Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 023

Textual Sentiment, Option Characteristics, and Stock Return Predictability

Cathy Yi-Hsuan Chen
Matthias R. Fengler
Wolfgang Karl Härdle
Yanchu Liu

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;

JEL classification:
C58, G12, G14, G41