Do emotions expressed online correlate with actual changes in decision-making? The case of stock day traders

Bin Liu, Ramesh Govindan, Brian Uzzi

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Emotions are increasingly inferred linguistically from online data with a goal of predicting off-line behavior. Yet, it is unknown whether emotions inferred linguistically from online communications correlate with actual changes in off-line activity. We analyzed all 886,000 trading decisions and 1,234,822 instant messages of 30 professional day traders over a continuous 2 year period. Linguistically inferring the traders' emotional states from instant messages, we find that emotions expressed in online communications reflect the same distributions of emotions found in controlled experiments done on traders. Further, we find that expressed online emotions predict the profitability of actual trading behavior. Relative to their baselines, traders who expressed little emotion or traders that expressed high levels of emotion made relatively unprofitable trades. Conversely, traders expressing moderate levels of emotional activation made relatively profitable trades.

Original languageEnglish (US)
Article numbere0144945
JournalPloS one
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2016

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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