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

Xianneng Li; Wen He; Kotaro Hirasawa

doi:10.1109/CEC.2014.6900421

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

情報生産システム研究センター

研究成果: Conference contribution

5 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	https://doi.org/10.1109/CEC.2014.6900421
出版ステータス	Published - 2014 9月 16
イベント	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing 継続期間: 2014 7月 6 → 2014 7月 11

Other

Other	2014 IEEE Congress on Evolutionary Computation, CEC 2014
City	Beijing
Period	14/7/6 → 14/7/11

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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10.1109/CEC.2014.6900421

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引用スタイル

Li, X, He, W & Hirasawa, K 2014, Creating stock trading rules using graph-based estimation of distribution algorithm. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900421, Institute of Electrical and Electronics Engineers Inc., pp. 731-738, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, 14/7/6. https://doi.org/10.1109/CEC.2014.6900421

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abstract = "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.",

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

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