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.
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Fiol-Roig, G., Miró-Julià, M.: Applying Data Mining Techniques to Stock Market Analysis. Accepted for publication in the 8th International Conference on Practical Applications of Agents and Multi-Agent Systems. Trends and Strategies on Agents and Multiagent Systems (2010)
Fayyed, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence, AI Magazine Fall 96, 37–54 (1996)
Fiol-Roig, G.: UIB-IK: A Computer System for Decision Trees Induction. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 601–611. Springer, Heidelberg (1999)
Weinstein, S.: Stan’s Weinstein’s Secrets For Profiting in Bull and Bear Markets. McGraw-Hill, New York (1988)
Miró-Julià, M.: Knowledge discovery in databases using multivalued array algebra. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 17–24. Springer, Heidelberg (2009)
Fiol-Roig, G.: Learning from Incompletely Specified Object Attribute Tables with Continuous Attributes. Frontiers in Artificial Intelligence and Applications 113, 145–152 (2004)
The R project, http://www.r-project.org/
http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
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Miró-Julià, M., Fiol-Roig, G., Isern-Deyà, A.P. (2010). Decision Trees in Stock Market Analysis: Construction and Validation. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_19
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DOI: https://doi.org/10.1007/978-3-642-13022-9_19
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