Textual Sentiment, Option Characteristics, and Stock Return Predictability

Cathy Yi-Hsuan Chen, Matthias Fengler, Wolfgang Härdle and Yanchu Liu

No 2018-023, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: 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: investor disagreement; option markets; overnight information; stock return predictability; textual sentiment; topic model; trading-time information (search for similar items in EconPapers)
JEL-codes: C58 G12 G14 G41 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (20)

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Working Paper: Textual Sentiment, Option Characteristics, and Stock Return Predictability (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2018023

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