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Analysts’ accrual-related over-optimism: do analyst characteristics play a role?

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Abstract

Bradshaw et al. (J Acc Res 39:45–74, 2001) find that analyst forecast over-optimism is greater for firms with high accruals. This “accrual-related over-optimism” is generally interpreted as evidence that analyst forecasts do not fully incorporate predictable earnings reversals associated with high accruals. We investigate whether analyst experience, access to resources (brokerage size), and portfolio complexity moderate the relation between over-optimistic forecasts and high accruals. We demonstrate the robustness of accrual-related over-optimism to controls for cash flow and prior forecast errors. We find that accrual-related over-optimism is lower for analysts with greater general experience and for analysts following fewer firms but find only limited evidence of lower accrual-related over-optimism for analysts from larger brokerages and for analysts following fewer industries.

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Notes

  1. Prior literature supports the idea that selective disclosure was reduced by Reg FD (Chen and Matsumoto 2006; Gintschel and Markov 2004).

  2. We review this literature in further detail below.

  3. Recent studies examining the accrual anomaly suggests that research focusing on accrual mispricing without also considering cash flow mispricing is incomplete (Barone and Magilke 2009; Desai et al. 2004; Yu 2007).

  4. For an example of how individual analyst attributes can be used to improve the efficiency of the consensus forecast, see Butler et al. (2007).

  5. Sloan (1996) does not investigate the source of this empirical regularity, but a line of literature suggests that distortions in accruals due to earnings management are responsible (Dechow and Dichev 2002; Richardson et al. 2005, 2006; Xie 2001). Alternatively, Drake et al. (2009) and Louis et al. (2007) suggest that the accrual anomaly is associated with low quality disclosure.

  6. Similarly, DeFond and Park (2001) document patterns in earnings response coefficients that suggest that market participants do not fully understand the reversing nature of abnormal working capital accruals.

  7. Similarly, Teoh and Wong (2002) show that analysts following firms that issue equity are more optimistic when issue-year accruals are high.

  8. Also, a significant body of research investigates the effect of experience on auditing tasks. (See, for example, Bonner (1990), Bonner and Walker (1994), Hammersley (2006), Libby and Frederick (1990), and Shelton (1999).).

  9. In general, these studies ask questions such as: “Does forecast accuracy improve as analyst experience increases?” Thus, the authors investigate the direct association between experience and forecast accuracy (i.e., experience is a main effect).

  10. For example, Clement (1999) adds controls for firm-year fixed effects and includes a different set of explanatory variables in his model.

  11. These studies ask questions such as: “Do analysts fully incorporate the information in prior stock returns into their forecasts?” and “Do more experienced analysts more fully incorporate the information in prior stock returns into their forecasts?” Thus, the authors investigate whether experience acts as a moderator variable in models of forecast accuracy (i.e., experience is part of an interaction).

  12. Our study is similar in spirit to Mikhail et al. (2003) in that we also investigate whether greater experience is associated with analysts’ ability to incorporate useful accounting information into their earnings forecasts, but our study differs from Mikhail et al. (2003) in important ways. For example, we test our empirical predictions by investigating the cross-section of annual analyst earnings forecasts rather than the time-series of quarterly individual analyst earnings forecasts. This allows us to consider a larger number of firms, which increases the generalizability of our findings, and, more important, allows us to ask: “Do analyst forecasts exhibit lower accrual-related over-optimism when analysts have a particular characteristic (e.g., higher general experience) relative to other analysts?” rather than: “Do analysts’ forecasts incorporate their past forecast errors to a greater extent as their firm-specific forecasting experience grows?” Moreover, we consider general experience, firm-specific experience, brokerage size, and portfolio complexity concurrently in order to determine which of these factors are important for attenuating accrual-related over-optimism.

  13. We estimate all regressions using ordinary least squares. All test statistics are based on Roger’s standard errors, which control for heteroscedasticity (White corrected) and are adjusted to account for any possible correlation of the residuals within firm clusters (Petersen 2009).

