Measurement Error and Nonlinearity in the Earnings-Returns Relation |
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Authors: | Beneish Messod Harvey Campbell |
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Affiliation: | (1) Indiana University, USA;(2) Duke University and NBER, USA |
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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. |
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Keywords: | measurement error unexpected earnings nonparametric estimation |
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