Abstract
The Minority Game (MG) is a simple model for understanding collective behavior of agents in an idealized situation for a finite resource. It has been regarded as an interesting complex dynamical disordered system from a statistical mechanics point of view. In previous work, we have investigated the problem of learning the agent behaviors in the minority game by assuming the existence of one ”intelligent agent” who can learn from other agent behaviors. In this paper, we propose a framework called Minority Game Data Mining (MGDM), that assumes the collective data are generated from combining the behaviors of variant groups of agents following the minority games. We then apply this framework to time-series data analysis in the real-world. We test on a few stocks from the Chinese market and the US Dollar-RMB exchange rate. The experimental results suggest that the winning rate of the new model is statistically better than a random walk.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Arthur, W.B.: Bounded rationality and inductive behavior (the El Farol problem). American Economic Review 84, 406 (1994)
Challet, D., Marsili, M., Zhang, Y.C.: Stylized facts of financial markets and market crashes in minority games. Physica A 294, 514 (2001)
Challet, D., Marsili, M., Zecchina, R.: Statistical mechanics of systems with heterogeneous agents: Minority Games. Phys. Rev. Lett. 84, 1824 (2000)
Challet, D., Zhang, Y.C.: Emergence of cooperation in an evolutionary game. Physica A 246, 407 (1997)
Gode, D.K., Sunder, S.: Allocative efficiency of markets with zero-intelligence traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)
Holland, J.H.: Emergence: From Chaos to Order (1998)
Li, G., Ma, Y., Dong, Y., Qin, Z.: Behavior learning in minority games. To appear in Collaborative Agent-Research and Development International Workshop (CARE) (2009)
Johnson, N.F., Jefferies, P., Hui, P.M.: Financial Market Complexity. Oxford University Press, Oxford (2003)
Lo, T.S., Hui, P.M., Johnson, N.F.: Theory of the evolutionary minority game. Phys. Rev. E 62, 4393 (2000)
Mantegna, R.N., Stanley, H.E.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (1999)
Qin, Z.: Market mechanism designs with heterogeneous trading agents. In: Proceedings of Fifth International Conference on Machine Learning and Applications (ICMLA-2006), Orlando, Florida, USA, pp. 69–74 (2006)
Qin, Z.: Naive Bayes classification given probability estimation trees. In: The Proceedings of ICMLA-06, pp. 34–39 (2006)
Qin, Z., Lawry, J.: Decision tree learning with fuzzy labels. Information Sciences 172(1-2), 91–129 (2005)
Rapoport, A., Chammah, A.M., Orwant, C.J.: Prsoner’s Dilemma: A Study in Conflict and Cooperation. Uni. of Michigan Press, Ann Arbor (1965)
Smith, V.L.: An experimental study of competitive market behavior. Journal of Political Economy 70, 111–137 (1962)
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
Ma, Y., Li, G., Dong, Y., Qin, Z. (2010). Minority Game Data Mining for Stock Market Predictions. In: Cao, L., Bazzan, A.L.C., Gorodetsky, V., Mitkas, P.A., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2010. Lecture Notes in Computer Science(), vol 5980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15420-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-15420-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15419-5
Online ISBN: 978-3-642-15420-1
eBook Packages: Computer ScienceComputer Science (R0)