HKUST Business School Magazine

AI collusion in financial markets A crucial feature of financial markets is information asymmetry. Investors can profit from trading an asset if they possess more knowledge about its value than other investors. Trading becomes highly strategic when multiple investors are informed about the asset’s value. If these informed investors trade the asset intensively, their private information will quickly be reflected in the market, leading to significant price changes that can render their trading unprofitable. To maximize profits, informed investors may coordinate their trading to allow their private information to be gradually absorbed by the market without causing immediate, substantial price fluctuations. This coordinated trading behavior among informed investors is known as collusion. Such collusion is typically challenging to achieve. Explicit collusion is illegal, and implicit collusion is difficult to maintain, as informed investors always have the incentive to deviate from the agreement to enhance their short-term profits. Moreover, effective monitoring of each other's behavior is hindered by significant information asymmetry in financial markets. While collusion in financial markets might be challenging for humans, my recent working paper with Wharton finance professors Winston Wei DOU and Itay GOLDSTEIN indicates that such collusion may emerge when trading orders are executed by AI-powered algorithms. 4 We developed a trading laboratory where AI algorithms trade assets using basic reinforcement learning techniques. Over time, these algorithms adapt their behavior through self-learning and learn to coordinate independently, even without direct instructions or communication. This AI collusion suggests that market liquidity and price informativeness may be negatively impacted. Mechanisms behind AI collusion in financial markets AI algorithms differ from human traders in that they do not simply mimic human behavior. Traditional theories and experimental studies on human behavior fall short in explaining the actions of AI traders and the market equilibria they may form. AI operates with a distinct form of intelligence, where decision-making is guided by pattern recognition rather than emotions or logical reasoning, making it unaffected by higher-order beliefs. Therefore, understanding the dynamics of financial markets with AI-powered trading algorithms requires an understanding of algorithmic behavior that resembles the “psychology” of machines. 5 This approach parallels how decision theory and psychology have provided valuable insights into modelling human behavior within an economic context. Economic theories of learning in games provide valuable insights into mechanisms that may drive AI collusion in financial markets. As highlighted in our working paper, the emergence of AI collusion can result from either sophisticated artificial intelligence or from what can be termed "artificial stupidity," depending on prevailing market conditions. In less noisy markets, algorithms can develop price-trigger strategies to maintain collusion, demonstrating artificial intelligence. Essentially, these algorithms learn to trade the asset as if adhering to an implicit agreement. However, if market prices become abnormal, the algorithms interpret this as a breach of the agreement, prompting all algorithms to trade aggressively in a punitive manner. These strategies align with theoretical mechanisms in dynamic games with imperfect monitoring, where a return to non- collusive competition occurs when the market price significantly deviates from its expected collusive level. 6 Biz@HKUST 31