Combining ICA with Kernel based regressions for trading support systems on financial options

Smart Innovation, Systems and Technologies

Volumn 4, Issue , 2010, Pages 163-169

  Huang, Shian Chang ^a   Li, Chuan Chyuan ^a   Lee, Chih Wei ^b   Chang, M Jen ^c  

^a NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF BUSINESS   (Taiwan)

^c NATIONAL DONG HWA UNIVERSITY   (Taiwan)

Author keywords

Algorithmic trading system; Financial trading; Independent component analysis; Kernel partial least square regression; Support vector machine

Indexed keywords

ALGORITHMIC TRADING SYSTEM; COMPLICATED PRODUCTS; FINANCIAL TRADING; INDEPENDENT COMPONENT ANALYSIS(ICA); KERNEL BASED REGRESSION; KERNEL PARTIAL LEAST SQUARE REGRESSIONS; KNOWLEDGE-INTENSIVE INDUSTRIES; PERFORMANCE IMPROVEMENTS;

ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; INDEPENDENT COMPONENT ANALYSIS; INVESTMENTS; PROFITABILITY; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

COMMERCE;

EID: 84879320585     PISSN: 21903018     EISSN: 21903026     Source Type: Book Series    
DOI: 10.1007/978-3-642-14616-9_15     Document Type: Article

References (9)

1. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
2. Hyvärinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411-430 (2000)
3. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics. Springer, NY (2002)
4. Rosipal, R.: Kernel Partial Least Squares for Nonlinear Regression and Discrimination. Neural Network World 13(3), 291-300 (2003)
5. Rosipal, R., Kramer, N.: Overview and Recent Advances in Partial Least Squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) Subspace, Latent Structure and Feature Selection Techniques, pp. 34-51. Springer, Heidelberg (2006)
6. Rosipal, R., Trejo, L.J.: Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space. Journal of Machine Learning Research 2, 97-123 (2001)
7. Schoelkopf, B., Burges, C.J.C., Smola, A.J.: Advances in kernel methods - support vector learning. MIT Press, Cambridge (1999)
8. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
9. Wold, S., Ruhe, H., Wold, H., Dunn, W.J.: The collinearity problem in linear regression. The PLS approach to generalized inverse. SIAM Journal of Scientic and Statistical Computations 5, 735-743 (1984)