This paper develops a theory of the frequency of financial analysts' forecast revisions and then tests the empirical predictions of the model. Financial analysts act as information intermediaries for firms and investors and therefore their forecast revision frequency helps explain the equilibrium of the supply of and demand for earnings predictions and assessments of firm value. The theory is based on the analyst's costs of information gathering and the profits obtained from selling the information to investors. Our analysis is conducted in two stages. In the first stage, a single-period, Kyle (1985) model is used to determine the profits generated by privately informed investors who trade on the analyst's forecast revision. The analyst is assumed to be compensated as a function of these profits. In the second stage, the analyst's optimal revision frequency to collect and sell private information is determined. We find that the analyst's optimal revision frequency is increasing in the variance of liquidity trading volume, the volatility of the underlying earnings process, and the earnings-response coefficient and decreasing in the total number of informed traders who invest in the firm and the cost of revision. These theoretical results are developed into empirical hypotheses that the frequency of analysts' forecast revisions between earnings announcements is positively associated with variability of the earnings process, average prior trading volume, and earnings response coefficients, and negatively associated with skewness of prior trading volume, after controlling for firm size and prior average daily stock price changes. These hypotheses are tested cross-sectionally and we find significant support each of the hypothesized relations.

Financial Analysts' Forecasts and Stock Recommendations reviews research related to the role of financial analysts in the allocation of resources in capital markets. The authors provide an organized look at the literature, with particular attention to important questions that remain open for further research. They focus research related to analysts' decision processes and the usefulness of their forecasts and stock recommendations. Some of the major surveys were published in the early 1990's and since then no less than 250 papers related to financial analysts have appeared in the nine major research journals that we used to launch our review of the literature. The research has evolved from descriptions of the statistical properties of analysts' forecasts to investigations of the incentives and decision processes that give rise to those properties. However, in spite of this broader focus, much of analysts' decision processes and the market's mechanism of drawing a useful consensus from the combination of individual analysts' decisions remain hidden in a black box. What do we know about the relevant valuation metrics and the mechanism by which analysts and investors translate forecasts into present equity values? What do we know about the heuristics relied upon by analysts and the market and the appropriateness of their use? Financial Analysts' Forecasts and Stock Recommendations examines these and other questions and concludes by highlighting area for future research.

​Earnings forecasts are ubiquitous in today’s financial markets. They are essential indicators of future firm performance and a starting point for firm valuation. Extremely inaccurate and overoptimistic forecasts during the most recent financial crisis have raised serious doubts regarding the reliability of such forecasts. This thesis therefore investigates new determinants of forecast errors and accuracy. In addition, new determinants of forecast revisions are examined. More specifically, the thesis answers the following questions: 1) How do analyst incentives lead to forecast errors? 2) How do changes in analyst incentives lead to forecast revisions?, and 3) What factors drive differences in forecast accuracy?