GIFfluence: A Visual Approach to Investor Sentiment and the Stock Market

Ming Gu, David Hirshleifer, Siew Hong Teoh and Shijia Wu

MPRA Paper from University Library of Munich, Germany

Abstract: We study dynamic visual representations as a proxy for investor sentiment about the stock market. Our sentiment index, GIFsentiment, is constructed from millions of posts in the Graphics Interchange Format (GIF) on a leading investment social media platform. GIFsentiment correlates with seasonal mood variations and the severity of COVID lockdowns. It is positively associated with contemporaneous market returns and negatively predicts returns for up to four weeks, even after controlling for other sentiment and attention measures. These effects are stronger among portfolios that are more susceptible to mispricing. GIFsentiment positively predicts trading volume, market volatility, and flows toward equity funds and away from debt funds. Our evidence suggests that GIFsentiment is a proxy for misperceptions that are later corrected.

Keywords: GIF; Dynamic Visuals; Investor Sentiment; Attention; Salience; Social Finance; Stock Mispricing and Trading; Return Predictability; Anomalies; Mental Models; Narratives (search for similar items in EconPapers)
JEL-codes: C53 D84 D85 G12 G14 (search for similar items in EconPapers)
Date: 2025-12-21
New Economics Papers: this item is included in nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:127438

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