Textual Sentiment, Option Characteristics, and Stock Return Predictability
Type
working paper
Date Issued
2018
Author(s)
Abstract (De)
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
No
Eprints ID
258143
File(s)![Thumbnail Image]()
Name
EWP-1808.pdf
Size
3.73 MB
Format
Adobe PDF
Checksum (MD5)
ecfe143b99fdd183d727c56d5094d335