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- W2904139812 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." @default.
- W2904139812 created "2018-12-22" @default.
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- W2904139812 date "2018-07-18" @default.
- W2904139812 modified "2023-10-02" @default.
- W2904139812 title "Textual Sentiment, Option Characteristics, and Stock Return Predictability" @default.
- W2904139812 hasPublicationYear "2018" @default.
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