AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
84 Pages Posted: 23 May 2023 Last revised: 4 Mar 2024
Date Written: January 27, 2024
Abstract
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 speculators with asymmetric information to explore the implications of AI-powered trading strategies on speculators' market power, information rents, price informativeness, market liquidity, and mispricing. Our results demonstrate that informed AI speculators, even though they are ``unaware'' of collusion, can autonomously learn to employ collusive trading strategies. These collusive strategies allow them to achieve supra-competitive trading profits by strategically under-reacting to information, even without any form of agreement or communication, let alone interactions that might violate traditional antitrust regulations. Algorithmic collusion emerges from two distinct mechanisms. The first mechanism is through the adoption of price-trigger strategies (``artificial intelligence''), while the second stems from homogenized learning biases (``artificial stupidity''). The former is evident only when there is limited price efficiency and noise trading risk. In contrast, the latter persists even under conditions of high price efficiency or large noise trading risk. As a result, in a market with prevalent AI-powered trading, both price informativeness and market liquidity can suffer, reflecting the influence of both artificial intelligence and stupidity.
Keywords: Reinforcement learning, AI collusion, Homogenization, Self-confirming equilibrium, Asymmetric information, Price informativeness, Market liquidity.
JEL Classification: D43, G10, G14, L13.
Suggested Citation: Suggested Citation