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

<div><p>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.</p></div

Instant Messaging and Trading are Closely Associated in Time.

<p>The figure shows the probability of observing traders trading (red area) and instant messaging (blue area) over the hours of the day averaged over time and traders in our sample and indicates that changes in the frequency of instant messaging are associated with changes in the frequency of trading. Trading begins at 9am and ends at 4pm. Instant messaging done at any time day or night is captured by the firm’s recording system.</p

Instant Message Emotional Activation Coding.

<p>Left panel contains an actual Instant Message (IM) conversation as recorded in our data for a trader. Words highlighted in red are words in the ANEW dictionary that reflect a level of emotional activation over our threshold. Right panel indicates how an emotional activation level is computed for a selection of words that appear in the instant message conversation and the ANEW dictionary.</p

Linguistically Inferred Emotional Activation Levels.

<p>Each boxplot represents a trader’s distribution of daily instant messages expressing emotional activation as inferred from the linguistic content of their sent instant messages. Traders’ anonymous IDs are identified by the numbers on the x axis. Comparably distributions of emotional activation have been identified in traders from physiological measurements [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144945#pone.0144945.ref016" target="_blank">16</a>].</p