Abstract
We consider the problem of forecasting under the environments of sudden unexpected changes. The objective of the forecasting is to detect several different types of changes and to be adaptive to these changes in the automated way. The main contribution of this paper is a development of a novel forecast method based on paired evaluators, the stable evaluator and the reactive evaluator, that are good at dealing with consecutive concept drifts. A potential application of such drifts is Finance. Our back-testing using financial data in US demonstrates that our forecasting method is effective and robust against several sudden changes in financial markets including the late-2000s recessions.
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References
Bach, S., Maloof, M.: Paired learners for concept drift. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 23–32 (2008)
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of SIAM International Conference on Data Mining (SDM 20007), pp. 443–448 (2007)
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavalda, R.: New ensemble methods for evolving data streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 139–148 (2009)
Carhart, M.M.: On persistence in mutual fund performance. Journal of Finance 52(1), 57–82 (1997)
Fama, E.F., French, K.R.: The cross-section of expected stock returns. Journal of Finance 47(2), 427–465 (1992)
Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33(1), 3–56 (1993)
French, K.R.: Fama/french factors in u.s. research returns data, http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html (accessed July 23, 2010)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with Drift Detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)
Giacomini, R., Rossi, B.: Detecting and predicting forecast breakdowns. Review of Economic Studies 76(2), 669–705 (2009)
Kolter, J., Maloof, M.: Dynamic weighted majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8, 2755–2790 (2007)
Kuncheva, L.I., Žliobaitė, I.: On the window size for classification in changing environments. Intelligent Data Analysis 13(6), 861–872 (2009)
Lazarescu, M.M., Venkatesh, S., Bui, H.H.: Using multiple windows to track concept drift. Intelligent Data Analysis 8(1), 29–59 (2004)
Lintner, J.: The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics 47(1), 13–37 (1965)
Mossin, J.: Equilibrium in a capital asset market. Econometrica 34(4), 768–783 (1966)
Nara, Y., Ohsawa, Y.: Tools for shifting human context into disasters: a case-based guideline for computer-aided earthquake proofs. In: Proceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, pp. 655–658 (2000)
Nisida, K., Yamauchi, K.: Learning and detecting concept drift with two online classifiers. In: Proceedings of the 22nd Annual Conference of the Japanese Society for Artificial Intelligence, pp. 3C2–1 (2008)
Ohsawa, Y., Nara, Y.: Decision process modeling across internet and real world by double helical model of chance discovery. New Generation Computing 21, 109–121 (2003)
Sharpe, W.F.: Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance 19(3), 425–442 (1964)
So, J., Furuhata, M., Mizuta, T.: Operational model considering transaction costs to correspond to sudden changes in japanese stock markets. In: Proceedings of the 6th workshop SIG-FIN in the Japanese Society for Artificial Intelligence, pp. 23–29 (2010)
Žliobaitė, I.: Learning under concept drift: an overview. Technical report, Vilnius University, Faculty of Mathematics and Informatics (2009)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
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Furuhata, M., Mizuta, T., So, J. (2013). Paired Evaluators Method to Track Concept Drift: An Application in Finance. In: Ohsawa, Y., Abe, A. (eds) Advances in Chance Discovery. Studies in Computational Intelligence, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30114-8_9
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DOI: https://doi.org/10.1007/978-3-642-30114-8_9
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