Loss Aversion Online: Emotional Responses to Financial Booms and Crashes

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.

💡 Research Summary

The paper investigates how large‑scale financial events shape emotional expression on social media by analyzing Reddit discussions from both finance‑focused and non‑finance communities. Two distinct macro‑economic episodes are examined: a market boom announced on November 8 2024 and a market crash reported on April 2 2025. The authors construct balanced treatment (financial) and control (non‑financial) groups using a pre‑event stability window (April 6–May 6 2023) and apply bidirectional pruning to ensure covariate balance (maximum standardized mean difference ≤ 0.166). After excluding users active in both groups, the final dataset comprises roughly 320 K posts and 2.8 M comments across 15 financial subreddits (e.g., r/personalfinance, r/investing) and 11 non‑financial subreddits (e.g., r/explainlikeimfine).

Textual content is filtered for English, de‑duplicated, and then annotated for sentiment (VADER), seven basic emotions (DistilRoBERTa‑fine‑tuned), and a suite of LIWC categories covering affect, motives, and cognitive processes. Daily proportions of posts/comments containing each label are computed, normalizing for subreddit activity.

Two quasi‑experimental designs are employed. First, Difference‑in‑Differences (DiD) models estimate short‑term (±10 days) and medium‑term (±30 days) effects, with the interaction term (financial × event) capturing the causal shift relative to the control group. Second, a Bayesian Structural Time‑Series (Causal Impact) model uses the non‑financial series as covariates to predict a counterfactual trajectory for the financial series; deviations post‑event constitute the causal effect, with significance defined by posterior probability > 95% and credible intervals excluding zero.

Key findings:

• Crash (April 2025) – Short‑term DiD shows increases in surprise (+3.63 pp) and certainty (+3.39 pp) in financial posts, and a rise in fear (+0.67 pp) in comments. Medium‑term DiD reveals a drop in positive sentiment (–2.22 pp), a rise in negative sentiment (+2.34 pp), and reduced achievement language (–2.50 pp) in posts; comments exhibit higher anger (+0.98 pp) and sadness (+0.97 pp). Causal Impact confirms a 2.54 % decline in positive sentiment and a 6.49 % increase in negative sentiment, with pronounced spikes in anger and sadness.

• Boom (November 2024) – Short‑term DiD indicates decreases in reward language (–4.25 pp) and anger (–2.06 pp) in posts, while comments show modest gains in joy (+1.32 pp) and cognitive processing (+1.74 pp). Medium‑term DiD finds elevated surprise, joy, positive sentiment, reward, achievement, and certainty (all between +0.6 and +1.4 pp) in comments. Causal Impact yields mixed results: sadness in posts falls (–10.11 %), yet anger in posts rises sharply (+18.73 %). Overall, emotional shifts during the boom are weaker and more heterogeneous than during the crash.

Statistical significance is robust across all reported coefficients (p < 0.05 for DiD, posterior > 95% for Causal Impact).

The authors interpret these asymmetries through the lens of loss aversion and negativity bias: losses (crash) provoke stronger, more persistent negative affect, whereas gains (boom) elicit muted or ambivalent responses. The study demonstrates that behavioral‑economic theories, traditionally validated in labs or surveys, manifest in real‑time, large‑scale online discourse.

Methodologically, the work showcases careful group balancing, rigorous exclusion of cross‑group users, and the combined use of DiD and Bayesian time‑series to triangulate causal claims. Ethical considerations include adherence to Reddit’s API terms, anonymization of data, and the determination that institutional review board approval was unnecessary.

Implications are twofold: (1) monitoring Reddit sentiment can serve as an early warning system for population‑level mental‑health stress linked to financial turbulence; (2) platform designers and policymakers can leverage these insights to develop targeted interventions that mitigate the psychological fallout of market downturns, fostering healthier online communities. The paper thus contributes both substantive evidence on the psychological impact of macro‑economic events and a methodological blueprint for future large‑scale social‑media causal analyses.