AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

Joint with Itay Goldstein, Yan Ji

April 2025

The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets by reshaping how trading works. This study constructs a theoretical laboratory where financial markets function as information aggregation mechanisms, compelling investors to trade cautiously on private signals to preserve information rents. We find that informed AI speculators can autonomously sustain collusive supra-competitive profits without agreement, communication, or intent. AI collusion undermines competition and market efficiency, emerging robustly through two algorithmic mechanisms: (i) price-trigger strategies when information-insensitive investors are strongly prevalent and noise trading risk is low, and (ii) over-pruning bias in learning under other conditions.