Abstract
In this article we present a new method for the analysis of dependencies in case of multivariate time series. In this approach, we assume that the set of time series representing the various financial instruments creates a multidimensional variable. Such a multidimensional variable is decomposed into independent components which enable to analyze the morphology of given financial instruments and to identify the hidden interdependencies. We propose a new multiplicative version of the Natural Gradient ICA algorithm that could be used in automated trading systems or modeling environments. The presented method is tested on real stock markets data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Back, A.D., Weigend, A.S.: A First Application of Independent Component. Analysis to Extracting Structure from Stock Returns. International Journal of Neural Systems 8, 473–484 (1997)
Cardoso, J.F.: High-order contrasts for independent component analysis. Neural Computation 11(1), 157–192 (1999)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing. John Wiley, Chichester (2002)
Drakakis, K., Rickard, S., de Frein, R., Cichocki, A.: Analysis of financial data using non-negative matrix factorization. Int. Math. Forum 3(39), 1853–1870 (2008)
Hellstrom, T.: ASTA - a Tool for Development of Stock Prediction Algorithms. Theory of Stochastic Processes 5(21), 22–32 (1999)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley (2001)
Krutsinger, J.: Trading Systems: Secrets of the Masters. McGraw-Hill (1997)
Lee, D.D., Seung, H.S.: Learning of the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Li, Y., Cichocki, A., Amari, S.: Sparse component analysis for blind source separation with less sensors than sources. In: Fourth Int. Symp. on ICA and Blind Signal Separation, Nara, Japan, pp. 89–94 (2003)
Luo, Y., Davis, D., Liu, K.: A Multi-Agent Decision Support System for Stock Trading. The IEEE Network Magazine Special Issue on Enterprise Networking and Services 16(1) (2002)
Murphy, J.J.: Technical Analysis of the Financial Markets. New York Institute of Finance (1999)
Nison, S.: Japanese Candlestick Charting Techniques. New York Institute of Finance (1991)
Peters, E.: Fractal market analysis. John Wiley and Son (1996)
Shiryaev, A.N.: Essentials of stochastic finance: facts, models, theory. World Scientific, Singapore (1999)
Schwager, J.: Stock Market Wizards: Interviews with America’s Top Stock Traders. Harper Paperbacks (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2012). Multiplicative ICA Algorithm for Interaction Analysis in Financial Markets. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_72
Download citation
DOI: https://doi.org/10.1007/978-3-642-29350-4_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
eBook Packages: Computer ScienceComputer Science (R0)