Stock trading AIs are naturaly prone to collusion

See all articles by Winston Wei Dou

Winston Wei Dou

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Itay Goldstein

University of Pennsylvania - The Wharton School - Finance Department ; National Bureau of Economic Research (NBER)

Yan Ji

Hong Kong University of Science & Technology (HKUST) - Department of Finance

Abstract

The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets. Alongside the benefits, it raises concerns for collusion. This study first develops a model to explore the possibility of collusion among informed speculators in a theoretical environment. We then conduct simulation experiments, replacing the speculators in the model with informed AI speculators who trade based on reinforcement-learning algorithms. We show that they autonomously sustain collusive supra-competitive profits without agreement, communication, or intent. Such collusion undermines competition and market efficiency. We demonstrate that two separate mechanisms are underlying this collusion and characterize when each one arises.