MACHINE LEARNING IN FINANCIAL TRADING: OPPORTUNITIES AND RISKS

Abstract

Machine learning (ML) has emerged as a transformative force in financial trading, offering innovative tools for data-driven decision-making, algorithmic strategy development, and real-time market analysis. By leveraging vast datasets and complex patterns, ML enables traders to enhance prediction accuracy, automate trading processes, and optimize portfolio management. However, the adoption of ML also introduces significant challenges and risks, including model overfitting, data quality issues, lack of transparency, and potential systemic vulnerabilities. This paper explores the key opportunities presented by machine learning in the financial trading landscape while critically examining the associated risks. It emphasizes the need for robust regulatory frameworks, ethical considerations, and continuous model validation to ensure responsible and effective use of ML technologies in financial markets.