Business Commons^™

Machine Learning Stock Market Prediction Studies: Review And Research Directions, Troy J. Strader, John J. Rozycki, Thomas H. Root, Yu-Hsiang John Huang

Journal of International Technology and Information Management

Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine …