Textual Sentiment, Option Characteristics, and Stock Return Predictability - Cathy Yi-Hsuan Chen; Matthias R. Fengler; Wolfgang Karl Härdle; Yanchu Liu (Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", 2018)
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.
Keywords: C58; G12; G14; G41; 330; investor disagreement; option markets; overnight information; stock return predictability; textual sentiment; topic model; trading-time information