Decision Trees in Stock Market Analysis: Construction and Validation

Abstract

Data Mining techniques and Artificial Intelligence strategies can be used to solve problems in the stock market field. Most people consider the stock market erratic and unpredictable since the movement in the stock exchange depends on capital gains and losses. Nevertheless, patterns that allow the prediction of some movements can be found and studied. In this sense, stock market analysis uses different automatic techniques and strategies that trigger buying and selling orders depending on different decision making algorithms. In this paper different investment strategies that predict future stock exchanges are studied and evaluated. Firstly, data mining approaches are used to evaluate past stock prices and acquire useful knowledge through the calculation of financial indicators. Transformed data are then classified using decision trees obtained through the application of Artificial Intelligence strategies. Finally, the different decision trees are analyzed and evaluated, showing accuracy rates and emphasizing total profit associated to capital gains.