Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Financial markets generate huge volume of data every second in the form of price movements, order books, news feeds, and economic indicators. Traditional statistical models often fail to capture nonlinear patterns and fast changing dynamics of such markets. Machine Learning (ML) techniques are increasingly used in finance and especially in algorithmic trading to predict market behavior, manage risks, and optimize trading strategies. This paper reviews the role of ML in finance with special focus on algorithmic trading systems. It discusses supervised, unsupervised and reinforcement learning approaches used for prediction of asset prices, portfolio optimization, high frequency trading, sentiment analysis from financial news, and risk management. Various ML algorithms like regression models, support vector machines, neural networks, deep learning architectures and reinforcement learning frameworks are explained with their applications in trading. Advantages, limitations and practical challenges of ML in financial markets are also presented. Tables and figures summarize different methods and their use cases. Finally, the paper concludes with future directions of ML-driven finance systems.

Keywords: Machine Learning, Algorithmic Trading, Financial Prediction, Deep Learning, Reinforcement Learning, Quantitative Finance, Sentiment Analysis, Portfolio Optimization.