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

This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neuralnetwork (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH, GARCH) and exponential generalized autorjective conditional hetero-synthesis (EGARCH) to extract new input variables.

Abstract

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).