  I explore the relationship between investor emotions expressed on social
media and asset prices. The field has seen a proliferation of models aimed at
extracting firm-level sentiment from social media data, though the behavior of
these models often remains uncertain. Against this backdrop, my study employs
EmTract, an open-source emotion model, to test whether the emotional responses
identified on social media platforms align with expectations derived from
controlled laboratory settings. This step is crucial in validating the
reliability of digital platforms in reflecting genuine investor sentiment. My
findings reveal that firm-specific investor emotions behave similarly to lab
experiments and can forecast daily asset price movements. These impacts are
larger when liquidity is lower or short interest is higher. My findings on the
persistent influence of sadness on subsequent returns, along with the
insignificance of the one-dimensional valence metric, underscores the
importance of dissecting emotional states. This approach allows for a deeper
and more accurate understanding of the intricate ways in which investor
sentiments drive market movements.
