Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

  • Erkam Güreşen
  • Gülgün Kayakutlu
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 288)


Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).


Artificial Neural Network Artificial Neural Network Model Hybrid Model Recurrent Neural Network Financial Time Series 


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Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Erkam Güreşen
    • 1
  • Gülgün Kayakutlu
    • 1
  1. 1.Department of Industrial Engineering, MaçkaIstanbul Technical UniversityIstanbulTurkey

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