ALGORITHMIC TRADING AND MARKET MANIPULATION: A LEGAL PERSPECTIVE ON INSIDER TRADING REGULATIONS

Algorithmic trading has revolutionized financial markets by increasing efficiency, reducing transaction costs, and enhancing liquidity.However, its rapid expansion has introduced significant concerns regarding market manipulation, insider trading, and regulatory oversight.The rise of High-Frequency Trading (HFT), which enables thousands of transactions per second, has made it increasingly difficult for regulators to detect and prevent fraudulent trading practices.In jurisdictions like India, the SEBI (Prohibition of Insider Trading) Regulations, 2015 govern securities transactions and aim to prevent the misuse of Unpublished Price-Sensitive Information (UPSI) by insiders.However, these regulations were primarily designed for traditional trading environments and may not adequately address the complexities of AI-driven trading strategies.Algorithmic trading raises unique legal challenges, such as lack of transparency, difficulty in establishing intent, and regulatory enforcement gaps.This paper critically examines whether India's existing legal framework is sufficient to regulate algorithmic trading and prevent insider trading in automated transactions.The study further explores global regulatory approaches in jurisdictions such as the United States (SEC), United Kingdom (FCA), and the European Union (MiFID II Regulations), which have implemented sophisticated AI-based market surveillance mechanisms and real-time monitoring to curb manipulation.Comparative analysis highlights regulatory best practices that could be adapted to India's financial markets.In light of these challenges, this paper proposes legal and policy reforms, including mandatory algorithmic audits, enhanced AI-driven surveillance mechanisms, cross-