Learning gated bayesian networks for algorithmic trading

被引：0

作者：

Bendtsen, Marcus ^{[1
]}

Peña, Jose M. ^{[1
]}

机构：

[1] Department of Computer and Information Science, Linköping University

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8754卷

关键词：

Algorithmic trading; Bayesian networks; Decision support; Probabilistic graphical models;

D O I：

10.1007/978-3-319-11433-0_4

中图分类号：

学科分类号：

摘要：

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. © Springer International Publishing Switzerland 2014.

引用

页码：9 / 64

页数：55

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