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Measurement Error and Nonlinearity in the Earnings-Returns Relation

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  • Beneish, Messod D
  • Harvey, Campbell R

Abstract

There is a long history of research which examines the relation between unexpected earnings and unexpected returns on common stock. Early literature used simple linear regression models to describe this relation. Recently, a number of authors have proposed nonlinear models. These authors find that the earnings-returns relation is approximately linear for small changes but is 'S'-shaped globally. However, unexpected earnings are generated by the sum of a measurement error and a true earnings innovation, so the apparent nonlinearity could be an artifact of nonlinearity in the measurement errors. Using a research design that minimizes the presence of measurement errors, we provide evidence consistent with the hypothesis that measurement errors contribute to the nonlinearities in the earnings-returns relation. While we are not suggesting that the earnings-returns relation is linear, our evidence suggests that there is no advantage to using a nonlinear model for large firms that are widely followed by analysts. Copyright 1998 by Kluwer Academic Publishers

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  • Beneish, Messod D & Harvey, Campbell R, 1998. "Measurement Error and Nonlinearity in the Earnings-Returns Relation," Review of Quantitative Finance and Accounting, Springer, vol. 11(3), pages 219-247, November.
    • Handle: RePEc:kap:rqfnac:v:11:y:1998:i:3:p:219-47
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    Cited by:

    1. Mohamed Naceur Mahjoubi & Ezzeddine Abaoub, 2015. "Earnings Response Coefficient as a Measure of Market Expectations: Evidence from Tunis Stock Exchange," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 377-389.
    2. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2020. "The information content of funds from operations and net income in real estate investment trusts," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Peter Hecht & Tuomo Vuolteenaho, 2005. "Explaining Returns with Cash-Flow Proxies," NBER Working Papers 11169, National Bureau of Economic Research, Inc.
    4. Mohamed Sellami, 2006. "Typologie des déterminants comptables de la valeur : Apports de l'approche économique de l'information dans la mesure de la valeur," Post-Print halshs-00558252, HAL.
    5. Robert Freeman & Adam Koch & Haidan Li, 2011. "Can historical returns-earnings relations predict price responses to earnings news?," Review of Quantitative Finance and Accounting, Springer, vol. 37(1), pages 35-62, July.
    6. Thomas Hemmer & Eva Labro, 2019. "Management by the Numbers: A Formal Approach to Deriving Informational and Distributional Properties of “Unmanaged” Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 57(1), pages 5-51, March.
    7. Derann Hsu & Cheng-Huei Chiao, 2011. "Relative accuracy of analysts’ earnings forecasts over time: a Markov chain analysis," Review of Quantitative Finance and Accounting, Springer, vol. 37(4), pages 477-507, November.
    8. Helena Isidro & José G. Dias, 2017. "Earnings quality and the heterogeneous relation between earnings and stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1143-1165, November.
    9. Arturo Leccadito & Stefania Veltri, 2015. "A regime switching Ohlson model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2015-2035, September.

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