Abstract
Using Quinlan’s Cubist, this paper examines whether there is a consistent, interpretation of the efficient market hypothesis between financial econometrics and machine learning. In particular, we ask whether machine learning can be useful only in the case when the market is not efficient. Based on the forecasting performance of Cubist in our artificial returns, some evidences seems to support this consistent interpretation. However, there are a few cases whereby Cubist can beat the random walk even though the series is independent. As a result, we do not consider that the evidence is strong enough to convince one to give up his reliance on machine learning even though the efficient market hypothesis is sustained.
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References
Chen, S.-H. Kuo, T.-W. (1999): Towards an Agent-Based Foundation of Financial Econometrics: An Approach Based on Genetic-Programming Artificial Markets. In Banzhaf, W., Daida, J., Eiben, A. F., Garzon, M. H., Honavar, V., Jakiela, M., Smith, R. E. (Eds.), CEC CO-99: Proceedings of the Genetic and Evolutionary Computation Conference, Vol. 2..Morgan Kaufmann, 966–973
Chen, S.-H., Tan, C.-W. (1999): Brief Signals in the Real and Artificial Stock Markets: An Approach Based on the Complexity Function. In: Arabrria, II. R.. (Ed.), Proceedings of the International Conference on Artificial intelligence, Vol. II. Computer Science Research, Education, and Application Press, 423–429
Chen, S.-H., Yeh, C.-H. (1999): On the Consequence of “Following the Herd”: Evidence from the Artificial Stock Market. In: Arabrria, H. R. (Ed.), Proceedings of the International Conference on Artificial Intelligence, Vol. II. Computer Science Research, Education, and Application Press, 388–394
Chen, S. -Il., Yeh, C. -H. (2001): Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market..Iournal of Economic; Dynamics and Control. 25, 363–393
Chen, S.H., Wang, H.-S., Zhang, B.-T. (1999): Forecasting High-Frequency Financial Time Series with Evolutionary Neural Trees: The Case of Hang-Seng Stock Index. In: Arabrria; H. R. (Ed.), Proceedings of the International Conference on Artificial Intelligence, Vol. II. Computer Science Research, Education, and Application Press, 437–443
Chen, S.-H., Yeh, C.-H. (2002): On the Emergent Properties of Artificial Stock Markets: The Efficient Market Hypothesis and the Rational Expectations Hypothesis. Journal of Economic Behavior and Organization, 99 (1), 217–239
Chen, S.-H., Liao, C.-C. (2002): ‘Testing for Granger Causality in the Stock-Price Volume Relation: A Perspective from the Agent-Based Model of Stock Markets. Information Sciences, forthcoming
Grossman, S. J., Stiglitz, J. (1980): On the Impossibility of lnformationally Efficiency Markets. American Economic Review, 70, 393–108
Rissanen, J. (1986): Stochastic Complexity and Modeling. Annals of Statistics, 14 (3), 1080–1100
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© 2004 Springer-Verlag Berlin Heidelberg
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Chen, SH., Kuo, TW. (2004). Are Efficient Markets Really Efficient?: Can Financial Econometric Tests Convince Machine-Learning People?. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_13
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DOI: https://doi.org/10.1007/978-3-662-06373-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07902-3
Online ISBN: 978-3-662-06373-6
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