Stock market forecasting model based on a hybrid ARMA and support vector machines

Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARMA model and the SVMs model in the stock market forecasting problem in an attempt to provide a model with better explanatory power. Real data sets of stock market were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.

[1]  Kin Keung Lai,et al.  A Novel Adaptive Learning Algorithm for Stock Market Prediction , 2005, ISAAC.

[2]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[3]  Anthony Brabazon,et al.  A hybrid genetic model for the prediction of corporate failure , 2004, Comput. Manag. Sci..

[4]  Kyung-shik Shin,et al.  A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets , 2007, Appl. Soft Comput..

[5]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[6]  Jen-Ying Shih,et al.  A study of Taiwan's issuer credit rating systems using support vector machines , 2006, Expert Syst. Appl..

[7]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

[8]  N. Baba,et al.  An intelligent forecasting system of stock price using neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[9]  Ah Chung Tsoi,et al.  Lessons in Neural Network Training: Overfitting May be Harder than Expected , 1997, AAAI/IAAI.

[10]  Lei Xu,et al.  Application of adaptive RPCL-CLP with trading system to foreign exchange investment , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[11]  Martin Brown,et al.  Network Performance Assessment for Neurofuzzy Data Modelling , 1997, IDA.

[12]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[13]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[14]  George M. Giaglis,et al.  A hybrid approach for improving predictive accuracy of collaborative filtering algorithms , 2007, User Modeling and User-Adapted Interaction.

[15]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[16]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[17]  Young-Chan Lee,et al.  Application of support vector machines to corporate credit rating prediction , 2007, Expert Syst. Appl..

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[20]  Yiu-ming Cheung,et al.  Application of adaptive RPCL-CLP with trading system to foreign exchange investment , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[21]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..