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The Effect of Behavioral Factors on Stock Price Prediction using Generalized Regression and Backpropagation Neural Networks Models

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  • Payam Hanafizadeh

    (Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran)

  • Ahmad Hashemi

    (University of Science and Culture, Tehran, Iran)

Abstract

With regard to the importance of behavioral factors on stock price, which has been mentioned by researchers, this study includes four behavioral factors (overconfidence, representativeness, over reaction and under reaction) in addition to fundamental and technical factors as inputs for neural network models to evaluate the effectiveness of these behavioral factors on stock price prediction accuracy of 10 companies of DJIA index. Multi-layer perceptron (MlP) and generalized regression neural networks are used in this research as models to find the best model for each company based on unique characteristics of its own financial data. This study shows the mentioned behavioral factors are effective on accuracy of predictions of 8 out of 10 companies.

Suggested Citation

  • Payam Hanafizadeh & Ahmad Hashemi, 2014. "The Effect of Behavioral Factors on Stock Price Prediction using Generalized Regression and Backpropagation Neural Networks Models," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 5(4), pages 44-57, October.
    • Handle: RePEc:igg:jbir00:v:5:y:2014:i:4:p:44-57
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