A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

[1]  Daoqiang Zhang,et al.  (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition , 2005, Neurocomputing.

[2]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[3]  Bin Sun,et al.  Forecasting and identification of stock market based on modified RBF neural network , 2010, 2010 IEEE 17Th International Conference on Industrial Engineering and Engineering Management.

[4]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[5]  Chih-Wei Lee,et al.  Combining ICA with Kernel Based Regressions for Trading Support Systems on Financial Options , 2010 .

[6]  Yang Yiwen,et al.  Stock market trend prediction based on neural networks, multiresolution analysis and dynamical reconstruction , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[7]  S. S. Lam.  A genetic fuzzy expert system for stock market timing , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Xin Li,et al.  Application of Neural Networks in Financial Data Mining , 2007, International Conference on Computational Intelligence.

[9]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[10]  Lianfen Qian,et al.  Regularized Radial Basis Function Networks: Theory and Applications , 2002, Technometrics.

[11]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[12]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[13]  Chi-Jie Lu,et al.  Integrating independent component analysis-based denoising scheme with neural network for stock price prediction , 2010, Expert Syst. Appl..

[14]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[15]  Bogdan Wilamowski,et al.  Comparison between traditional neural networks and radial basis function networks , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[16]  Huaiqing Wang,et al.  Financial time series forecasting using LPP and SVM optimized by PSO , 2012, Soft Computing.

[17]  Dennis Ettes,et al.  Trading the stock markets using genetic fuzzy modeling , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[18]  Chi-Jie Lu,et al.  Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes , 2012, Expert Syst. Appl..

[19]  Ling Huang,et al.  ICA/RBF-Based Prediction of Varying Trend in Real Exchange Rate , 2006, 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06).

[20]  Massimiliano Versace,et al.  Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks , 2004, Expert Syst. Appl..

[21]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[22]  Liu Quan,et al.  Financial time series forecasting using LPP and SVM optimized by PSO , 2013, SOCO 2013.

[23]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[24]  Yang Yi.  Stock Market Trend Prediction Based on Neural Networks, Multiresolution Analysis and Dynamical Reconstruction , 2001 .

[25]  Chih-Chou Chiu,et al.  Integration of nonlinear independent component analysis and support vector regression for stock price forecasting , 2013, Neurocomputing.

[26]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[27]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Huaiqing Wang,et al.  A Feature Fusion Based Forecasting Model for Financial Time Series , 2014, PLoS ONE.

[29]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[30]  Adriano Lorena Inácio de Oliveira,et al.  A method for automatic stock trading combining technical analysis and nearest neighbor classification , 2010, Expert Syst. Appl..

[31]  Xu-lei Wang,et al.  Solve fractal dimension of Shanghai stock market by RBF neural networks , 2009, 2009 International Conference on Management Science and Engineering.

[32]  Daoqiang Zhang,et al.  ( 2 D ) 2 PCA : 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition , 2005 .

[33]  R. Haugen,et al.  The New Finance: The Case Against Efficient Markets , 1995 .

[34]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[35]  Guo Kun,et al.  Economy barometer analysis of China Stock Market:A dynamic analysis based on the thermal optimal path method , 2012 .

[36]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[37]  Li Zi-zhen.  Principal Component Analysis and BP Neural Network Modeling for Futures Foresting , 2007 .

[38]  S. I. Ao,et al.  A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting , 2011, Soft Comput..

[39]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[40]  Ajith Abraham,et al.  Hybrid Intelligent Systems for Stock Market Analysis , 2001, International Conference on Computational Science.

[41]  Arkady Borisov.  MODELLING RIGA STOCK EXCHANGE INDEX USING NEURAL NETWORKS , 2002 .

[42]  Marc J. Schniederjans,et al.  A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market , 2005, Comput. Oper. Res..

[43]  Chenn-Jung Huang,et al.  Application of wrapper approach and composite classifier to the stock trend prediction , 2008, Expert Syst. Appl..