AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

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.

Keywords: Reinforcement learning, AI collusion, Competition and market efficiency, Experience-based and self-confirming equilibrium, Information asymmetry and price informativeness, Market liquidity

JEL Classification: D43, G10, G14, L13.