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Accounting conservatism and Street earnings

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Abstract

This paper provides evidence that conditional conservatism reduces the usefulness of GAAP earnings for valuation by investors. We find that conditional conservatism reduces GAAP earnings persistence and informativeness, makes income smoothing more difficult, and makes forecasting GAAP earnings more difficult for analysts. We also find that analysts forecast Street earnings numbers with less conditional conservatism. The decrease in conditional conservatism from adjusting GAAP earnings to Street earnings leads to improvements in persistence, smoothing, and informativeness and reduces analysts’ forecast errors and dispersion. Furthermore, as GAAP conditional conservatism increases, (1) Street earnings more likely differ from GAAP, and (2) the magnitude of the difference between Street and GAAP earnings increases. Finally, we find that exclusions (from GAAP to Street) are of higher quality for firms with higher GAAP conditional conservatism. Our results suggest that, as the conditional conservatism of GAAP earnings increases, analysts’ exclusions make Street earnings more useful to investors.

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Notes

  1. Beaver and Ryan (2005) define conditional conservatism as “meaning that book values are written down under sufficiently adverse circumstances but not written up under favorable circumstances.” They state that unconditional conservatism means that “aspects of the accounting process determined at the inception of assets and liabilities yield expected unrecorded goodwill.” In this paper, we focus on conditional conservatism.

  2. Frankel and Roychowdhury (2009) provide evidence that is (albeit indirectly) consistent with this, as they find large negative special items reverse more quickly when reported by firms with more conservative earnings.

  3. We use the Basu (1997), Khan and Watts (2009), and Givoly and Hayn (2000) conditional conservatism measures and a summary measure that aggregates the three measures into one. We provide details of our procedure in Sect. 3.2.

  4. Some studies use I/B/E/S, and some use First Call as sources of Street earnings. We use I/B/E/S actual earnings as our measure of Street earnings.

  5. We provide details of measuring earnings properties (persistence, smoothing, and earnings response coefficients) and analyst forecast properties (error and dispersion) in Sect.  4 .

  6. Kolev et al.’s (2008) finding that the persistence of other items exclusions is smaller than the persistence of Street earnings is also consistent with the notion that other item exclusions contain transitory items. In other words, other item exclusions can contain transitory items resulting from conditional conservatism that are not reported by Compustat as special items. See Sect. 5.2 for further explanation.

  7. The intuition behind this test is that, if analysts intend that their exclusions improve the usefulness of earnings to investors, exclusions will have little relation with permanent earnings. Kolev et al. (2008) use future operating income as a proxy for permanent earnings. An alternative to future operating income is future operating cash flows. Whipple (2014) notes that analysts claim that they exclude noncash items that have no cash flow implications. Accordingly, we also analyze future operating cash flows and find very similar results.

  8. We focus on Street earnings and analysts’ incentives. However, we do not rule out the possibility that managers also play a role in mitigating some consequences of conditional conservatism, as prior studies suggest that Street earnings also reflect managerial incentives (e.g., Bradshaw and Sloan 2002; Christensen et al. 2011; Hsu and Kross 2011).

  9. Other studies also address conservatism and equity markets (in addition to Basu 1997). Balachandran and Mohanram (2011) find conservatism explains little of the decline over time in the value relevance of earnings. Callen et al. (2010) find that conservatism contributes to a nonlinear relation between unexpected equity returns and earnings news and that special items contribute to conditional conservatism. Mensah et al. (2004) study conservatism and analysts’ forecasts, but they use the Penman and Zhang (2002) unconditional conservatism measure. Kim and Pevzner (2010) analyze conditional conservatism and earnings response coefficients, but they do not assess the overall effect of conditional conservatism on earnings response coefficients.

  10. An exception is Whipple (2014), whose evidence is consistent with analysts often excluding other items to make Street earnings more valuation-useful to investors. Our exclusion quality evidence complements his but differs in that he does not address conditional conservatism.

  11. As we note in Sect. 1, our measure of Street earnings is the earnings construct the majority of analysts following a firm forecast. Not infrequently, Street earnings equal GAAP earnings. Additionally, from 2003 onward, some analysts provide forecasts of GAAP earnings even when Street earnings do not equal GAAP earnings (i.e., some analysts forecast non-GAAP and GAAP earnings). Thus we can analyze the consequences of conditional conservatism for analysts’ forecasts of GAAP earnings. See Sect. 4.1 for details.

  12. For example, Hong and Kubik (2003) find evidence suggesting that more accurate analysts more likely experience positive career events, and Stickel’s (1992) evidence suggests that forecast accuracy is associated with the All-America designation by Institutional Investor magazine, which in turn is associated with analyst compensation.

  13. By earnings construct, we mean either GAAP earnings or earnings excluding certain GAAP components. EBITDA is an example. For example, if the majority of the analysts following a firm and tracked by I/B/ES forecast EBITDA, then Street earnings would be EBITDA.

