AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

84 Pages Posted: 23 May 2023 Last revised: 1 Mar 2024

See all articles by Winston Wei Dou

Winston Wei Dou

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Itay Goldstein

University of Pennsylvania - The Wharton School - Finance Department ; National Bureau of Economic Research (NBER)

Yan Ji

Hong Kong University of Science & Technology (HKUST)

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

Dou, Winston Wei and Goldstein, Itay and Ji, Yan, AI-Powered Trading, Algorithmic Collusion, and Price Efficiency (January 27, 2024). Available at SSRN: https://ssrn.com/abstract=4452704 or http://dx.doi.org/10.2139/ssrn.4452704

Winston Wei Dou (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

HOME PAGE: http://www.nber.org/people/winston_wei_dou?page=1&perPage=50

Itay Goldstein

University of Pennsylvania - The Wharton School - Finance Department ( email )

The Wharton School
3620 Locust Walk
Philadelphia, PA 19104
United States
215-746-0499 (Phone)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Yan Ji

Hong Kong University of Science & Technology (HKUST) ( email )

Clearwater Bay
Kowloon, 999999
Hong Kong

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