A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines

?enol Emir (), Hasan Din?er () and Mehpare Timor ()
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?enol Emir: Department of Computer Programming, Beykent University
Hasan Din?er: Department of Banking and Finance, Beykent University
Mehpare Timor: Department of Quantitative Methods, Istanbul University

Review of Economics & Finance, 2012, vol. 2, 106-122

Abstract: The basic aim of this article is to provide a model to explain stock performance utmost level. To reach this purpose, at the initial step, the model results composed of fundamental and technical analysis variables considered separately; in the second step, building the model composed of fundamental and technical analysis parameters which has best explaining ability was the focal point of this study. Artificial Neural Network (ANN) is an approach that has been widely used for financial classification problems for a long time. In addition, promising results of a novel machine learning method known as the Support Vector Machines (SVM) have been presented in several studies compared to the ANN. The stock performance results relying on fundamental analysis have shown more successful classification rates than the models based on technical analysis. Moreover, it was also experienced that the models constructed by using SVM method in the both type of analyses have shown more prominent results.

Keywords: Stock selection; Fundamental analysis; Technical analysis; Support vector machines; Artificial neural networks (search for similar items in EconPapers)
JEL-codes: C10 G10 G11 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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