A Review Paper Artificial Intelligence and Data Mining Techniques for Stock Market Forecasting

Abstract

The stock market is a highly dynamic, non-linear, and complex system that poses significant challenges for accurate modeling and prediction. Traditional approaches such as fundamental and technical analysis have provided useful insights; however, they often fall short in capturing the intricate patterns of financial data, particularly in short- and medium-term forecasting. With the advancement of computational power and the emergence of artificial intelligence (AI) and data mining techniques, new opportunities have arisen for improving stock market prediction accuracy. Methods such as decision trees, artificial neural networks, clustering, association rule mining, and trend analysis have been widely applied to identify hidden patterns, extract knowledge from large datasets, and enhance investment decision-making. Recent studies have demonstrated that hybrid models integrating AI with data mining significantly outperform conventional methods, offering higher predictive power, better risk management, and more reliable strategies. This paper reviews the application of these techniques in stock market forecasting, highlighting their strengths, limitations, and potential to transform financial decision-making.