Measurement Errors of Expected-Return Proxies and the Implied Cost of Capital

Despite their popularity as proxies of expected returns, the implied cost of capital's (ICC) measurement error properties are relatively unknown. Through an in-depth analysis of a popular implementation of ICCs by Gebhardt, Lee, and Swaminathan (2001) (GLS), I show that ICC measurement errors can be not only nonrandom and persistent, but can also be associated with firms' risk or growth characteristics, implying that ICC regressions are likely confounded by spurious correlations. Moreover, I document that biases in GLS' measurement errors are driven not only by analysts' systematic forecast errors but also by functional form assumptions, so that correcting for the former—a primary focus of the ICC literature—is insufficient by itself. From these findings, I argue that the choice between ICCs and realized returns involves a tradeoff between bias and efficiency, and suggest that realized returns should be used in conjunction with ICCs to make more robust inferences about expected returns.