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We examine how increased competition stemming from an innovation in financial technology influences sell-side analyst research quality. We find that firms added to Estimize, an open platform that crowdsources short-term earnings forecasts, experience a pervasive and substantial reduction in consensus bias and a limited increase in consensus accuracy relative to matched control firms. Long-term forecasts and investment recommendations remain similarly biased, alleviating the concern that the documented reduction in bias is a response to broad economic forces. At the individual analyst level, we find that bias reduction is more pronounced among close-to-management analysts, and that more biased analysts respond by reducing their coverage of Estimize firms. The collective evidence suggests that competition from Estimize improves sell-side research quality by discouraging strategic bias.

A number of sell-side healthcare analysts gain access to information outside the purview of management through Freedom of Information Act requests to the Food and Drug Administration for records on factory inspections, complaints, and drug and medical device applications. Using a difference-in-differences methodology, we find that buy (sell) recommendations and upgrades (downgrades) earn higher (lower) stock returns over the year following the receipt of FDA records. We also examine the type of information revealed in FDA factory inspection reports, and find that analysts are less likely to downgrade and are less pessimistic in their recommendations than the consensus recommendation when the information contained in the FDA report is not particularly severe. Our findings are consistent with a subset of analysts utilizing non-public information channels independent of management to gain value-relevant information about their covered firms.

Sell-side analysts employ different benchmarks when defining their recommendations. A buy for some brokers means the stock is expected to outperform its industry, while for other brokers, it means the stock is expected to outperform the market or some return threshold. We show that these stated benchmarks have implications for the distribution of recommendations, price reactions to recommendations, and the investment value of recommendations. We conclude that, depending on the question, academics may need to account for the benchmarks when studying analysts' outputs, and investors may find the benchmarks beneficial in interpreting analysts' advice. JEL Classifications: G10; G24.

Equity analysts are often hired by firms they cover. I document the extent to which this revolving door phenomenon impairs analysts' independence. I do this by examining the presence of biased research reports issued during the year before analysts are employed by a firm they cover. I find that during their final year, revolving door analysts bias their EPS forecasts, their target prices, and their recommendations in a direction that suggests that they are attempting to gain favor from their prospective employers. Specifically, relative to other analysts, revolving door analysts issue more optimistic reports on the firms that hire them, and they issue more pessimistic reports on firms that do not hire them. These results suggest the presence of strategic bias, although more innocuous interpretations cannot be completely ruled out. JEL Classifications: G14; G17; G24; G28; M41. Data Availability: Data are available from public sources identified in the text.

We use daily page views of analyst estimates, ratings, and target prices on Yahoo Finance to understand when users seek sell-side analyst research. Demand for this information is most pronounced on days with earnings announcements, management guidance, and All-Star analyst reports. Surprisingly, demand does not increase at Form 10-K and Form 10-Q filings. While the overall demand for analyst estimates is 19.9 percent less than for analyst ratings and target prices, on earnings announcement and management guidance days, this preference is reversed. Moreover, the demand for analyst information substantially trumps that of SEC filings and financial statement information. JEL Classifications: M41; G14; G24.

We study the joint effects of intercompany investing and reporting of equity method investments on the accuracy and dispersion of analysts' annual earnings-per-share (EPS) forecasts. We compare firm-year observations with and without equity method investments. We posit two non-mutually exclusive explanations for how equity method investments may affect analyst forecast properties. The Opacity Effect posits that the condensed equity method disclosures increase information asymmetry, increasing analysts' forecast errors and forecast dispersion. The Diversification Effect suggests that the diversification of the investor and its investee earnings streams enhances earnings predictability, decreasing analysts' forecast errors and forecast dispersion. Our findings are consistent with both effects operating in the analyst forecasting task. Additional analyses are consistent with the Opacity Effect dominating. This occurrence results, on net, in less accurate and more dispersed forecasts for firm-years with equity method investments. JEL Classifications: G14, M41

We investigate if earnings forecast accuracy matters to security analysts by examining its association with analyst turnover. Controlling for firm- and time-period effects, forecast horizon and industry forecasting experience, we find that an analyst is more likely to turn over if his forecast accuracy is lower than his peers. We find no association between an analyst's probability of turnover and his absolute forecast accuracy. We also investigate another observable measure of the analyst's performance, the profitability of his stock recommendations. There is no statistical relation between the absolute or relative profitability of an analyst's stock recommendations and his probability of turnover. We interpret our findings as indicating that forecast accuracy is important to analysts.