  14. Our results are robust to using the I/B/E/S Summary File.

  15. In an untabulated sensitivity analysis, we also use principal component analysis to obtain an aggregate measure. We first standardize each conservatism measure to unit variance (mean equals zero and standard deviation equals one), and then we compute the principal component using the covariance method. We note that the proportion of variance explained by the principal component is only 35 %, which suggests that the principal component captures a relatively small portion of overall information contained in the three individual measures. Nonetheless, our conclusions are unaffected when we use the principal component as our aggregate measure.

  16. To assess the sensitivity of our results to scaling by stock price, we also estimate unscaled versions of Eq. (4) (and our earnings smoothing, earnings response coefficient, analyst forecast error, and analyst forecast dispersion equations discussed later) and include the inverse of stock price as an explanatory variable (untabulated). Our inferences from all analyses are unchanged.

  17. We follow Kothari et al. (2005) in estimating discretionary accruals. Specifically, we estimate the cross-sectional Jones (1991) model modified by the addition of return on assets. That is, we estimate Accrualsi,q = a (1/Assetsi,q−1) + bΔSalesi,q + cPPEi,q + dNIi,q + ei,q for each quarter and each two-digit SIC code industry, where ACCRUALSi,q is the difference between GAAP earnings and net operating cash flows; ASSETSi,q−1 is lagged total assets; ΔSALESi,q is change in sales; PPEi,q is property, plant, and equipment; and NIi,q is GAAP earnings. We scale ACCRUALSi,q, ΔSALESi,q, PPEi,q, and NIi,q by Assetsi,q−1. Discretionary accruals are the residuals from the accruals estimation model multiplied by Assetsi,q−1. Pre-managed earnings are GAAP earnings minus discretionary accruals.

  18. We do not use I/B/E/S GPS forecasts for all firm-quarters because that data is available only from 2003 onward. In a robustness check, we use only earnings four-quarters prior (e.g., Foster 1977; Bradshaw et al. 2011) to estimate ERCi. Our results (untabulated) are very similar.

  19. We measure the presence of a special item by whether there is a difference between Compustat’s quarterly after-tax earnings before special items and Compustat’s quarterly earnings from continuing operations (Doyle et al. 2003; Kolev et al. 2008).

  20. Our inferences are unchanged if we define SP_MAGi,q as the log of one plus the ratio of the absolute value of the dollar amount of special items to total assets.

  21. Although the three-year government bond yield is a time-specific variable, T_BILLi is firm specific because we average the three-year government bond yield across all quarters with other data available for firm i. Thus T_BILLi differs across firms because firms have different quarters with available data (for other variables).

  22. Our three measures of conservatism are positively and significantly correlated (not tabulated), but the highest of the three (Pearson) correlations is 0.133 (and only 0.064 when calculated using firm-quarter level data discussed below). This suggests that the three measures are different enough to make the use of all three meaningful and is consistent with the recommendation from Givoly et al. (2007) that researchers use multiple conservatism measures.

  23. We repeat estimation of Eq. (6) after replacing ERCi with the square root of ERCi while retaining its sign. Our inferences are not affected.

  24. We base significance levels for firm-level data regression on standard errors corrected for heteroskedasticity. We base significance levels for firm-quarter data regression results on standard errors corrected for heteroskedasticity and adjusted for clustering of observations by firm and time (Cameron et al. 2011).

  25. Prior research provides some, albeit limited, evidence related to this issue. Basu (1997) finds negative earnings changes have less persistence than positive earnings changes. Brooks and Buckmaster (1976) find similar results with extreme earnings changes. Neither controls for other determinants of earnings persistence nor analyzes earnings persistence unconditional on the sign of earnings change.

  26. As noted previously, conservatism could reduce earnings response coefficients, in part, through its effects on persistence and smoothing. However, conservatism is not the only determinant of persistence and smoothing and other determinants may be correlated with both conservatism and earnings response coefficients. To control for this possibility, we estimate Eq. (6) again (with ERCi as the dependent variable) and include PERSISTENCEi and SMOOTHi as independent variables. The coefficient on CONSERVATISMi remains negative and significant (p < 0.01).

  27. We repeat all of our analyses in Table 2 using the three individual conservatism measures (CONSV_SCOREi, CONSV_BASUi, and CONSV_NEGSKEWi) separately. Overall, the results are very similar to those we tabulate, and our inferences are unchanged. We do not tabulate in Table 2 (nor in other Tables) results from using the three individual conservatism measures for the sake of brevity.

  28. Controlling for special items also partially addresses the possibility that some measures of conservatism are influenced by transitory components in earnings that are unrelated to conditional conservatism. For example, CONSV_NEGSKEWi is affected by large, untimely transitory charges. Untimely transitory charges are unrelated to conditional conservatism because their recognition in earnings occurs after their economic effect.

  29. Because the distributions of AF_ERRORi,q and AF_DISPi,q are not symmetric (as Table 1 Panel B shows), we repeat our analyses after replacing AF_ERRORi,q and AF_DISPi,q with their square roots. Our inferences are unaffected. We also repeat all of our analyses in Table 3 using the three individual conservatism measures (CONSV_SCOREi,q, CONSV_BASUi,q, and CONSV_NEGSKEWi,q) separately. The results (untabulated) are very similar to those based on the aggregate measure (CONSERVATISMi,q) and our inferences are unchanged.

