Abstract
This paper presents an application of two nature-inspired algorithms to the financial problem concerning the detection of turning points. Nature-Inspired methods are receiving a growing interest due to their ability to cope with complex tasks like classification, forecasting and anomaly detection problems. A swarm intelligence algorithm, Particle Swarm Optimization (PSO), and an artificial immune system one, the Negative Selection (NS), are applied to the problem of detection of turning points, modeled as an Anomaly Detection (AD) problem, and their performances are compared. Both methods are found to give interesting results with respect to an unpredictable behavior.
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
Azzini, A., De Felice, M., Meloni, S., Tettamanzi, A.G.B.: Soft computing techniques for Internet backbone traffic anomaly detection. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 99–104. Springer, Heidelberg (2009)
Balachandran, S., Dasgupta, D., Nino, F., Garrett, D.: A framework for evolving multi-shaped detectors in negative selection. In: Proc. of IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, pp. 401–408 (2007)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: From natural to artificial systems. Oxford University Press, Oxford (1999)
Bouvry, P., Seredynsky, F.: Anomaly detection in TCP/IP networks using immune systems paradigm. Computer Comm. 30, 740–749 (2007)
Colby, R.: The Encyclopedia of Technical Market Indicators, 2nd edn. McGraw-Hill, New York (2002)
Dasgupta, D., Ji, Z.: Real-valued negative selection algorithm with variable-sized detectors. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)
De Castro, N.L., von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evolutionary Computation 6(3), 239–251 (2002)
Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proc. of the IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA, pp. 202–212 (1994)
Freitas, A.A., Timmis, J.: Revisiting the Foundations of Artificial Immune Sustems for Data Mining. Trans. on Evolutionary Computation 11(4) (August 2007)
Herbst, A.: Analyzing and Forecasting Futures Prices. Wiley, New York (1992)
Peters, E.: Chaos and Order in the Capital Markets, 2nd edn. Wiley, New York (1996)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE International Conf. on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)
Kim, J., Bentley, P.J., Aickelin, U., Greensmith, J., Tedesco, G., Twycross, J.: Immune system approaches to intrusion detection - a review. Natural Computing 6, 413–466 (2007)
Vince, R.: The Handbook of Portfolio Mathematics: Formulas for optimal allocation & leverage. Wiley, New York (2007)
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
Azzini, A., De Felice, M., Tettamanzi, A.G.B. (2010). A Study of Nature-Inspired Methods for Financial Trend Reversal Detection. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-12242-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12241-5
Online ISBN: 978-3-642-12242-2
eBook Packages: Computer ScienceComputer Science (R0)