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Prior research documents the relatively low explanatory power of the earnings-return association in traditional models that regress returns on levels and changes in earnings. However, these studies fail to consider the impact of variation in discount rates, or risk, as a possible cause of the low explanatory power. In this study, I investigate the impact of controlling for risk on the explanatory power of the earnings-return relation. I begin by estimating two related regression models of annual returns on earnings and changes in earnings drawn from prior research. Then, to examine whether controlling for risk affects the explanatory power of the regressions, I sort observations into portfolios formed on various risk proxies, including market beta, firm size, earnings/price ratio, two implied cost of equity capital proxies , and the combination of beta and firm size. I document higher average adjusted R 2 s that suggest a 30% increase in explanatory power, and larger average coefficient estimates of earnings, when I estimate the return-earnings regressions within risk portfolios than those of the Easton and Harris and Easton and Pae models. These findings suggest that controlling for cross-sectional variation in risk, a denominator effect, improves the explanatory power of the model.

The Impact of Controlling for Risk on the Value Relevance of Earnings: Evidence from the U.S.