In this paper, we propose a rational learning‐based explanation for the predictability in financial analysts' earnings forecast errors documented in prior literature. In particular, we argue that the serial correlation pattern in analysts' quarterly earnings forecast errors is consistent with an environment in which analysts face parameter uncertainty and learn rationally about the parameters over time. Using simulations and real data, we show that the predictability evidence is more consistent with rational learning than with irrationality (fixation on a seasonal random walk model or some other dogmatic belief).

The traditional view of equity analysts is that they are a source of new information about future cash flows. We broaden this view by demonstrating that equity analysts are also a substantive source of new information about priced risk. In particular, we document that, when announced, changes in analyst risk ratings distinctly and significantly affect equity returns, and are generally followed by significant changes in Fama–French factor loadings. Also, while less frequent than credit rating changes, equity risk rating changes are timelier, and with a larger overall stock price impact than credit rating changes.

We examine the determinants and the informativeness of financial analysts' risk ratings using a large sample of research reports issued by Salomon Smith Barney, now Citigroup, over the period 1997–2003. We find that the cross‐sectional variation in risk ratings is largely explained by variables commonly viewed as measures of risk, such as idiosyncratic risk, size, book‐to‐market, and leverage. In addition, earnings‐based measures of risk, such as earnings quality and accounting losses, also contribute to explaining the cross‐sectional variation in the risk ratings. Finally, we document that the risk ratings can be used to predict future return volatility after controlling for other predictors of future volatility. We conclude that analysts play an important role as providers of information about investment risk.

Crowdsourcing—when a task normally performed by employees is outsourced to a large network of people via an open call—is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two‐day size‐adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.