A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

Expert Systems with Applications

Volumn 36, Issue 4, 2009, Pages 7947-7951

  Hsu, Sheng Hsun ^a   Hsieh, JJ Po An ^b   Chih, Ting Chih ^a   Hsu, Kuei Chu ^a  

^a CHUNG HUA UNIVERSITY   (Taiwan)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Self organizing map; Stock price prediction; Support vector machine

Indexed keywords

CONFORMAL MAPPING; IMAGE RETRIEVAL; SUPPORT VECTOR MACHINES; TIME SERIES; VECTORS;

DATA POINTS; FINANCIAL INDICES; FINANCIAL MARKETS; HETEROGENEOUS DATUM; HOMOGENOUS REGIONS; INPUT SPACES; NON STATIONARIES; POTENTIAL SOLUTIONS; SELF-ORGANIZING MAP; STATISTICAL DISTRIBUTIONS; STATISTICAL PROPERTIES; STOCK PRICE FORECASTING; STOCK PRICE PREDICTION; STOCK PRICES; SUPPORT VECTOR MACHINE; SUPPORT VECTOR REGRESSIONS; TWO STAGES;

COSTS;

EID: 60249084893     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.10.065     Document Type: Article

References (18)

1. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 1-47
2. Cao L., and Gu Q. Dynamic support vector machines for non-stationary time series forecasting. Intelligent Data Analysis 6 (2002) 67-83
3. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods (2000), Cambridge, UK
4. Dittenbach M., Rauber A., and Merkl D. Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 48 (2002) 199-216
5. Fritzke B. Growing grid - A self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2 5 (1995) 9-13
6. Huang W., Nokamori Y., and Wang S.-Y. Forecasting stock market movement direction with support vector machine. Computers and Operations Research 32 (2005) 2513-2522
7. Jacobs R.A., Jordan M.A., Nowlan S.J., and Hinton G.E. Adaptive mixtures of local experts. Neural Computation 3 (1991) 79-87
8. Kim K.-j. Financial time series forecasting using support vector machines. Neurocomputing 55 (2003) 307-319
9. Kohonen T. Self-organizing maps (1995), Springer-Verlag, Berlin
10. Kohonen T., Kaski S., Lagus K., Salojvi J., Paatero V., and Sarela A. Self organization of a massive document collection. IEEE Transactions on Neural Networks 11 3 (2000) 574-585
11. Pai P.-F., and Lin C.-S. A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33 (2005) 497-505
12. Smith K.A., and Gupta J.N.D. Neural networks in business: Techniques and applications for the operations research. Computers and Operations Research 27 (2000) 1023-1044
13. Smola, A. J., & Scholkopf, B. (1998). A tutorial on support vector regression. NeuroCOLT Technical Report, TR Royal Holloway College, London, UK.
14. Tay F.E.H., and Cao L.J. Application of support vector machines in financial time series forecasting. Omega 29 (2001) 309-317
15. Tay F.E.H., and Cao L.J. Improved financial time series forecasting by combining support vector machines with self-organizing feature map. Intelligent Data Analysis 5 (2001) 339-354
16. Vapnik V.N. The nature of statistical learning theory (1995), Springer-Verlag, New York
17. Wittkemper H.-G., and Steiner M. Using neural networks to forecast the systematic risk of stocks. European Journal of Operational Research 90 (1996) 577-588
18. Yang C.C., Chen H., and Hong K. Visualization of large category map for Internet browsing. Decision Support Systems 35 (2003) 89-102