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 language | English (US) |
|---|---|
| Article number | e0144945 |
| Journal | PloS one |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2016 |
Funding
We thank Matthias Leiss, Christain Schultz, Chungwei Hang, Zhe Zhang, and Munindar Singh for their help with the research. Research was sponsored by the Northwestern Institute on Complex System (NICO), the Army Research Laboratory under cooperative agreement W911NF-09-2-0053 to RG and BU and ARO No. 65674-NS-CF to BU. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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. The specific roles of these authors are articulated in the 'author contributions' section.
ASJC Scopus subject areas
- General