AI Arbitrage and the Law

Closing Regulatory Gaps in Algorithmic Trading

Abstract

Artificial Intelligence (AI) has transformed financial markets by enabling high-frequency trading (HFT) and AI-driven arbitrage, allowing superhuman speed, data processing, and decision-making. While traditional arbitrage exploits price inefficiencies across markets, AI arbitrage leverages advanced machine learning, predictive analytics, and latency advantages to capitalize on fleeting opportunities. However, this technological evolution has outpaced existing legal frameworks, raising significant regulatory, ethical, and market integrity concerns. This article examines the legal challenges of AI arbitrage, particularly the disparity in how regulators treat AI-driven trading versus human traders, including issues related to market manipulation, insider trading, and price-fixing. AI systems can engage in trading behaviors that, if performed by humans, might violate existing securities, commodities, or antitrust laws. These practices challenge foundational legal principles governing fair markets. Despite existing laws addressing market manipulation, insider trading, and price-fixing, AI presents enforcement difficulties due to its autonomous nature, speed, and complexity. This article explores regulatory gaps, including problems proving AI intent, the challenge of monitoring AI trading strategies, and the lack of transparency in algorithmic decision-making. It further examines landmark cases such as United States v. Coscia and the DOJ’s RealPage litigation to highlight how AI is already testing the limits of legal enforcement. To address these issues, the article calls for rules that hold AI trading to the same standards as human trading, like rules against manipulation and insider trading.