AI-POWERED HIGH-FREQUENCY AND REINFORCEMENT-LEARNING TRADING SYSTEMS: ADVANCING OR UNDERMINING MARKET EFFICIENCY AND FAIRNESS?
Abstract
The increasing integration of Artificial Intelligence (AI) into financial markets, particularly within high-frequency trading (HFT) and reinforcement learning (RL) systems, presents a complex subject for financial economics and regulatory oversight. These advanced algorithmic strategies execute trades at speeds beyond human capability, influencing market dynamics profoundly . While proponents assert that AI-driven trading enhances market efficiency by facilitating faster price discovery and increased liquidity, critics raise concerns regarding potential market destabilization, algorithmic bias, and fairness erosion for human participants . Understanding the dual capacity of AI in financial markets requires a detailed examination of its mechanisms, observable effects, and regulatory challenges.
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