Sad But Accurate? Examining the Role of Discrete Emotions on Prediction Performance
Type
journal article
Date Issued
2026-04-28
Author(s)
Abstract
How do discrete emotions influence prediction accuracy? Drawing on appraisal-tendency framework and approach-avoidance models, we hypothesized that approach-oriented emotions with high certainty (anger, happiness) would enhance prediction accuracy through heuristic processing, while avoidance-oriented emotions with low certainty appraisals (fear, sadness) would trigger over-deliberation and impair performance. We further tested whether trust-in-feelings (TIF) moderates these relationships. Participants (N = 740) underwent emotion induction and TIF manipulation (high vs. none) before making predictions about weather, movie box office performance and stock market movements. Results contradicted predictions: fear and sadness improved weather prediction accuracy (ORs = 1.67-2.28), performing comparably to anger and happiness. Effects were domainspecific, emerging only for weather. TIF showed minimal effects (one of seven predictions reached significance), representing a failure to replicate Pham et al.'s (2012) "emotional oracle effect". These findings challenge assumptions about negative emotions in forecasting and suggest emotional engagement - rather than discrete emotions or TIF - drives accuracy.
Keywords
Discrete emotions
prediction accuracy
forecasting
trust-in-feelings
emotional engagement
appraisal-tendency framework
approach-avoidance motivation
dualprocess theory
affect-as-information
emotional oracle effect
decision-making
judgment
HSG Classification
not classified
Refereed
No
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
Thomas Li - Sad But Accurate?.pdf
Size
2.95 MB
Format
Adobe PDF
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