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Learning Gated Bayesian Networks for Algorithmic Trading

  • Marcus Bendtsen
  • Jose M. Peña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8754)

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.

Keywords

Probabilistic graphical models Bayesian networks algorithmic trading decision support 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marcus Bendtsen
    • 1
  • Jose M. Peña
    • 1
  1. 1.Department of Computer and Information ScienceLinköping UniversitySweden

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