Machine Learning for Real-Time Financial Market Monitoring

Abstract

Machine learning (ML) has emerged as a powerful tool in the financial industry, particularly for real-time market monitoring. By leveraging large datasets, ML algorithms can identify patterns and predict market behavior with greater accuracy than traditional statistical methods. This paper explores various ML techniques used in financial market monitoring, focusing on real-time prediction, anomaly detection, and risk management. We also discuss the challenges and future prospects of implementing ML in real-time trading systems.