Abstract
In this paper, I argue that we can use consumer and investor perceptions to forecast short-term fluctuations in asset prices. Using tweets scraped from Twitter between 2009 and 2019, I perform textual analysis to construct daily sentiment indices. While other scholars have relied on third-party companies to complete this task, doing so limits our potential understanding of sentiments' effects on asset pricing. The sentiment indices I constructed are numerical, not dichotomous, scores, which allows to control for sentiment strength. Results indicate that sentiments can forecast daily stock returns and volatility jumps.
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