An Improved Hybrid Model with Automated Lag Selection to Forecast Stock Market

Document Type : Research Paper


MSc., Department of Financial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.


Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic Algorithm II (NSGA II) to, more accurately, predict the trend of movement and volatility of Tehran Stock Exchange Price Index, and to compare its ability with the ARIMA model based on RMSE, MAPE, and TIC error assessment criterions.
Methods: For this purpose, the data of Tehran Stock Exchange Dividend and Price Index (TEDPIX) was used during the period of October 2008 to September 2013. The hybrid model NSGA II - GMDH utilizes the GMDH network as a model resistant to non-stationary and noisy data for prediction and uses the NSGA II multi-objective optimization algorithm to minimize predictive error and select the optimal input variables.
Results: The results of the proposed hybrid model in this study indicated a lower error and more prediction accuracy compared to ARIMA model for out-of-sample data based on all three error criterions.
Conclusion: The empirical findings of the study showed that the proposed model has higher flexibility and capability in covering unstable changes in the total index movement trend.


Main Subjects

Abbod, M. & Deshpande, K. (2008). Using Intelligent Optimization Methods to Improve the Group Method of Data Handling in Time Series Prediction. In M. Bubak, G. D. V. Albada, J. Dongarra & P. M. A. Sloot (Eds.), International Conference on Computational Science (pp.16–25). Poland: June.
Cheng, C.H., Chen, T.L. & Wei, L.Y. (2010). A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180(9), 1610–1629.
Deb, K., Pratap, A., Agrawal, S. & Meyarivan, T. (2002). Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 182–197.
Dorodi, D., Abrahimi, S. B. (2017). Presenting a new hybrid method for predicting the Stock Exchange price inde. Financial Research Journal, 18(4), 613-632. (in Persian)
Ebadati, O. M. & Mortazavi, M. (2016). An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World, 28(1), 41–55.
Fakhari, H., Valipour Khatir, M., Mousavi, M. (2017). Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting. Financial Research Journal, 19(2), 299-318. (in Persian)
Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing, 172(8), 446–452.
Galotti, M. & Schiantavelli, F. (1994). Stock mare volatility and investment: Do only fundamental matters? Economica, 61(242), 144–165.
Gharleghi, B., Shaari, A.H. & Shafighi, N. (2014). Predicting exchange rates using a novel “cointegration based neuro-fuzzy system”. International Economics, 137(1), 88–103.
Giles, C.L., Lawrence, S. & Tsoi, A.C. (2001). Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning, 44(1-2), 161–183.
Grigoryan, H. (2016). Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA). Database Systems Journal, 7(1), 12–21.
Ivakhnenko, A.G. (1968). Group Method of Data Handling - a rival of the method of stochastic approximation. Soviet Automatic Control, 3(1), 43–55.
Lawrence, R. (1997). Using Neural Networks to Forecast Stock Market Prices. Canada: University of Manitoba.
Lu, C.J., Lee, T.S. & Chiu, C.C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125.
Mehrara, M., Moeini, A., Ahrari, M. & Hamony, A. (2009). Modeling stock market prices based on GMDH Neural Network: a case study for Iran. Quarterly Journal of Economic Research and Policies, 17(50), 31–51. (in Persian)
Müller, J.A. & Lemke, F. (2000). Self-Organising Data Mining: Extracting Knowledge from Data. Hamburg: BoD.
Rafiuzzaman, M. (2014). Forecasting Chaotic Stock Market Data using Time Series Data Mining. International Journal of Computer Applications, 101(10), 27–34.
Shams, Sh., Naji Zavareh, M. (2015). Comparison Between the Hybrid Model of Genetic Fuzzy and Self - Organizing Systems and Linear Model to Predict the Price of Gold Coin Futures Contracts. Financial Research Journal, 17(2), 239-258. (in Persian)
Sun, Y. & Gao, Y. (2015). An improved hybrid algorithm based on PSO and BP for stock price forecasting. The Open Cybernetics & Systemics Journal, 9(1), 2565–2568.
Timmermann, A. & Granger, C. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), 15–27.
Tsai, C. F. & Wang, S.P. (2009). Stock Price Forecasting by Hybrid Machine Learning Techniques. In A. SIO-IONG, O. Castillo, C. Douglas, D. Dagan-Feng & L. Jeong-A(Eds.), Proceedings of the International Multi Conference of Engineers and Computer Scientists (pp.57–63). Hong Kong: March.
Tsai, C.Y. & Huang, C.L. (2009). A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Systems with Applications, 36(2), 529–1539.
Voss, M.S. & Howland, J.C. (2003). Financial modelling using social programming. In M. H. Hamza (Eds.), Financial Engineering and Applications (pp.16–25). Canada: July.
Yang, C.H., Liao, M.Y., Chen, P.L., Huang, M.T., Huang, C.W., Huang, J.S. & Chung, J.B. (2009). Constructing financial distress prediction model using group method of data handling technique. In Proceedings of the eighth, International conference on machine learning and cybernetics (pp. 2897–2902). China: July.
Yu, L., Wang, S.Y. & Lai, K.K. (2009). A neural-network-based nonlinear metamodeling approach to financial time series forecasting. Applied Soft Computing, 9(2), 563–574.
Zhang, M., He, C. & Liatsis, P. (2012). A D-GMDH model for time series forecasting. Expert Systems with Applications, 39(5), 5711–5716.
Zhu, B., He, C. & Liatsis, P. (2012). A robust missing value imputation method for noisy data. Applied Intelligence, 36(1), 61–74.