Measurement Error and Nonlinearity in the Earnings-Returns Relation

Measurement Error and Nonlinearity in the Earnings-Returns Relation 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. Review of Quantitative Finance and Accounting Springer Journals

Measurement Error and Nonlinearity in the Earnings-Returns Relation

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Kluwer Academic Publishers
Copyright © 1998 by 1998 Kluwer Academic Publishers
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
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