  14. Recall that we follow the existing accrual literature and rank our continuous independent variables. (See, for example, Bradshaw et al. (2001, 2006), Collins et al. (2003), Mashruwala et al. (2006), and Richardson et al. (2005).) Our use of ranks makes the results easier to interpret, reduces the influence of outliers, and allows us to form portfolios which better capture certain nonlinearities that might characterize the relations we study.

  15. Desai et al. (2004) investigate whether the accrual anomaly is distinct from the value-glamour phenomena. They find that accruals are associated with future returns even after controlling for value-glamour proxies employed in the finance literature (e.g., book-to-market).

  16. I/B/E/S codes analyst recommendations into five categories as follows: strong buy = 1; buy = 2; hold = 3; sell = 4; and strong sell = 5. We reverse this coding (i.e., strong buy = 5, strong sell = 1) so that a higher value indicates that the stock is more highly endorsed by the analyst. This transformation allows for a more intuitive interpretation of our results.

  17. Note that we run the previous models separately for four forecast horizon groups to more closely follow the methodology in Bradshaw et al. (2001). Here, we choose to control for forecast horizon in the model directly for parsimony.

  18. Clement (1999) assumes that active analysts would supply forecasts for firms they follow during this period and that analysts who release forecasts less than 30 days prior to the period end are more likely to simply be mimicking the forecasts of other analysts rather than relying on their own analyses.

  19. We estimate a main effects model because high collinearity between the main effect terms and the interaction terms is generally not problematic, but high collinearity between main effect terms can be an issue (Jaccard and Turrisi 2003).

  20. As an additional test, we follow Bradshaw et al. (2001) and estimate Eq. 1 at the firm-level using the consensus (median) forecast. We estimate the model in each of the 12 months following the prior year’s earnings announcement. Consistent with Bradshaw et al. (2001), we find that the coefficient on R WCaccr is negative and significant and that the magnitude of the coefficient decreases as the forecast horizon decreases.

  21. An example of a selective disclosure commonly used in the pre-Reg FD period is a restricted-access conference call (Bushee et al. 2004).

  22. In a pre-Reg FD setting, Bowen et al. (2002) find that conference calls improve the forecast accuracy of those analysts privy to the call, suggesting that Securities and Exchange Commission (SEC) concerns about selective disclosure pre-Reg FD were valid. More recent work suggests that the passage of FD reduced the amount of private information selectively disclosed to certain analysts (Agrawal et al. 2006; Chen and Matsumoto 2006; Francis et al. 2006).

  23. The SEC implemented Reg FD on October 23, 2000. Thus, we set Regfd equal to one for firms with fiscal year-ends after October 2000 and zero otherwise. We also perform the analyses after removing all observations with fiscal year-ends between June 2000 and June 2001. The inferences are consistent with those reported here.

  24. Gintschel and Markov (2004) provide similar evidence using analyst stock recommendations.

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Acknowledgments

We thank Peter Easton (the editor), two anonymous reviewers, Sami Keskek, James Myers, and workshop participants at Texas A&M University for helpful comments and suggestions. We also thank I/B/E/S International Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System. Michael Drake gratefully acknowledges financial support from the Deloitte & Touche Foundation and the Mays Business School while at Texas A&M University. Linda Myers gratefully acknowledges financial support from the Garrison/Wilson Chair at the University of Arkansas and from the PricewaterhouseCoopers Faculty Fellowship while at Texas A&M University.

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  1. Fisher College of Business, The Ohio State University, Columbus, OH, 43210, USA

    Michael S. Drake

  2. Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR, 72701, USA

    Linda A. Myers

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  1. Michael S. Drake
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Correspondence to Linda A. Myers.

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Drake, M.S., Myers, L.A. Analysts’ accrual-related over-optimism: do analyst characteristics play a role?. Rev Account Stud 16, 59–88 (2011). https://doi.org/10.1007/s11142-009-9118-3

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