Using data from a Twitter-like investor social platform, we document evidence consistent with self-enhancing transmission bias. We find investors are more likely to post about their better-performing stocks. Their followers are more likely to buy the posted stocks than other non-posted stocks. The effect of the postings on follow-up purchases is consistent with an attention-based interpretation: the postings bring the discussed stocks into the followers’ choice set and increase their purchases. We also find that postings’ effect on follow-up purchases is related to postings’ perceived credibility. The performance-postings relationship is stronger among more volatile stocks and the relationship between postings and follow-up purchases is stronger among stocks with higher recent returns, shedding light on the spread of high-variance and extrapolative strategies. We also document that the social network features influential nodes.

We use COVID-19 and sell-side analysts as an experiment to study the effects of gender on labor productivity. We find that the forecast accuracy of female analysts declined more than that of male analysts, especially when schools were closed and among analysts who were more likely to have young children, were inexperienced, were busier, or lived in southern states of the US. Relative to male analysts, females also reduced their forecast timeliness and resorted to more heuristic forecasts but did not reduce coverage or updating frequency. Relative to pre pandemic, female analysts’ careers were more negatively affected than male analysts’. Overall, our results show that the pandemic impacted female analysts more than males through the quality of their forecasts but not the quantity.

We examine how stakes affect investor behaviors. In our unique setting, investors trade stocks in real accounts using their own money and simultaneously in a simulated setting. Our real-world within-investor estimation shows that investors exhibit stronger biases and perform worse in higher-stakes real accounts than in lower-stakes simulated accounts. Investors exhibit strong biases in both types of accounts, and the biases in both are strongly positively correlated. Such behavioral consistency suggests that low-stakes experiments are informative about real-world behaviors. Using additional account-level datasets, we demonstrate external validity by documenting a stronger (reverse) disposition effect on stocks (funds) with greater portfolio weights.

We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.