Does Social Media Sentiment Matter in the Pricing of U.S. Stocks?

This paper applies a recently developed social media-based sentiment proxy for the construction of a new risk factor for sentiment-augmented asset pricing models on U.S. equities. Accounting for endogeneity, autocorrelation and heteroskedasticity in a GMM framework, we find that the inclusion of sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017) for di ff erent industry and style portfolios like size, value, profitability, investment. The sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability.