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Conditional conservatism and disaggregated bad news indicators in accrual models

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Abstract

Conditional conservatism is an integral but often unmodeled part of the normal accrual process. The standard economic determinants of accruals contain information about unrealized losses. We argue that accountants recognize these unrealized losses as disaggregated write-downs for small asset pools. Modeling disaggregated impairments yields new economic insights about accruals and improved accrual models. We predict that accrual conservatism manifests as a sum of asymmetries for a vector of news indicators, rather than as an asymmetry for a scalar aggregate news proxy. We argue that more detailed segment-level and quarterly indicators have an incremental effect on annual firm-level accruals. We also predict a dynamic effect of successive loss indicators because accountants look for consistent patterns in these variables. Empirical results for U.S. firms support our predictions. The asymmetries in accruals are consistent with conservatism in validation tests. We also document improved statistical power and type I error in earnings management tests.

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Notes

  1. Ball and Shivakumar (2006) argue that multiple indicators can play a role in conservatism because they help mitigate measurement error in the bookable component of firm value. This measurement-error argument suggests that accountants estimate the total bookable value change using all available indicators and recognize impairment when this estimate as a whole indicates bad news (i.e., there is a single asymmetry for this summary measure).

  2. The pre-codification accounting rules for inventory and receivables were based on Accounting Research Bulletins (ARB) 29 and 30 (CAP 1947a, b), respectively, and later incorporated into ARB 43 (CAP 1953). All of the main provisions of this earlier guidance, such as the lower-of-cost-or-market rule for inventory (Statement 6 in ARB 29) and the parallel rule for receivables (Statement 9 in ARB 30), were retained in ASC 310 and 330. We cite only the ASCs to avoid duplication. Basu (1995, 2009) reports that accounting was conservative in the early 1400s, long before prevailing best practices were codified in mandatory standards.

  3. McNichols (2002) combined the Jones (1991) and Dechow and Dichev (2002) models and found that both models have incremental explanatory power. Allen et al. (2013) argue that the combined model is preferable conceptually because it captures two distinct economic roles of accruals.

  4. Lu et al. (2011), Call et al. (2014), He (2015), and others use Ball and Shivakumar’s model in robustness checks but primarily use symmetric accrual models. Dechow and Ge (2006) show that the role of accruals differs in growing and declining firms, with much greater use of conservative accounting rules for the latter, and examine the implications for earnings persistence. Hsu et al. (2011, 2012) and Collins et al. (2014a) argue that asymmetric timeliness models in conservatism research should be estimated using operating accruals rather than earnings but do not examine implications for accruals research. Banker et al. (2015, 2016b) report that sales change has an asymmetric effect on operating accruals. We discuss other potential sources of accrual asymmetry in Sect. 4.1.

  5. Banker et al. (2016a) argue that asset impairment is based on predicted cash flow during the asset’s expected life. Because net income incorporates impairments of current assets, long-lived tangible assets, and infinite-lived goodwill, they predict that conservatism incorporates multiple indicators that best match the different expected lives of these asset classes. Whereas they focus on time horizon differences across assets, we show that multiple indicators are informative even for current assets, all of which have a comparable expected life of less than 1 year.

  6. The “market” in this rule is determined by current replacement cost and net realizable value (i.e., estimated sales value net of the cost of completion and disposal). If the net realizable value is below the replacement cost, then the market is based on this lower amount (ASC 330-10-35-4). However, if the replacement cost is lower than the net realizable value less the normal profit margin, then the market is based on the latter (ASC 330-10-35-5). Therefore inventory write-downs primarily reflect decreases in the net realizable value relative to the replacement cost, which are usually caused by selling price decreases.

  7. The allowance for doubtful accounts reflects expected future write-offs of receivables and can be adjusted both upward and downward (ASC 310-10-35-37). When a particular receivable is deemed uncollectible, it is written off and deducted from the allowance; if the receivable is subsequently recovered, this good news is recognized only when the cash has been received (ASC 310-10-35-41).

