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Dissecting Earnings Recognition Timeliness

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  • RYAN T. BALL
  • PETER EASTON

Abstract

We dissect the portion of stock price change of the fiscal year that is recognized in reported accounting earnings of the year. We call this portion earnings recognition timeliness (ERT). The emphasis in our dissection is on empirical identification of two fundamental precepts of financial accounting: (1) the matching principle, which is manifested in the recognition of expenses in the same period as the related benefits (i.e., sales revenue) accrue; and (2) recognition of expenses in the current period due to changes in expectations regarding earnings of future periods (we refer to these expenses as the expectations element of expenses). Although the expectations element has implicitly been at the core of much of the recent empirical literature on asymmetry in the earnings/return relation, it has not been explicitly identified. This recent literature is based on the premise that bad news about the future leads to more recognition of expenses in the current period (such as write‐downs) whereas good news about the future tends to have a much lesser effect on expenses of the current period; asymmetry in the expenses/return relation is captured implicitly via the observation of asymmetry in the earnings/return relation (i.e., asymmetry in ERT). Since the ERT reflects the relation between sales revenue and returns, matched expenses and returns, as well as the relation between the expectations element of expenses and returns, a focus on the expectations element may lead to sharper inferences. Our straightforward empirical procedure permits a focus on this element.

Suggested Citation

  • Ryan T. Ball & Peter Easton, 2013. "Dissecting Earnings Recognition Timeliness," Journal of Accounting Research, Wiley Blackwell, vol. 51(5), pages 1099-1132, December.
  • Handle: RePEc:bla:joares:v:51:y:2013:i:5:p:1099-1132
    DOI: 10.1111/1475-679X.12018
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    Cited by:

    1. Ghysels, Eric & Ball, Ryan & Zhou, Huan, 2014. "Can we Automate Earnings Forecasts and Beat Analysts?," CEPR Discussion Papers 10186, C.E.P.R. Discussion Papers.
    2. Manuel Cano-Rodríguez & Manuel Núñez-Nickel, 2015. "Aggregation Bias in Estimates of Conditional Conservatism: Theory and Evidence," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 42(1-2), pages 51-78, January.
    3. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.

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