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    Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey
    (Academic Journals, 2012) Timor, Mehpare; Dincer, Hasan; Emir, Senol
    Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that find a wide range of applications both in the field of engineering and social sciences. Recently, studies especially in the field of finance for the classification and estimation make it necessary to use these methods often in this area. In this study, different SVM and ANN models for the problem of stocks selection which provide maximum returns have been applied on different combinations of data sets which obtained from the balance sheets, stocks prices and the results of a comparative analysis has been presented. The findings show that SVM and ANN models including financial ratios give meaningful performance results for the stock selection.
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    A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines
    (Academic Research Centre of Canada, 2012) Emir, Senol; Dincer, Hasan; Timor, Mehpare
    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.