  8. Companies often cite sales decreases as the reason for inventory write-downs. For example, in 2001 Cisco Systems Inc. had an inventory write-down of $2.25 billion due to “a sudden and significant decrease in demand” (source: https://www.sec.gov/Archives/edgar/data/858877/000109581101505065/0001095811-01-505065.txt). In 2012, Research in Motion Ltd. recorded an inventory write-down of $485 million, citing “lower than anticipated demand” for its Blackberry Playbook tablet (source: https://www.sec.gov/Archives/edgar/data/1070235/000119312513132586/0001193125-13-132586.txt).

  9. Basu (2005) points out that aggregation adds friction to impairments because it reduces the probability and size of write-downs for economically impaired assets, giving rise to the “uncertain impairment trigger” of Beaver and Ryan (2005). Contrarily, a firm can credibly commit to greater conservatism by choosing disaggregated inventory pools, because the pool definitions must be applied consistently from year to year (ASC 330-10-35-10).

  10. SAB 99 (SEC 1999) states that even a small misstatement is likely material if it masks a trend in earnings, changes a loss into a profit, increases managers’ compensation, or affects a firm’s compliance with contractual requirements. The U.S. Auditing Standards Board (ASB) provides similar examples of qualitative criteria for materiality in AU-C 450 paragraph A23 (AICPA 2015). Eilifsen and Messier (2015) survey eight large U.S. audit firms and find that all of them use these qualitative criteria (in addition to quantitative criteria for materiality).

  11. Pope and Walker (1999), Giner and Rees (2001), and Ryan and Zarowin (2003) examine an asymmetric effect of lagged stock returns on current period earnings in a multi-period extension of the Basu (1997) model. Unlike lagged cash flow in our model, lagged stock return is a forward-looking indicator with a long horizon. Therefore the predicted asymmetry in these papers has an ambiguous sign (Beaver and Ryan 2005) because it reflects not only deferrals but also early recognition of unrealized future losses that are embedded in lagged stock return.

  12. Impairment tests are conducted for multiple asset pools within each segment and likely use more detailed information than is available in segment-level disclosures. We do not decompose our indicators beyond individual segments because the required data is not publicly available. We use segment-level data only for sales because segment data for cash flow and number of employees is unavailable for most firms. Although firms have some discretion in how to define operating segments, they can aggregate these segments only if they have similar economic characteristics and are similar in all of the following areas: nature of products or services, nature of production processes, type of customers, distribution method, and regulatory environment (ASC 280-10-50-11, FASB 2016b; previously codified in SFAS 131, FASB 1997). Therefore firms are unlikely to aggregate dissimilar operations.

  13. Quarterly data include cash flow. However, cash flow fluctuates predictably due to seasonal factors (e.g., Frankel et al. 2016). Furthermore, a temporary seasonal decrease in the market price does not require an inventory write-down (ASC 330-10-55-2). Therefore negative cash flow for a quarter is not a reliable impairment indicator.

  14. Prior multi-indicator accrual models assume independent additive impacts of each indicator. In contrast, we predict that the total impact of two successive negative cash flows is greater than the sum of their individual impacts. The parallel predictions for successive cash losses in years t–1 and t are ambiguous. Because these losses likely triggered some write-downs in year t–1, write-downs in year t might be smaller. However, because these successive losses indicate more persistent bad news in year t, write-downs in year t might be bigger.

  15. For example, accountants could mechanically focus on current cash flow. When cash flow is positive (and other indicators are negative), they could argue that the evidence does not “indicate clearly that a loss has been sustained,” which is the verification threshold for impairment in ASC 330-10-35-4. When cash flow is negative, they could interpret this as sufficient evidence of impairment. This would manifest as an asymmetric effect of concurrent cash flow, consistent with Ball and Shivakumar (2005, 2006), but would not reproduce our Hypotheses 1–5.

