This paper examines the information content of firm-specific sentiment extracted from Twitter messages. We find that Twitter sentiment predicts stock returns without subsequent reversals. This finding is consistent with the view that tweets provide information not already reflected in stock prices. We investigate possible sources of return predictability with Twitter sentiment. The results show that Twitter sentiment provides new information about analyst recommendations, analyst price targets and quarterly earnings. This information explains about one third of the predictive ability of Twitter sentiment for stock returns. Taken together, our findings shed new light on whether and why social media content has predictive value for stock returns.

This paper investigates the impact of institutional herding on stock price formation, conditional on firms’ credit ratings, using 13F data from 1986 to 2019. In line with the current literature, we find herding intensity is driven by past returns consistent with momentum trading; however, we also find that herding is more sensitive to past returns for non-investment grade (NIG) stocks than investment grade (IG) stocks, resulting in a market bifurcation. We then examine the price impact of these trades and find that herding in NIG equities enhances price discovery. One plausible explanation is that information gradually diffuses within non-investment grade stocks, and herding behavior strengthens information discovery. Finally, we show both momentum-triggered herding and non-momentum-triggered herding contribute to price discovery among non-investment grade stocks.