and four research assistants were involved in the hand-collection effort. Two research assistants initially collected data from filings between 2008 and 2011 and a follow-up effort was made to help ensure that firms disclosing at least two of these allowance or reserve accounts were included in the sample. Subsequently, one of the authors supervised additional data collection effort by two separate research assistants from filings between 2012 and 2014 and randomly checked data input accuracy, although no follow-up efforts were made for firms that disclosed one account in these years to determine whether more accounts were missed. In the end, we recognize the possibility that some firms might transparently disclose more information than we captured during hand-collection.

  10. 10.

    This research design choice could work against finding an association between bias in accounting estimates and meeting or just beating analyst expectations because Cassell et al. (2015) find that less transparent disclosers exhibit more discretion in certain allowance accounts. Although this is a potential limitation of the study, a significant result among companies that provide more transparent allowance or reserve disclosure would arguably be expected to hold for the companies disclosing these allowance or reserve accounts less transparently.

  11. 11.

    We estimate the model by industry using one-digit SIC rather than two-digit SIC to help ensure sufficient observations for each industry-year cross-section. Although our sample is comprised of manufacturers, this design choice allows for a more precise discretion estimate in allowance and reserve accounts to the extent firms within each one-digit SIC are more similar. In an untabulated analysis, we find consistent results when we estimate Model (1) by year only.

  12. 12.

    To do this, we identify companies that disclose an allowance for doubtful accounts nontransparently by pulling all available observations from the same industries (SIC 2000–3999) during the sample period with a nonmissing allowance for doubtful accounts (Compustat variable: RECD) and removing firms that we identified through our manual filing searches that provide transparent disclosure of this allowance account (resulting in 13,177 firm-year observations). Our searches identified 3425 transparent disclosers of the allowance for doubtful accounts, 3071 of which disclosed at least two allowance or reserve accounts transparently, thereby becoming part of our sample.

  13. 13.

    As noted previously, we use the I/B/E/S consensus analyst forecast number in our analysis. Doyle et al. (2013) explain that this number excludes certain expected, nonrecurring GAAP items. To determine whether and how pervasively inventory write-downs are excluded from the analyst forecasts I/B/E/S collects, we first determined that 870 of the 1310 firm-year observations within our sample transparently disclosed an inventory valuation allowance and provided Non-GAAP exclusions (i.e., EPS per I/B/E/S differs from GAAP EPS). We then randomly selected 50 observations and examined the press releases of the annual earnings announcement from the SEC’s EDGAR database. Of the 50 observations examined, only five mention excluding an inventory-related write-down. Although the exclusion of inventory write-downs does not appear to pervasively influence our analysis, we examine the sensitivity of our results to excluding the inventory allowance from our analysis altogether and instead focus on the allowance for doubtful accounts, the warranty reserve, and the sales returns allowance. If we limit our tests of models (2) and (3) to focus on an aggregate measure that excludes the inventory reserve measure (including dropping any observations that only disclosed two accounts where one is the inventory reserve account), our inferences for H1 (see Table 5, Panel C) and H2 (untabulated) remain the same.

  14. 14.

    We control for lagged values of company size, expected growth (i.e., the market-to-book ratio), and leverage to reduce the mechanical dependence between these characteristics and our measure of aggregate discretion within allowances and reserve accounts.

  15. 15.

    Specifically, the DISCLOSE 3 ACCTS and DISCLOSE 4 ACCTS are indicator variables that equal one if a given firm-year observation disclosed three or four of the allowance or reserve accounts, respectively, and zero otherwise. Including these variables helps address the concern that any results are mechanically driven by firms that disclosed more accounts. Furthermore, the DISC_WARR_ALL, DISC_INV_ALL, and DISC_SR_ALL are indicator variables that equal one if, among the allowance accounts disclosed, an observation disclosed information for the warranty allowance, inventory allowance, sales return allowance accounts, respectively and zero otherwise. Including these variables helps address the concern that any results are mechanically driven by firms that disclosed certain of the allowance accounts examined in the study. However, the exclusion of these indicator variables yields consistent results in terms of statistical and economic significance.

  16. 16.

    We ignore industry fixed effects because our sample is comprised of a more homogenous set of observations (i.e., manufacturers). However, our inferences are not affected by the inclusion of fixed effects for specific manufacturing industries at the two-digit SIC code level.

  17. 17.

    Given that all auditors of public companies likely became aware of and subject to the risk assessment standards at the same time, we cannot employ a difference-in-difference design or identify a group of public companies that are either not (or are less) affected by these auditing standards.

  18. 18.

    For example, chief financial executives surveyed by Dichev et al. (2013) estimate that, in any given period, roughly 20% of firms manage earnings. Lim and Tan (2008) find that 17% of their sample meet or just beat the analyst benchmark, while Ashbaugh et al. (2003) find a higher frequency of 27%.

  19. 19.

    As a validation test, we compare aggregate discretion for firms meeting or just beating expectations with multiple allowance and reserve accounts disclosed, relative to firms that disclose a single allowance or reserve account. Recall that we remove these firm-year observations from our sample. Our data collection efforts (see footnote 8) identified 2324 firm-year observations with only one allowance or reserve account disclosed transparently. Of those, 406 meet or just beat expectations. In untabulated results, we find statistically higher mean and median aggregate discretion (AGG_DISC) for suspect firm-years with multiple allowance and reserve accounts disclosed relative to suspect firm-years with only one allowance or reserve disclosed.

  20. 20.