  16. Allen et al. (2013) use the balance sheet method because it provides a comprehensive measure of working capital accruals that are classified as arising from operating activities (such as purchasing inventory from a supplier) and investing activities (such as obtaining inventory in an acquisition). Dechow (1994) explains that, while net cash flow is measured objectively, operating cash flow includes an accrual adjustment for investing and financing activities that involves accounting judgment. For example, accountants can choose whether to classify receivables as short-term or long-term and whether to expense or capitalize certain items (Dechow et al. 2008). These choices affect reported operating cash flow and earnings. Basu (1997) examines conservatism in both operating and investing accruals (reported as the difference between XE, CFO, and CFOI in his Table 2) because the categorization of cash flows as “investing” or “operating” might be influenced by conservatism-related accounting judgments (see also Hsu et al. 2012). For consistency with our accrual definition, our empirical cash-flow measure incorporates short-term investment activities associated with current assets but excludes longer-term activities such as investment in new property, plant, and equipment. Operating cash flows and accruals can also be computed using data from the statement of cash flows. However, this method does not capture working capital accruals classified as arising from investing activities, and the required data is available only beginning in 1987. Our results are robust to both methods.

  17. Following Allen et al. (2013), we estimate the models at the two-digit SIC level and then aggregate the industry-specific estimates using the Fama and MacBeth (1973) approach. Allen et al. discard industries that have fewer than 30 firm-year observations; we also require at least five bad news observations (i.e., DS = 1, DE = 1, or DC = 1) per industry to reduce the noise in asymmetric timeliness estimates. The results are robust to alternative screening criteria and continue to hold when we use pooled estimation with two-way clustering by firm and year.

  18. Standard data-reduction methods such as principal-components analysis seek to find a summary measure that best captures variation in the independent variables (but does not necessarily describe the dependent variable well). In contrast, we find a summary measure that best fits the dependent variable (i.e., accruals), enabling tests against alternative models. We set the bad-news cutoff to zero for consistency with the standard bad-news definitions of Basu (1997) and Ball and Shivakumar (2005, 2006). Because both X t and DX t are functions of the parameters α 1… α 5, we substitute these functions into the first line of Eq. (2) and estimate the resulting nonlinear model for each industry using Stata command nl. We then combine these industry-specific estimates using the Fama and MacBeth (1973) approach.

  19. Segment-level accrual is a piecewise-linear function of segment-level sales change. Firm-level accrual is the sum of these asymmetries. This sum can be rewritten as the main effect of the sum of segment-level sales changes (i.e., firm-level SGR) plus an incremental effect of the sum of segment-level decreases (i.e, segSGR −). Following Berger and Hann (2007), we use data for business and operating segments (Compustat segment types BUSSEG and OPSEG, respectively), restrict the sample to multi-segment firms, and discard firm-year observations if the sum of segment-level sales deviates by more than 5 % from the firm-level sales. Business segment data under SFAS No. 14 (FASB 1976) begins in 1976, and more detailed operating segment data under SFAS No. 131 (FASB 1997) begins in 1998.

  20. In untabulated tests, our main model (3) also significantly outperforms an extended aggregate model that uses separate linear combinations of indicators to capture good and bad news, and another model that uses α 1 SGR + α 2 EGR and α 3 CF t−1+α 4 CF t  + α 5 CF t+1 as partly aggregated news proxies.

  21. Collins and Kim (2014) find that mergers and acquisitions significantly distort annual growth rates because the business entity is not comparable across periods. Divestitures likely have a similar effect. Collins and Hribar (2002) find that both acquisitions and dispositions have a large impact on balance sheet accrual measures. Therefore the association between the growth variables and accruals in our full sample could partly reflect correlated measurement error due to mergers and divestitures. For example, if a firm acquires (divests) a segment equivalent to 10 % of its operations, then its sales, employees, and working-capital accounts will all increase (decrease) by 10 %. However, this mechanical association cannot explain the incremental asymmetric effects that we focus on. Furthermore, because mergers and divestitures add noise to our classification of good and bad news for the growth variables, they likely weaken our ability to detect conservatism, working against our findings.

