EquityBot is an automated stock-trading algorithm that uses emotions on Twitter as the basis for investments in a simulated bank account. This art project poses the question: can an artist create a stock-trading algorithm that will outperform professional managed accounts?
During stock market hours, EquityBot scrapes Twitter to determine the frequency of eight basic human emotions: anger, fear, joy, disgust, anticipation, trust, surprise and sadness. The software code captures fluctuations in the number of tweets containing these emotions. It then correlates them to changes in stock prices.
The premise for EquityBot’s trading strategy is that popular emotions on Twitter will influence specific stock prices.
When an emotion is trending upwards EquityBot will select a stock that follows a similar trajectory. It deems this to be a “correlated investment” and will buy this stock. Conversely, when an emotion is spiraling downwards on Twitter, EquityBot will look for a correlation, And if it owns that stock in its portfolio, it will sell it.
Over time, the project will track the performance of all eight emotions.
And, over the course of many years, the project will track its performance in relation to popular managed funds.
“Disruptions: Party Crashing through the Front Door – An ISEA 2015 Late Post-Mortem”, by Patrick Lichty, Furtherfield, Oct 10 2015
Mood Swings, Q21, Vienna, Austria
MemFest 2015, Bilbao, Spain
ISEA 2015, Vancouver
MoneyLab 2, Economies of Dissent, Institute of Network Cultures, Amsterdam
Bay Area Digitalists, San Francisco
Interventions, Fuse Factory, Columbus, Ohio
Impakt Festival 2014, Utrecht, The Netherlands