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


  • ?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)


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.

Suggested Citation

  • ?enol Emir & Hasan Din?er & Mehpare Timor, 2012. "A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 106-122, August.
  • Handle: RePEc:bap:journl:120309

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    References listed on IDEAS

    1. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    2. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    3. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    4. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
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    More about this item


    Stock selection; Fundamental analysis; Technical analysis; Support vector machines; Artificial neural networks;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General


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