Creating stock trading rules using graph-based estimation of distribution algorithm

Xianneng Li*, Wen He, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems - stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.

本文言語English
ホスト出版物のタイトルProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ731-738
ページ数8
ISBN(印刷版)9781479914883
DOI
出版ステータスPublished - 2014 9 16
イベント2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing
継続期間: 2014 7 62014 7 11

Other

Other2014 IEEE Congress on Evolutionary Computation, CEC 2014
CityBeijing
Period14/7/614/7/11

ASJC Scopus subject areas

  • 人工知能
  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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