title: Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders.
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.
authors.affiliation: Google Inc, 1600 Amphitheater Parkway, Mountain View, California, United States of America.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: University Southern California, Department of Computer Science, Los Angeles, California, United States of America.
authors.forename: Ramesh
authors.initials: R
authors.lastname: Govindan
authors.affiliation: Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, Illinois, United States of America.
authors.forename: Brian
authors.initials: B
authors.lastname: Uzzi
mesh_terms: D003132:Commerce; D003657:Decision Making; D004644:Emotions; D006801:Humans; D020407:Internet; D008962:Models, Theoretical