Algorithmic Collusion and Market Efficiency: Economic and Ethical Implications of AI in Finance

Abstract

The rapid integration of Artificial Intelligence (AI) into financial markets, particularly in high-frequency trading and speculative activities, presents a nascent but significant risk of autonomous algorithmic collusion. This raises fundamental questions about market efficiency, fairness, and the adequacy of existing regulatory frameworks in preventing anti-competitive behavior. This research aims to explore how reinforcement-learning algorithms in securities trading autonomously sustain supra-competitive profits, and what the economic consequences are for market efficiency and liquidity. It also investigates the ethical and regulatory implications of such algorithmic collusion in the absence of explicit human agreement or communication. Computational modeling and simulation studies, combined with empirical market data analysis and legal/regulatory analysis, would be appropriate. This research would contribute to financial economics by modeling a new form of market inefficiency driven by AI, and inform policy debates on AI regulation in finance, particularly concerning antitrust, market surveillance, and the development of ethical AI guidelines for automated trading systems.