Forecasting of Volatile Assets using Artificial Swarm Intelligence

Abstract

Swarm Intelligence (SI) is a natural process that has been shown to amplify decision-making accuracy in many social species, from schools of fish to swarms of bees. Artificial Swarm Intelligence (ASI) is a technology that enables similar benefits in networked human groups. The present research tests whether ASI enables human groups to reach more accurate financial forecasts. Specifically, a group of MBA candidates at Cambridge University was tasked with forecasting the three-day price change of 12 highly volatile assets, a majority of which were cult (or meme) stocks. Over a period of 9 weeks, human forecasters who averaged +0.96% ROI as individuals amplified their ROI to +2.3% when predicting together in artificial swarms (p=0.128). Further, a $\$5,000$ bankroll was managed by investing in the top three buy recommendations produced each week by ASI, which yielded a 2.0% ROI over the course of the 9-week study. This suggests that swarm-based forecasting has the potential to boost the performance of financial traders in real-world settings.