Abstract
The aim of this work is to forecast future opportunities in financial stock markets, in particular, we focus our attention on situations where positive instances are rare, which falls into the domain of Chance Discovery. Machine learning classifiers extend the past experiences into the future. However the imbalance between positive and negative cases poses a serious challenge to machine learning techniques. Because it favours negative classifications, which has a high chance of being correct due to the nature of the data. Genetic Algorithms have the ability to create multiple solutions for a single problem. To exploit this feature we propose to analyse the decision trees created by Genetic Programming. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. To illustrate our approach, it was applied to predict investment opportunities with very high returns. From experiment results we showed that the Repository Method can consistently improve both the recall and the precision.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tsang, E.P., Martinez-Jaramillo, S.: Computational finance. In: IEEE Computational Intelligence Society Newsletter, pp. 3–8 (2004)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Li, J.: A genetic programming based tool for financial forecasting. Colchester CO4 3SQ, UK: PhD Thesis, University of Essex (2001)
Tsang, E.P., Yung, P., Li, J.: Eddie-automation, a decision support tool for financial forecasting. Journal of Decision Support Systems, Special Issue on Data Mining for Financial Decision Making 37(4) (2004)
Tsang, E.P., Markose, S., Er, H.: Chance discovery in stock index option and future arbitrage. In: New Mathematics and Natural Computation, vol. 1(3), pp. 435–447. World Scientific, Singapore (2005)
Abe, A., Ohsawa, Y.: Special issue on chance disocvery. In: New Generation Computing, ser. 1, vol. 21, pp. 1–2. Springer, Berlin (2002)
Ohsawa, Y., McBurney, P.: Chance discovery. Springer, Heidelberg (2003)
Provost, F.J., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Madison, W. (ed.) Proc. Fifteenth Intl. Conf. Machine Learning, pp. 445–553 (1998), [Online] Available: citeseer.ist.psu.edu/provost97case.html
Kubat, M., Holte, R.C., Matwin, S.: Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30, 195–215 (1998)
Tsang, E.P., Li, J., Butler, J.: Eddie beats the bookies. International Journal of Software, Practice and Experience 20(10), 1033–1043 (1998)
Sharpe, W.F., Alexander, G.J., Bailey, J.V.: Investments. Prentice-Hall International, Inc., Upper Saddle River (1995)
Quinlan, J.R.: Rule induction with statistical data. Journal of the operational research Society 38, 347–352 (1987)
Chomsky, N.: Aspects of the theory of syntax. MIT Press, Cambridge (1965)
Angeline, P.: Genetic Programming and Emergent Intelligence. In: Kinnear, Jr., K.E. (ed.) Advances in Genetic Programming. Ch. 4, pp. 75–98. MIT Press, Cambridge (1994)
Nordin, P., Francone, F., Banzhaf, W.: Explicitly Defined Introns and Destructive Crossover in Genetic Programming. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, California, USA, July 9, pp. 6–22 (1995)
Soule, T., Foster, J.A.: Code size and depth flows in genetic programming. In: Genetic Programming 1997: Proceeding of the Second Annual Conference, pp. 313–320. Morgan Kaufmann, San Francisco (1997)
Langdon, W.B.: Quadratic bloat in genetic programming. In: Proceedings of the Genetic and evolutionary Computation Conference, pp. 451–458 (2000)
Garcia-Almanza, A.L.: Technical report, rule simplification, http://privatewww.essex.ac.uk/~algarc/documents/Rule-simplification.doc
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garcia-Almanza, A.L., Tsang, E.P.K. (2006). The Repository Method for Chance Discovery in Financial Forecasting. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_5
Download citation
DOI: https://doi.org/10.1007/11893011_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
eBook Packages: Computer ScienceComputer Science (R0)