  22. The asymmetry for the longer-term indicators (employee growth and realized future cash flow) is significant for inventory but not for receivables, consistent with our argument that inventory reflects an earlier stage of the operating cycle than receivables.

  23. Bushman et al. (2011a) predict and find that conservatism leads to an asymmetry in firms’ capital expenditures because it gives managers an incentive to quickly cut capital expenditures when investment opportunities decrease but does not have a comparable incentive effect when investment opportunities increase. Srivastava et al. (2015) show that greater conservatism is associated with quicker termination of unprofitable projects.

  24. The simulation results for models (5) and (6) are not comparable because these models have additional data requirements that reduce sample size. We find a qualitatively similar improvement in test performance for models (5) and (6) relative to model (1), using a consistent sample to estimate both benchmarks (untabulated).

  25. For consistency with our main results in Tables 3–6, we estimate each model industry by industry at the two-digit SIC level. We then conduct a t test on regression residuals for the earnings management observations. Following Dechow et al. (2012), we also estimate pooled regressions with a dummy variable for earnings management years as an additional regressor and use two-way clustering by firm and year to assess the statistical significance of this dummy. Untabulated results in this robustness check resemble those in Table 8.

  26. Consider a simple model in which earnings X = CF + A + ε, where CF is cash flow, A is normal accrual, and ε is abnormal accrual. Suppose that CF, A, and ε are drawn from independent normal distributions with mean zero and unit variance, and ε is independent of any potential driver of earnings management. The top decile of earnings corresponds to X > 2.219. Using standard formulas for multivariate normal distribution (e.g., Maddala 1983, p. 367), the expected abnormal accrual in the top earnings decile is E{ ε | X > 2.219 } = E{ ε | CF + A + ε > 2.219 } = 1.013, which is more than one standard deviation above the unconditional mean E{ ε } = 0. To determine the false rejection rate, we simulate CF, A, and ε for a sample of 100,000 firms, randomly select 100 firms from the top earnings decile, and test whether the average ε for these 100 observations is significantly different from zero. In all 1000 simulations, the average ε for these observations is positive and significant, indicating a false rejection rate of 100 %.

  27. We also conduct the tests for performance-matched accruals following Kothari et al. (2005). Similar to Dechow et al. (2012), we find that performance matching on ROA is effective in extreme ROA deciles, yielding type I errors of 10.2–12.8 %, but is often unstable in other earnings management partitions. For example, performance-matched tests have a type I error of 63.0–95.4 % in extreme cash flow deciles and 73.2–99.7 % in extreme adjusted-ROA deciles. Banker et al. (2015) recommend matching on sales growth instead of ROA.

  28. For example, unmodeled normal write-downs in standard models reduce signed discretionary accruals (which suggests higher earnings quality) but increase absolute discretionary accruals (which suggests lower earnings quality), leading to conflicting results. Incorporating conservatism in the model could resolve this conflict.

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Acknowledgments

We thank Patricia Dechow (editor), Sugata Roychowdhury (discussant), Mary Barth, Chad Larson, Alastair Lawrence, Lakshmanan Shivakumar, Steve Stubben, two anonymous reviewers, and workshop participants at Temple University, Washington State University, University of Calgary, the 2015 Review of Accounting Studies conference, the 2015 AAA annual meeting, and the 2015 Banff Accounting Conference for helpful comments and suggestions.

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  1. Fox School of Business, Temple University, Philadelphia, PA, USA

    Dmitri Byzalov & Sudipta Basu

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  1. Dmitri Byzalov
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Correspondence to Dmitri Byzalov.

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Byzalov, D., Basu, S. Conditional conservatism and disaggregated bad news indicators in accrual models. Rev Account Stud 21, 859–897 (2016). https://doi.org/10.1007/s11142-016-9361-3

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