    Although we only find certain differences between firms that disclose multiple versus only one of these accounts transparently, we find that nontransparent disclosers of these allowances tend to be smaller, younger, have lower cash flows from operations, are more highly leveraged, are less likely to be audited by a Big Four or industry specialist auditor, have weaker internal controls, and fewer mergers and acquisitions. Given evidence presented by Cassell et al. (2015) of increased earnings management among firms that disclose allowances and reserves less transparently, it is certainly possible that our results generalize to these other firms, although lack of disclosure limits us from examining this explicitly.

  21. 21.

    In an untabulated analysis, we find no evidence of multicollinearity concerns (e.g., all variance inflation factors are less than five and no correlation coefficients are greater than 0.8 in absolute value) (Kennedy 1992).

  22. 22.

    In untabulated results, we find a similar pattern when examining unexpected discretion from individual allowance accounts. For each individual account, we find that an F-test of whether MB + MB*POST = 0 is not rejected. This analysis provides no evidence that firms shift to focusing earnings management attempts on certain individual accounts following the issuance of the risk assessment standards.

  23. 23.

    A test of whether the coefficient on MB plus the coefficient on REVERSAL is less than (or greater than) zero would indicate whether the effects do not offset.

  24. 24.

    We also examined the association between our measure of aggregate discretion and missing the consensus analyst forecast. We re-estimate Model (2), replacing MB with MISS FORECAST, an indicator variable set equal to one if the firm misses the most recent consensus analyst earnings forecast. In an untabulated analysis, we find that missing forecasts is associated with income-decreasing aggregate discretion. Although this result is consistent with firms building up these allowances and reserves in periods when they do not meet analyst forecasts and we control for firm performance in our model, we cannot rule out the possibility that this association reflects poor firm performance necessitating additional allowances and reserves.

  25. 25.

    Maidenform Brands, Inc. 2012. Form 10-K. Maidenform Brands, Inc. Available as of October 10, 2018 at: https://www.sec.gov/Archives/edgar/data/1323531/000104746912002380/a2207895z10-k.htm.

  26. 26.

    American Railcar Industries, Inc. 2011. Form 10-K. American Railcar Industries, Inc. Available as of September 13, 2018 at: https://www.sec.gov/Archives/edgar/data/1344596/000095012311020799/c13378e10vk.htm.

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Acknowledgements

We are grateful for helpful comments and suggestions from Ken Bills, Patricia Dechow (editor), Jeffrey Doyle, Kelly Huang, James Myers, Linda Myers, Roy Schmardebeck, Melissa Lewis-Western, David Wood, anonymous conference and journal reviewers, and participants at the 2015 BYU Accounting Research Symposium, the 2016 AAA Auditing Section Midyear Meeting, the 2016 22nd annual International Symposium on Audit Research (ISAR), and the 2017 AAA Annual meeting. We also thank workshop participants at Utah State University. We thank Jenalyn Meldrum, Jacob Fryer, Lyndon Orton, and Karson Fronk for their research assistance. We thank the Jon M. Huntsman School of Business at Utah State University and the BYU Marriott School of Business at Brigham Young University for financial support.

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Correspondence to Timothy A. Seidel.

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Appendices

Appendix 1

Table 10 Variable definitions

Appendix 2

Examples of firms with income-increasing aggregate discretion in allowances that meet or just beat analyst expectations

Below we provide two examples of firm-year observations with income-increasing aggregate discretion in allowances that met or just beat analyst expectations. Each of the figures below is text/tables taken from the 10-K filings of the companies being discussed

Example 1 Maidenform Brands, Inc.

Presented below (as Figure 1) is an extract from the company’s notes to the 2011 financial statements.Footnote 25 In fiscal year 2010, Maidenform beat the last consensus analyst forecast by one cent. Below are the disclosures provided for the allowance for doubtful accounts.

Figure 1: Maidenform Brands, Inc. 2011 Footnote 15. Valuation and Qualifying Accounts

figurea

Note that, for the fiscal year ended December 31, 2010, the “additions, charged to expense” to the allowance for doubtful accounts went from an income benefit of $134,000 in fiscal 2009 to an income benefit of $460,000 in fiscal 2010. In fiscal 2011, the income effect reverts back to an expense of $72,000. We note a similar pattern for the sales returns and allowances, where additions, charged to expense went from $71,927,000 in fiscal 2009 down to $63,827,000 in fiscal 2010 and then back to $76,214,000 in fiscal 2011. In summary, we find a consistent pattern in that the income impact of the allowance for doubtful accounts and sales returns and allowances appear unusually low in a year when Maidenform just exceeded analyst expectations.

Example 2 American Railcar Industries, Inc. (ARI)

From ARI’s 2010 10-K filing, we include a portion of the company’s notes to the consolidated financial statements (as Figure 4).Footnote 26 In fiscal year 2009, ARI beat the last consensus analyst forecast by one cent. Below are the disclosures provided for the allowance for doubtful accounts and inventory reserves.

Figure 2: American Railcar Industries, Inc.’s 2008–2010 Allowance Disclosures

figureb

Note that, in fiscal 2009, ARI had an income benefit associated with its adjustment to the doubtful accounts allowance, while in the year prior and subsequent to 2009, it recognized a net expense. Moreover, in 2009, ARI also had a much lower income impact to its inventory allowance (lower charge to cost of goods sold), compared to the year prior and subsequent to 2009. In summary, we find a consistent pattern in that the income impact of the allowance for doubtful accounts and inventory reserves appear unusually low in a year where ARI just exceeded analyst expectations

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Seidel, T.A., Simon, C.A. & Stephens, N.M. Management bias across multiple accounting estimates. Rev Account Stud (2020). https://doi.org/10.1007/s11142-019-09518-8

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Keywords

  • Management bias
  • PCAOB standards
  • Meeting analyst expectations
  • Accounting estimates

JEL classification

  • M41
  • M42
  • M48