Transitory noise in reported earnings: Implications for forecasting and valuation

Abstract

The paper considers a setting where the present value of expected dividends determines price and the information to forecast the future includes reported earnings. In the model, reported earnings have been garbled by transitory noise, which cannot be inferred. ‘True’, but now unobservable, earnings are permanent as in Ohlson (1992). The stage setting result shows that capitalized expected reported earnings for the next period equals price regardless of the noise. More subtle is the influence of current reported earnings on the forecast of future expected earnings and, relatedly, the valuation in terms of the history of data. Because of the noise term, Bayesian updating implies that the investor uses the entire earnings history to learn about permanent earnings and to forecast future expected reported earnings. Specifically, the main result shows that the next period’s expected earnings equal a weighted average of (i) current reported earnings and (ii) beginning-of-the-period expected earnings for the current period. This framework is often referred to as ‘adaptive expectations’ because there is gradual learning and updating. It depends critically on dividend policy irrelevance. The paper goes on to show that the weight on current earnings (term (i)) decreases as the noise increases. The model has testable implications for returns on earnings regressions and how one operationalizes value-relevance.