Forecasting Stock Price using Hybrid Model based on Wavelet Transform in Tehran and New York Stock Market

Document Type : Original Article

Authors

1 Ms. in Financial Engineering, Department of Industrial Engineering and Management Systems, Amirkabir University, Tehran, Iran (Corresponding Author)

2 Associate Prof in Industrial Engineering and Management System, Department of Industrial Engineering and Management Systems, Amirkabir University, Tehran, Iran

3 Ms. Student in Financial Engineering, Department of Industrial Engineering and Management Systems, Amirkabir University, Tehran, Iran

Abstract

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-period and multi-period forecasting of stock market price in different markets. At first, we decompose time series into detail and approximate series with wavelet transform, and then we used ARMA-GARCH and ANN models to forecast detail and approximate series, respectively. In addition to the approximate series, we use some technical indexes in this model to improve our ANN model. To evaluate the proposed model in forecasting stock price, we compare our model with ANN, ARIMA-GARCH and ARIMA-ANN models on Tehran and New York Stock Exchange (NYSE) historical prices. The results of study show that the proposed model has better performance in single-period forecasting on Tehran and New York market rather than other models.
 

Keywords

References

1)     Adhikari, R., & Agrawal, R. K. (2014). A combination of artificial neural network and random walk models for financial time series forecasting. Neural Computing and Applications, 24(6), 1441–1449.
2)     Al Wadi, S., Ismail, M. T., Alkhahazaleh, M. H., & Karim, S. A. A. (2011). Selecting Wavelet Transforms Model in Forecasting Financial Time Series Data Based on ARIMA Model. Applied Mathematical Sciences, 5(7), 315–326.
3)     Baba, N., & Kozaki, M. (1992). An intelligent forecasting system of stock price using neural networks. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks (Vol. 1, pp. 371–377 vol.1).
4)     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.
5)     Bowden, N., & Payne, J. E. (2008). Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models. Energy Economics, 30(6), 3186–3197.
6)     Chang, P. C., & Fan, C. Y. (2008). A hybrid system integrating a wavelet and TSK fuzzy rules for stock price forecasting. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 38(6), 802–815.
7)     Chen, K. Y., & Wang, C. H. (2007). A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Systems with Applications, 32(1), 254–264.
8)     Chenoweth, T., & Obradović, Z. (1996). A multi-component nonlinear prediction system for the S&P 500 index. Neurocomputing, 10(3), 275–290.
9)     Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2002). ARIMA Models to Predict Next-Day Electricity Prices. IEEE Power Engineering Review, 22(9), 57.
10)  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.
11)  Doulai, P., & Cahill, W. (2001). SHORT-TERM PRICE FORECASTING IN ELECTRIC ENERGY MARKET, 749–754.
12)  Fernández-Rodrı́guez, F., González-Martel, C., & Sosvilla-Rivero, S. (2000). On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market. Economics Letters, 69(1), 89–94.
13)  Garcia, R. C., Contreras, J., Akkeren, M. van, & Garcia, J. B. C. (2005). A GARCH forecasting model to predict day-ahead electricity prices. IEEE Transactions on Power Systems, 20(2), 867–874.
14)  Ghiassi, M., Saidane, H., & Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21(2), 341–362.
15)  Hyup Roh, T. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33(4), 916–922.
16)  Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling. Energy Economics, 34(3), 828–841.
17)  Joo, T. W., & Kim, S. B. (2015). Time series forecasting based on wavelet filtering. Expert Systems with Applications, 42(8), 3868–3874.
18)  Kaastra, I., & Boyd, M. (1999). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236.
19)  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.
20)  Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing Journal, 11(2), 2664–2675.
21)  Lendasse, A., Bodt, E. de, Wertz, V., & Verleysen, M. (2000). Non-linear financial time series forecasting - Application to the Bel 20 stock market index. E.J.E.S.S., 14(1), 81–91. Retrieved from https://doi.org/10.1051/ejess:2000110
22)  Pai, P.-F., & Lin, C.-S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33, 497–505.
23)  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.
24)  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.
25)  Yu, L., Wang, S., & Lai, K. K. (2005). A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers and Operations Research, 32(10), 2523–2541.
26)  Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.
International Journal of Finance & Managerial Accounting
Volume 3, Issue 11
October 2018
Pages 43-57

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