Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders

PLoS One. 2016 Jan 14;11(1):e0144945. doi: 10.1371/journal.pone.0144945. eCollection 2016.

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Commerce
  • Decision Making*
  • Emotions*
  • Humans
  • Internet*
  • Models, Theoretical

Grants and funding

This work received support from the Army Research Laboratory (under cooperative agreement W911NF-09-2-0053); DARPA BAA-11-64; Northwestern Institute on Complex Systems (NICO). The funder (Google) provided support in the form of salaries for author BL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.