We examine whether Regulation Fair Disclosure (Reg FD) has reduced the informativeness of analysts’ information outputs. For a sample of financial analysts’ earnings forecasts and recommendations released in a 2-year window around Reg FD's effective date, we show that in the post-Regulation FD period the absolute price impact of information disseminated by financial analysts is lower by 28%. We also show that the drop in price impact varies systematically with brokerage house and stock characteristics related to the level of selective disclosure prior to Reg FD. Based on the time-series and cross-sectional evidence we conclude that Reg FD has been effective in curtailing selective disclosure.

We study the determinants and market impact of sell-side debt research. Analyzing a sample of 5920 debt reports published by 15 brokerage firms from 1999 to 2004, we document that companies with a higher probability of financial distress, lower market-to-book ratio, larger debt, and higher leverage receive more debt research. In addition, we document higher frequency of debt reports around credit ratings downgrades and find that their publication impacts equity prices. The evidence enhances our understanding of the nature of the market forces shaping sell-side debt research and its effect on price formation.

Prior research concludes that financial analysts do not process public information efficiently in generating their earnings forecasts. The ordinary least squares (OLS) regression-based tests used in prior studies assume implicitly that analysts face a quadratic loss function. In contrast, we argue that analysts likely face a linear loss function, and hence, try to minimize their absolute forecast errors. We conduct and compare rational expectations tests using these two alternative loss functions. We reproduce most prior findings of forecast inefficiency with OLS regressions, but find virtually no evidence of forecast inefficiency with least absolute deviation regressions, where we explicitly assume a linear loss function.

We investigate to what extent the market uses information that is predictive of whether earnings will meet or beat the analyst consensus forecast of earnings ( MBE henceforth): measures of a firm's incentives to engage in MBE behavior, measures of constraints on MBE , measures of past MBE practices by firm and industry, and other variables. Using the Mishkin test framework and Bonferroni‐adjusted p ‐values, we document that of a total of 21 variables, the market inefficiently uses information in one difficulty measure and four other predictors, suggesting that strong empirically and theoretically grounded relationships concerning MBE behavior are more likely to be unraveled by the market. We further show that a portfolio based on the difference between the objective MBE probability and the market‐assessed MBE probability generates significant abnormal returns. The documented return anomaly is distinct from other known anomalies and cannot be fully explained by arbitrage risk or transaction costs.

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).

Our study introduces analyst/investor days, a new disclosure medium that allows for private interactions with influential market participants. We also highlight interdependencies in the choice and information content of analyst/investor days and conference presentations, a well-researched disclosure medium that similarly allows for private interactions. Analyst/investor days are less frequent, but with longer duration and greater price impact than conference presentations. They are mostly hosted by firms that already have opportunities to interact with investors at conferences, but whose complex and diverse activities make the short duration and rigid format of a conference presentation an imperfect solution to these firms' information problems. Analyst/investor days and conference presentations tend to occur in different quarters, consistent with their competing for the time and attention of senior management. When these two mediums are scheduled in close temporal proximity to each other, analyst/investor days diminish the information content of conference presentations, but not vice versa, consistent with managers' favoring analyst/investor days over conference presentations as a disclosure medium. JEL Classifications: D82; M41; G11; G12; G14. Data Availability: Data are publicly available from the sources identified in the paper.

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.