Loss Aversion Online: Emotional Responses to Financial Booms and Crashes

2026-01-20

AI Summary

This study investigates how financial booms and crashes shape online users’ emotional expressions. Leveraging large-scale real-time data from both financial and non-financial Reddit communities, the research uniquely integrates a quasi-experimental design with causal inference methods—specifically difference-in-differences and Causal Impact—and employs LIWC lexicon-based metrics alongside daily sentiment indicators for quantitative analysis. The findings reveal that financial crashes significantly amplify negative emotions, whereas emotional responses during boom periods are weaker and less consistent, providing robust empirical support for loss aversion theory in digital behavioral contexts. By innovatively bridging behavioral economics with large-scale social media analytics, this work uncovers the asymmetric psychological impact of financial market volatility on public sentiment.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Humans and AI: Emotional Intelligence

Application Category

• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds

Abstract

Financial events negatively affect emotional well-being, but large-scale studies examining their impact on online emotional expression using real-time social media data remain limited. To address this gap, we propose analyzing Reddit communities (financial and non-financial) across two case studies: a financial crash and a boom. We investigate how emotional and psycholinguistic responses differ between financial and non-financial communities, and the extent to which the type of financial event affects user behavior during the two case study periods. To examine the effect of these events on expressed language, we analyze daily sentiment, emotion, and LIWC counts using quasi-experimental methods: Difference-in-Differences (DiD) and Causal Impact analyses during a financial boom and a financial crash. Overall, we find coherent, negative shifts in emotional responses during financial crashes, but weaker, mixed responses during booms, consistent with loss aversion. By exploring emotional and psycholinguistic expressions during financial events, we identify future implications for understanding online users'mental health and building connected, healthy communities.

Problem

• loss aversion
• financial events
• emotional expression
• social media
• online communities

Innovation

• loss aversion
• social media analysis
• Difference-in-Differences
• Causal Impact
• psycholinguistics