AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
43 Pages Posted: 23 May 2023 Last revised: 31 May 2023
Date Written: May 31, 2023
The integration of algorithmic trading and reinforcement learning, known as AI-powered trading, has significantly impacted capital markets. This study utilizes a model of imperfect competition among informed traders with asymmetric information to explore the implications of AI-powered trading strategies on informed traders' market power and price efficiency. Our results demonstrate that informed AI traders can collude and generate substantial profits by strategically manipulating low order flows, even without explicit coordination that violates antitrust regulations. This algorithmic collusion arises from two mechanisms: collusion through biased learning and collusion through punishment threat. Collusion through punishment threat creates a paradoxical situation in terms of price informativeness. Consequently, in a market with prevalent AI-powered trading and collusion through punishment threat, perfect price efficiency remains unattainable.
Keywords: Paradox of price informativeness, Reinforcement learning, Market Power, Collusion, Asymmetric information.
JEL Classification: D43, G10, G14, L13.
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