HKUST Business School Magazine

References 1. Terrence Hendershott, Charles M. Jones, and Albert J. Menkveld, 2011, “Does Algorithmic Trading Improve Liquidity?” Journal of Finance, 66(1), pages 1-33 2. Nasdaq gets SEC nod for first exchange AI-driven order type, https://www. reuters.com/technology/nasdaq-gets-sec-nod-first-exchange-ai-driven-order- type-2023-09-08/ 3. Jennie Bai, Philippon, Thomas, and Alexi Savov, 2016, “Have financial markets become more informative?” Journal of Financial Economics, 122(3), pages 625- 65 4. Itay Goldstein, Winston Dou, and Yan Ji, 2024, “AI-Powered Trading, Algorithmic Collusion, and Price Efficiency,” Working paper 5. Itay Goldstein, Chester S Spatt, and Mao Ye, 2021, “Big Data in Finance,” Review of Financial Studies, 34(7), pages 3213–3225 6. Edward Green and Porter, Robert, 1984, “Noncooperative Collusion under Imperfect Price Information,” Econometrica, 52(1), pages 87-100 7. Drew Fudenberg and David Levine, 1993, “Self-Confirming Equilibrium,” Econometrica, 61(3): 523–45. Chaim Fershtman and Ariel Pakes, 2012, “Dynamic Games with Asymmetric Information: A Framework for Empirical Work,” Quarterly Journal of Economics, 127(4), pages 1611–1661 8. Amazon Used Secret ‘Project Nessie’ Algorithm to Raise Prices, https://www. wsj.com/business/retail/amazon-used-secret-project-nessie-algorithm-to- raise-prices-6c593706 9. Stephanie Assad, Robert Clark, Daniel Ershov, and Lei Xu, “Algorithmic pricing and competition: Empirical evidence from the German retail gasoline market,” Journal of Political Economy, 132, 723-771 In contrast, in highly noisy markets, algorithms struggle to accurately grasp the underlying environment. Their learning can be biased due to the inherent statistical limitations of basic reinforcement learning algorithms in a multi- agent dynamic system. Surprisingly, these biases, stemming from artificial stupidity, can lead algorithms to trade in a coordinated manner that ultimately increases profits for all participants. This steady-state equilibrium formed by AI algorithms resembles the experience-based equilibrium or self- confirming equilibrium found in economic theory. 7 An intriguing aspect of AI collusion is that it does not require identical algorithms. Different algorithms can still learn to collude, albeit to varying degrees. However, algorithmic homogenization plays a crucial role in facilitating AI collusion, which can occur when algorithms are developed from similar foundational models, effectively creating a hub-and-spoke conspiracy. Challenges and implications Beyond financial markets, there are growing concerns and evidence of AI-driven market manipulation in retail markets globally. The U.S. Federal Trade Commission recently filed a lawsuit against Amazon, alleging that the company employed a secret algorithm to manipulate prices. 8 Additionally, recent studies indicate that algorithmic pricing used by retail gasoline stations in Germany leads to anti-competitive outcomes. 9 While there is currently no evidence of AI collusion negatively impacting financial markets, the threat remains significant as leading digital trading platforms increasingly endorse reinforcement learning-based AI trading bots. The potential for AI collusion in financial markets has raised concerns for the U.S. SEC and regulators worldwide. SEC Chair Gary GENSLER particularly cautioned against “the possibility of AI destabilizing the global financial market if big tech-based trading companies monopolize AI development and applications within the financial sector.” Insight Biz@HKUST 32