AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

Winston Dou, Itay Goldstein and Yan Ji

No 34054, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D43 G10 G14 L13 (search for similar items in EconPapers)
Date: 2025-07
Note: AP CF IO LE PR
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