A unique data set from a large Ponzi scheme allows me to study word‐of‐mouth diffusion of investment information. Investors could join the scheme only by invitation from an existing member, which allows me to observe how the idea spreads from one person to the next based on inviter‐invitee relationships. I find that the observed social network has a scale‐free connectivity structure, which significantly facilitates the diffusion of the investment idea and contributes to the growth and survival of the socially spreading Ponzi scheme. I further find that investors invest more if their inviter has comparatively higher age, education, and income.

We develop a method for defining groups of peer firms on the basis of joint analyst coverage. Besides industry boundaries, analysts’ coverage choices reflect other aspects of firm relatedness such as business model. We find that the analyst-based method produces substantially more homogeneous groups of firms compared to common industry classifications, and has a number of other desirable properties. The paper has two broader implications. First, it demonstrates the advantages of a self-organizing approach to classification, as opposed to a hierarchical system. Second, it illustrates a new positive information production externality generated by the institution of security market analysis.

We find that firms are more likely to split their stock if their peer firms have recently done so. The effect is comparable to an increase of 40–50% in the share price. Splitting probability is also increasing in the announcement returns of peer splits. These results are consistent with social learning from peers’ actions and outcomes. The unique features of the setting and various further tests render alternative explanations unlikely. We find no clear benefit in following successful peer splitters. Firms are sometimes suspected to succumb to imitation, and the effect we show could be a case in point.

This study examines whether sell-side equity analysts engage in “social learning” in which their earnings forecasts for certain firms are influenced by the forecasts and outcomes of “peer” analysts associated with other firms in their respective portfolios. We find that analyst optimism is negatively correlated with recent forecast errors, by peers, on other firms in the analyst's portfolio. An analyst is also more likely to issue “bold” forecasts when peers recently issued similar forecasts for other portfolio firms. Analysts learn more from peers with similar personal characteristics. Overall, social learning benefits analysts and improves their forecast accuracy.

We study the aggregate impact of information spillovers emerging from industry star firms. Changes in stars’ relative earnings growth predict future earnings growth, consensus earnings surprises, and job postings of same-industry nonstar firms. Star-firm performance also predicts industry-level GDP and employment growth. Price markup and innovation spillovers are potential channels underlying these patterns. Our results further show that this performance predictability is not fully incorporated into nonstars’ stock prices. A long–short portfolio based on star firms’ earnings growth earns an annualized 6-factor alpha of 8.7%. Together, our findings provide consistent evidence of the economic importance of star firms.