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Gated Bayesian Networks for Algorithmic Trading
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8678-1164
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
2016 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 69, 58-80 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 69, 58-80 p.
Keyword [en]
Probabilistic graphical models, Bayesian networks, algorithmic
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-124066DOI: 10.1016/j.ijar.2015.11.002ISI: 000368957000004OAI: oai:DiVA.org:liu-124066DiVA: diva2:895685
Note

Funding agencies: Center for Industrial Information Technology, Linkoping University (CENIIT) [09.01]; Swedish Research Council [2010-4808]

Available from: 2016-01-19 Created: 2016-01-19 Last updated: 2016-03-10

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The full text will be freely available from 2016-11-11 00:00
Available from 2016-11-11 00:00

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