Abstract
Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strategy buy-and-hold.
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References
Treleaven, P., Galas, M., Lalchand, V.: Algorithmic Trading Review. Commun. ACM. 56, 76–85 (2013)
Nuti, G., Mirghaemi, M., Treleaven, P., Yingsaeree, C.: Algorithmic Trading. Computer 44, 61–69 (2011)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann (1988)
Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer (2007)
Bendtsen, M., Peña, J.M.: Gated Bayesian Networks. In: 12th Scandinavian Conference on Artificial Intelligence, pp. 35–44. IOS Press (2013)
Pardo, R.: The Evaluation and Optimization of Trading Strategies. John Wiley & Sons (2008)
Chan, E.P.: Quantitative Trading: How to Build Your Own Algorithmic Trading Business. John Wiley & Sons (2009)
Sharpe, W.F.: Likely Gains from Market Timing. Financial Analysts Journal 31, 60–69 (1975)
Tashman, L.J.: Out-of-Sample Tests of Forecasting Accuracy: An Analysis and Review. International Journal of Forecasting 11, 437–450 (2000)
Journal of Technical Analysis, http://www.mta.org
Murphy, J.J.: Technical Analysis of the Financial Markets. New York Institute of Finance (1999)
Liehr, S., Pawelzik, K., Kohlmorgen, J., Lemm, S., Müller, K.-R.: Hidden Markov Gating for Prediction of Change Points in Switching Dynamical Systems. In: ESANN, pp. 405–410 (1999)
Peña, J.M.: Every LWF and AMP Chain Graph Originates From a Set of Causal Models. ArXiv e-prints (2013)
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Bendtsen, M., Peña, J.M. (2014). Learning Gated Bayesian Networks for Algorithmic Trading. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_4
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DOI: https://doi.org/10.1007/978-3-319-11433-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11432-3
Online ISBN: 978-3-319-11433-0
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