Social Media Emotions and Market Behavior *

40 Pages Posted: 25 Apr 2024

Date Written: June 25, 2024

Abstract

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. In line with extant literature, 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. Central to my study, I document the persistent influence of sadness on subsequent returns over one to three days and show the insignificance of the one-dimensional valence metric, underscoring the critical importance of analyzing distinct emotional states. This approach allows for a deeper and more accurate understanding of the ways in which investor sentiments drive market movements.

Keywords: Deep Learning, Investor Emotions, Text Analysis, Social Media, Return Predictability. JEL Codes: G41, L82

Suggested Citation

Vamossy, Domonkos F., Social Media Emotions and Market Behavior * (June 25, 2024). Available at SSRN: https://ssrn.com/abstract=4806896 or http://dx.doi.org/10.2139/ssrn.4806896

Domonkos F. Vamossy (Contact Author)

University of Pittsburgh ( email )

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