Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
We distill sentiment from a huge assortment of NASDAQ news articles by means of machine learning methods and examine its predictive power in single-stock option markets and equity markets. We provide evidence that single-stock options react to contemporaneous sentiment. Next, examining return predictability, we discover that while option variables indeed predict stock returns, sentiment variables add further informational content. In fact, both in a regression and a trading context, option variables orthogonalized to public and sentimental news are even more informative predictors of stock returns. Distinguishing further between overnight and trading-time news, we find the first to be more informative. From a statistical topic model, we uncover that this is attributable to the differing thematic coverage of the alternate archives. Finally, we show that sentiment disagreement commands a strong positive risk premium above and beyond market volatility and that lagged returns predict future returns in concentrated sentiment environments.
investor disagreement option markets overnight information stock return predictability textual sentiment topic model trading-time information