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Earnings Per Share Forecast Using Extracted Rules from Trained Neural Network by Genetic Algorithm

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  • Hossein Etemadi
  • Ahmad Ahmadpour
  • Seyed Moshashaei

Abstract

Earnings per share (EPS) is one of the main financial ratio that is considering by managers, investors and financial analysts. It is usually using in investment decisions, profitability evaluation, profit risk, and stock price estimation. Therefore, EPS forecasting is a valuable and attractive task for managers and investors. This paper examines EPS forecasting using multi-layer perceptron (MLP) neural network and rule extraction from neural network by genetic algorithm technique and determined an optimal model between MLP and RE technique by evaluating their forecasting accuracy. For this purpose, we use 990 listed firms in Tehran Stock Exchange in the period of 2000–2010. The results show that the RE technique is significantly more accurate than the MLP model. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Hossein Etemadi & Ahmad Ahmadpour & Seyed Moshashaei, 2015. "Earnings Per Share Forecast Using Extracted Rules from Trained Neural Network by Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 55-63, June.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:1:p:55-63
    DOI: 10.1007/s10614-014-9455-6
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    References listed on IDEAS

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    Cited by:

    1. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    2. Gholamhossein Mahdavi & Abbas Ali Daryaei, 2016. "Attitude toward auditing, marketing and corporate governance (An examination based in Parsons’ social action theory)," International Journal of Corporate Social Responsibility, Springer, vol. 1(1), pages 1-16, December.

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