Artificial Swarm Intelligence amplifies accuracy when predicting financial markets

Abstract

Across the natural world, many species have evolved methods for amplifying their decision-making accuracy by thinking together in real-time closed-loop systems. Known as Swarm Intelligence (SI) in the field of biology, the process has been deeply studied in schools of fish, flocks of bird, and swarms of bees. The present research looks at human groups and tests their ability to make financial predictions by forming online systems modeled after natural swarms. Specifically, groups of financial traders were tasked with predicting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 14 consecutive weeks. Results showed that individual participants, who averaged 61% accuracy when predicting weekly trends on their own, amplified their accuracy to 77% when predicting together as real-time swarms. These results reflect a 26% increase in financial prediction accuracy and show high statistical significance (p=0.001). This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy of financial forecasts.