主题：AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
主讲人：吉岩，香港科技大学商学院副教授
时间：12月27日（周三）上午10:00-11:30
地点：4-101教室
语言：英文
摘要：
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, and market liquidity. 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 profits by strategically under-reacting to information, even in the absence of explicit communication or coordination that might breach conventional antitrust regulations. Algorithmic collusion emerges from two distinct mechanisms. The first mechanism is collusion via price trigger strategies (“artificial intelligence”), while the second stems from learning biases (“artificial stupidity”) and homogenization. The former is evident only when there is limited price efficiency and information asymmetry. In contrast, the latter persists even under conditions of high price efficiency or severe information asymmetry. 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.
主讲人介绍：
Yan Ji is an Associate Professor of Finance at the Hong Kong University of Science and Technology. His research interests lie in the intersection of asset pricing, industrial organization, and macroeconomics. His work has appeared in academic journals such as Journal of Political Economy, Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Monetary Economics, and Management Science. He obtained his PhD in Economics from MIT.