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AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

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  • Winston Wei Dou
  • Itay Goldstein
  • Yan Ji

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.

Suggested Citation

  • Winston Wei Dou & Itay Goldstein & Yan Ji, 2025. "AI-Powered Trading, Algorithmic Collusion, and Price Efficiency," NBER Working Papers 34054, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:34054
    Note: AP CF IO LE PR
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    More about this item

    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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