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A Review on Applied Data Mining Techniques to Stock Market Prediction

A Review on Applied Data Mining Techniques to Stock Market Prediction
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Author(s): Neslihan Fidan (Istanbul University, Turkey)and Beyza Ahlatcioglu Ozkok (Yildiz Technical University, Turkey)
Copyright: 2013
Pages: 19
Source title: Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems
Source Author(s)/Editor(s): Petraq Papajorgji (Universiteti Europian i Tiranes, Albania), Alaine Margarete Guimarães (State University of Ponta Grossa, Brazil)and Mario R. Guarracino (Italian National Research Council, Italy)
DOI: 10.4018/978-1-4666-3946-1.ch009

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Abstract

A portfolio manager considers forecasting the asset prices and measurement of the market risk of an underlying asset. Financial institutions produce datasets to handle their problems by using data mining tools. Recently new technologies have been developed for tracking, collecting, and processing financial data. From a data analysis point of view, this chapter reviews the published articles based upon predictive data mining applications to stock market index. It is observed that hybrid models that combine data mining techniques or integrate an algorithm to a method work efficiently. Finally, the chapter provides likely directions of future researches.

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