Textual Sentiment, Option Characteristics, and Stock Return Predictability

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2018-023

Classification and Topics

• Classification: Wirtschaft ; Financial Econometrics ; Asset Pricing; Trading Volume; Bond Interest Rates ; Information and Market Efficiency; Event Studies; Insider Trading ; Behavioral Finance: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets‡
• Subject: investor disagreement ; option markets ; overnight information ; stock return predictability ; textual sentiment ; topic model ; trading-time information

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Chen, Cathy Yi-Hsuan ; Fengler, Matthias R. ; Härdle, Wolfgang Karl ; Liu, Yanchu
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/230734
• Last update: 15.03.2025, 12:46 AM CET