  30. We use raw values instead of ranks in this analysis. In Table 4, we do not test for differences in the means and medians of GAAP versus Street earnings for our aggregate conservatism measure (CONSERVATISMi) because the aggregate measure is standardized to be between zero and one within the GAAP and Street samples. Therefore the GAAP and Street means and medians of the aggregate measure are very similar by construction.

  31. This data requirement results in only 15,307 firm-quarters for forecast error and 11,651 firm-quarters for forecast dispersion. We first lose about one-half of the data because GPS forecasts are not available before 2003. We then lose about 60 % of the data when we require Street earnings and GAAP earnings differ. Finally, we lose about one-third of the remaining data because of missing GPS forecasts from 2003 onward.

  32. Our controls in Table 5 include SPEC and SP_MAG. Results are qualitatively similar when we exclude them.

  33. In a sensitivity analysis, we further restrict the sample for our Table 5 columns (4)–(5) analyses to observations where analysts issue both GAAP and non-GAAP forecasts on the same day and GAAP and non-GAAP forecasts differ (Christensen et al. 2013). Results are similar to those we report in Table 5 columns (4)–(5) (p < 0.01 and p < 0.05, respectively), despite a smaller sample size.

  34. We also estimate Eq. (7) without fixed effects because the coefficient estimates of fixed effect logit models are inconsistent (e.g., Greene 1997; Greene 2004). Our results are not affected when we include or exclude year and industry fixed-effects.

  35. Because the distribution of EXCLi,q is not symmetric (as Table 1, Panel C, shows), we repeat our analyses after replacing EXCLi,q with its square root while retaining its sign. Our inferences are unaffected.

  36. Results in Table 7 are similar when we replace CONSERVATISMi,q with CONSV_SCOREi,q, CONSV_BASUi,q, or CONSV_NEGSKEWi,q, respectively.

  37. We do not include SP_MAGi,q in column (2) to avoid a mechanical relation between the exclusion magnitude and the special item magnitude.

  38. In a hand-collected sample, Whipple (2014) finds that over 25 percent of other item exclusion observations include transitory components. Also, analysts can exclude recurring earnings components that contain transitory items and those transitory items can be the result of conditional conservatism. An example would be a change in estimate in response to bad news (e.g., useful lives of fixed assets) that increases depreciation or amortization expenses.

  39. We calculate other item exclusions as total exclusions (the difference between Street and GAAP earnings) minus special items. If the magnitude of total exclusions is less than that of special items, we set special item exclusions equal to total exclusions and set other exclusions to zero. There are at least two potential sources of measurement error in classifying both special item and other item exclusions. First, we assume that, when a firm’s earnings contain a special item, the difference between Street earnings and GAAP earnings is caused by the exclusion of special items rather than the exclusion of other items. Second, special items may be excluded even though Street earnings equal GAAP earnings. We acknowledge that the potential measurement error in both special item and other item exclusions that could obscure differences in results between them.

  40. Several researchers, including Lipe (1986), Kormendi and Lipe (1987), and Collins and Kothari (1989) document that equity prices are more closely associated with more persistent, i.e., permanent, earnings.

  41. The interaction of conservatism and EXCLi,q is positive and significant for two of the three individual conservatism measures (CONSV_CSCOREi,q and CONSV_NEGSKEWi,q) when we use them in place of CONSERVATISMi,q.

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Acknowledgments

We thank workshop participants at Florida State University, Hong Kong University of Science and Technology, attendees and our discussant, Anup Srivastava, at the 2012 American Accounting Association Annual Meetings, Bruce Billings, Richard Morton, Haifeng You, Guochang Zhang, two anonymous reviewers, and especially Richard Sloan (editor) for helpful comments and suggestions. Hsu acknowledges financial support from the Hong Kong Research Grants Council (DAG05/06.BM04). Jin acknowledges financial support from the MOE through the Institute of Accounting and Finance at SUFE (13JJD790019), the National Natural Science Foundation of China (71072036, 71272012 and 71472114), and the Program for IRTSHUFE.

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Authors and Affiliations

  1. College of Business, Florida State University, 821 Academic Way, Tallahassee, FL, 32312, USA

    Frank Heflin

  2. Department of Accounting, HKUST Business School, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Charles Hsu

  3. School of Accountancy, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai, China

    Qinglu Jin

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  1. Frank Heflin
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  2. Charles Hsu
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  3. Qinglu Jin
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Correspondence to Frank Heflin.

Appendix

Appendix

See Table 9.

Table 9 Variable definition (in alphabetical order)
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Heflin, F., Hsu, C. & Jin, Q. Accounting conservatism and Street earnings. Rev Account Stud 20, 674–709 (2015). https://doi.org/10.1007/s11142-014-9311-x

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