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Stock Market Analysis using Data Mining Techniques: a Practical Application

Gabriel Fiol-Roig, Margaret Miro-Julia

Abstract


The stock market can be viewed as a particular data mining and artificial intelligence problem. The movement in the stock exchange depends on capital gains and losses and most people consider the stock market erratic and unpredictable. However, patterns that allow the prediction of some movements can be found. Stock market analysis deals with the study of these patterns. It uses different techniques and strategies, mostly automatic that trigger buying and selling orders depending on different decision making algorithms. It can be considered as an intelligent treatment of past and present financial data in order to predict the stock market future behavior. Therefore it can be viewed as an artificial intelligence problem in the data mining field. This paper aims to study, construct and evaluate these
investment strategies in order to predict future stock exchanges. 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.

Keywords


Data Mining, Artificial Intelligence, Stock Market, Investment Strategies, Decision Trees.

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