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Asymmetry in earnings timeliness and persistence: a simultaneous equations approach

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Abstract

This study addresses simultaneity bias in piecewise linear forms of the earnings-return relation. We specify an overidentified system of simultaneous equations that incorporates both asymmetric earnings timeliness and asymmetric earnings persistence specifications and implement two-stage least squares for this piecewise linear system. Estimation of a system that is piecewise linear in endogenous variables presents several issues that are unprecedented in the accounting literature. Findings provide evidence that the asymmetric timeliness specification is particularly affected by simultaneity and that failing to correct for simultaneity results in coefficient estimates that potentially understate the degree of asymmetric earnings timeliness. Moreover, inferences regarding how conditional conservatism has evolved over time are sensitive to whether OLS or 2SLS coefficients are used as the basis of comparison.

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Notes

  1. Although Dietrich et al. (2007) raise concerns about coefficient estimates from the Basu (1997) specification that derive from the endogenous nature of return, that study does not pursue a correction for this endogeneity.

  2. This suggestion is consistent with the Ohlson (1995) and Feltham and Ohlson (1995) frameworks where the value of the firm is a function of the persistence of abnormal earnings.

  3. Other studies raise concerns about measuring conditional conservatism using coefficients from the piecewise linear earnings-on-return model that are not directly related to endogeneity (e.g., Givoly et al. 2007; Patatoukas and Thomas 2011). The extent to which these other concerns remain after consideration of simultaneity is beyond the scope of our study.

  4. For ease of exposition, throughout we use the same notation for coefficients and error terms in the OLS and corresponding 2SLS equations. In all likelihood, they differ. Throughout the paper, variable subscripts i and t refer to firm i and fiscal year t, respectively.

  5. We thank an anonymous referee for pointing out that inclusion of exogenous variables in the second-stage equations, as is done in Beaver et al. (1997), is econometrically unnecessary for the purpose of identification. As long as the number of excluded instruments from an equation meets or exceeds the number of endogenous variables in the equation, identification is achieved. This point applies both to the linear and piecewise linear systems.

  6. Because R and X are endogenous, their indicator and interaction functions are endogenous by definition.

  7. Please refer to Wooldridge (2002, pp. 235–237).

  8. The forbidden regression is not without precedent in the accounting and finance literatures (e.g., Hanlon et al. 2003; Johnson 2003).

  9. Note that we use the fitted values from Eqs. (11a–11d) as additional instruments in the first-stage regressions under the correct 2SLS approach. That is not the same as the forbidden regression, where these fitted values are substituted directly into the second-stage equations.

  10. We estimate ERM, SMB, and HML firm-year coefficients using daily CRSP returns and daily factor returns from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  11. Following Lev and Sougiannis (1996), we require at least four other firms in the two-digit SIC group.

  12. We assign the percentage change in dividends variable, PCDPS, a value of zero for those firm-years wherein a firm did not pay dividends in both the current and prior year.

  13. Also, refer to http://www.stata.com/support/faqs/stat/2sls.html for additional discussion concerning the lack of statistical meaning of R 2 in the context of 2SLS.

  14. Larcker and Rusticus (2010) point out the importance of the over-identifying restrictions test before conducting the Hausman (1978) test for endogeneity. The over-identifying restrictions test is a test of the joint null that the instruments used in the first-stage regression are exogenous and that the exclusion restriction is appropriate (i.e., appropriate omission of the instruments from the second-stage equation). Untabulated findings indicate joint rejection of the null. However, additional untabulated findings from specifications in which we include 13 of our 15 exogenous variables in the second-stage model indicate the null cannot be rejected at the 0.05 level. This latter finding suggests that the rejection of the null in which all fifteen instruments are excluded is attributable to our exclusion restrictions rather than lack of exogeneity of the instruments.

  15. We do not specifically discuss estimated coefficients from the first-stage models. In general, inferences are similar across the 1963–1990 and 1963–2008 sample periods.

  16. We do not consider a partial R 2 or partial F-test, as we do not have non-instrument control variables in our system.

  17. The similarity in first-stage model fit between the linear and piecewise linear specifications for return and earnings is expected. The instrumentation across specifications differs only by the four additional instruments from Eq. (11a), the primary role of which is to facilitate instrumentation of the indicator and interaction functions of return and earnings in the piecewise linear specification. Accordingly, these additional four variables have little effect on the instrumentation of return and earnings themselves.

  18. The forbidden regression approach also involves estimating fitted values for return and earnings using Eqs. (7) and (8), rather than Eqs. (14b) and (15b), respectively. However, there is little effective difference in the resulting fitted values for return and earnings across these alternatives, as discussed in footnote 16.

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Acknowledgments

We appreciate the helpful comments of Stephen Ryan (editor), two anonymous referees, John Abowd, Tom Mroz, Daniel Taylor, Tim Vogelsang, and seminar participants at the University of North Carolina at Chapel Hill. We acknowledge funding from the KPMG research fund at the University of North Carolina. Edward Owens gratefully acknowledges funding from the Deloitte Doctoral Fellowship.

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Authors and Affiliations

  1. Stanford Graduate School of Business, Stanford University, Stanford, CA, 94305, USA

    William H. Beaver

  2. Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC, 27599, USA

    Wayne R. Landsman

  3. Simon Graduate School of Business, University of Rochester, Rochester, NY, 14627, USA

    Edward L. Owens

Authors
  1. William H. Beaver
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  2. Wayne R. Landsman
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  3. Edward L. Owens
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Correspondence to William H. Beaver.

Appendix

Appendix

See Table 7.

Table 7 Variable definitions
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Beaver, W.H., Landsman, W.R. & Owens, E.L. Asymmetry in earnings timeliness and persistence: a simultaneous equations approach. Rev Account Stud 17, 781–806 (2012). https://doi.org/10.1007/s11142-011-9174-3

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