"Human Swarming" Amplifies Accuracy and ROI when Forecasting Financial Markets

Many social species amplify their decision-making accuracy by deliberating in real-time closed-loop systems. Known as Swarm Intelligence (SI), this natural process has been studied extensively in schools of fish, flocks of birds, and swarms of bees. The present research looks at human groups and tests their ability to make financial forecasts by working together in systems modeled after natural swarms. Specifically, groups of financial traders were tasked with forecasting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 19 consecutive weeks. Results showed that individual forecasters, who averaged 56.6% accuracy when predicting weekly trends on their own, amplified their accuracy to 77.0% when predicting together as real-time swarms. This reflects a 36% increase in forecasting accuracy and shows high statistical significance (p<; 0.001). Further, if investments had been made according to these swarm-based forecasts, the group would have netted a 13.3% return on investment (ROI) over the 19 weeks, compared to the individual's 0.7% ROI. 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 and ROI of financial forecasts.

I. Introduction

Extensive prior research has shown that groups of human forecasters can outperform individual forecasters by aggregating estimations across groups using simple statistical methods [ 1 – 3 ] . Often referred to as the Wisdom of Crowds (WoC) or Collective Intelligence (CI), this phenomenon was first observed over a century ago and has been applied to many fields, from predicting financial markets to forecasting geopolitical events. The most common methods involve polling a population of individuals for self-reported estimations and then aggregating the collected input statistically as a simple or weighted mean [4] .