We develop a tool that extracts emotions from social media text data. Our
methodology has three main advantages. First, it is tailored for financial
context; second, it incorporates key aspects of social media data, such as
non-standard phrases, emojis and emoticons; and third, it operates by
sequentially learning a latent representation that includes features such as
word order, word usage, and local context. This tool, along with a user guide
is available at: https://github.com/dvamossy/EmTract. Using EmTract, we explore
the relationship between investor emotions expressed on social media and asset
prices. We document a number of interesting insights. First, we confirm some of
the findings of controlled laboratory experiments relating investor emotions to
asset price movements. Second, we show that investor emotions are predictive of
daily price movements. These impacts are larger when volatility or short
interest are higher, and when institutional ownership or liquidity are lower.
Third, increased investor enthusiasm prior to the IPO contributes to the large
first-day return and long-run underperformance of IPO stocks. To corroborate
our results, we provide a number of robustness checks, including using an
alternative emotion model. Our findings reinforce the intuition that emotions
and market dynamics are closely related, and highlight the importance of
considering investor emotions when assessing a stock's short-term value.Comment: Substantial changes to the projec