عنوان مقاله [English]
Forecasting financial markets is an important issue in finance area and research studies. Importance of forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include wavelet transform, ARMA-EGARCH and NN for day-ahead forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets.
Babu, C. N., & Reddy, B. E. (2014). A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing, 23, 27–38.
* Dai, W., & Lu, C.-J. (2008). Financial Time Series Forecasting Using a Compound Model Based on Wavelet Frame and Support Vector Regression. 2008 Fourth International Conference on Natural Computation, 328–332.
* Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34(3), 828–841.
* Joo, T. W., & Kim, S. B. (2015). Time series forecasting based on wavelet filtering. Expert Systems with Applications, 42(8), 3868–3874.
* Kaastra, I., & Boyd, M. (1999). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236.
* Khandelwal, I., Adhikari, R., & Verma, G. (2015). Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition. Procedia Computer Science, 48(Iccc), 173–179.
* October, M. (2008). A Wavelet Tour of Signal Processing.
* Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118–132.
* Tan, Z., Zhang, J., Wang, J., & Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy, 87(11), 3606–3610.
* Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501–5506.
* Tsay, R. S. (2005). Analysis of Financial Time Series.
* Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346–14355.
* Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.