Abstract
The paper presents a new method for multidimensional representation of financial information in the context of technical analysis. Typically, technical analysis of given financial instrument does not take into account a broader view on the market. We want to analyze the information about the environment of the primary instrument. Hence, there is the problem of the results synthesis in a coherent and a transparent way. In this paper we propose aggregation of the information from different sources into a single aggregate graph which enables a technical analysis. The complete information is obtained with the p-norms approach. To assess the impact of particular information on the primary instrument we applied divergence measures such as Csiszár divergence and Beta divergence. Practical experiment on the stock exchange data confirmed the validity of proposed approach.
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.
Similar content being viewed by others
References
Amari, S.: Diferential-Geometrical Methods in Statistics. Springer, Heidelberg (1985)
Amari, S.: Information geometry of the EM and EM algorithms for neural networks. Neural Networks 8, 1379–1408 (1995)
Anscombe, F.J.: Graphs in statistical analysis. The American Statistician 27, 17–21 (1973)
Cichocki, A., Zdunek, R., Amari, S.: Csiszar’s Divergences for Non-Negative Matrix Factorization: Family of New Algorithms. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 32–39. Springer, Heidelberg (2006)
Cichocki, A., Zdunek, R., Phan, A.-H., Amari, S.: Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis. John Wiley, Chichester (2009)
Csiszar, I.: Information measures: A critical survey. In: Prague Conference on Information Theory, vol. A, pp. 73–86. Academia Prague (1974)
Fama, E.: Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance 25(2), 383–417 (1970)
Hellstrom, T.: ASTA - a Tool for Development of Stock Prediction Algorithms. Theory of Stochastic Processes 5(21), 22–32 (1999)
Hellstrom, T., Holmstrom, K.: Parameter Tuning in Trading Algorithms Using ASTA. In: Computational Finace. MIT 3 Press, Cambridge (1999)
Jaynes, E.T.: Probability theory the logic of science. Cambridge Univ. Press, Cambridge (2003)
Kennedy, R.L., Lee, Y., Van Roy, B., Reed, C., Lippman, R.P. (eds.): Solving Data Mining Problems with Pattern Recognition. Prentice Hall, Englewood Cliffs (1997)
Krutsinger, J.: Trading Systems: Secrets of the Masters. McGraw-Hill, New York (1997)
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)
Minami, M., Eguchi, S.: Robust blind source separation by beta-divergence. Neural Computation 14, 1859–1886 (2002)
Murphy, J.J.: Technical Analysis of the Financial Markets. New York Institute of Finance (1999)
Murphy, J.J.: Intermarket Analysis Profiting from Global Market Relationships. John Wiley & Sons Inc., Chichester (2004)
Nison, S.: Japanese Candlestick Charting Techniques. New York Institute of Finance (1991)
Peters, E.: Fractal Market Analysis: Applying Chaos Theory to Investment & Economics. John Wiley & Sons, Chichester (1994)
Prechter, R., Frost, A.J.: Elliott Wave Principle: Key to Market Behavior. New Classics Library (1998)
Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. The American Statistician 42(1), 59–66 (1988)
Schwager, J.: Stock Market Wizards: Interviews with America’s Top Stock Traders. Harper Paperbacks (1993)
Schwager, J.: The New Market Wizards: Conversations with America’s Top Traders. Harper Paperbacks (1994)
Sperandeo, V.: Trader Vic: Methods of a Wall Street Master. J. Wiley & Sons, Chichester (1993)
Therrien, C.W.: Discrete Random Signals and Statistical Signal Processing. Prentice Hall, New Jersey (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szupiluk, R., Wojewnik, P., Ząbkowski, T. (2010). Aggregated Information Representation for Technical Analysis on Stock Market with Csiszár Divergence. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13541-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-13541-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13540-8
Online ISBN: 978-3-642-13541-5
eBook Packages: Computer ScienceComputer Science (R0)