AI-Powered Trading, Algorithmic Collusion, and Price Efficiency

43 Pages Posted: 23 May 2023 Last revised: 31 May 2023

See all articles by Winston Wei Dou

Winston Wei Dou

The Wharton School, University of Pennsylvania; 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: May 31, 2023

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 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.

Suggested Citation

Dou, Winston Wei and Goldstein, Itay and Ji, Yan, AI-Powered Trading, Algorithmic Collusion, and Price Efficiency (May 31, 2023). Available at SSRN: https://ssrn.com/abstract=4452704 or http://dx.doi.org/10.2139/ssrn.4452704

Winston Wei Dou (Contact Author)

The Wharton School, University of Pennsylvania ( email )

2318 Steinberg Hall - Dietrich Hall
3620 Locust Walk
Philadelphia, PA 19104
United States

HOME PAGE: http://finance-faculty.wharton.upenn.edu/wdou/

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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