Social media, news media and the stock market

Abstract: We contrast the impact of traditional news media and social media coverage on stock market volatility and trading volume. We develop a theoretical model of asset pricing and information processing, which allows for both rational traders and a variety of commonly studied behavioral biases. The model yields several novel and testable predictions about the impact of news and social media on asset prices. We then test the model’s theoretical predictions using a unique dataset which measures coverage of individual stocks in social and news media using a broad spectrum of print and online sources. Stocks with high social media coverage in one month experience high idiosyncratic volatility of returns and trading volume in the following month. Conversely, stocks with high news media coverage experience low volatility and low trading volume in the following month. These effects are statistically and economically significant and robust to controlling for stock and time fixed effects, as well as time-varying stock characteristics. The empirical evidence on news media is consistent with a market in which some traders are overconfident when interpreting new information. The evidence on social media is consistent with Tetlock (2011)’s “stale news” hypothesis (investors treat repeated information on social networks as though it were new) and with a model where investors’ perceptions are subject to random sentiment shocks.