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

^*Corresponding author for this work

Information, Production and Systems Research Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	731-738
Number of pages	8
ISBN (Print)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900421
Publication status	Published - 2014 Sept 16
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing Duration: 2014 Jul 6 → 2014 Jul 11

Other

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

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/CEC.2014.6900421

Cite this

Creating stock trading rules using graph-based estimation of distribution algorithm. / Li, Xianneng; He, Wen; Hirasawa, Kotaro.

Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 731-738 6900421.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

@inproceedings{020d71aaeaa34ad383138d40135b5f33,

title = "Creating stock trading rules using graph-based estimation of distribution algorithm",

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.",

author = "Xianneng Li and Wen He and Kotaro Hirasawa",

year = "2014",

month = sep,

day = "16",

doi = "10.1109/CEC.2014.6900421",

language = "English",

isbn = "9781479914883",

pages = "731--738",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

TY - GEN

T1 - Creating stock trading rules using graph-based estimation of distribution algorithm

AU - Li, Xianneng

AU - He, Wen

AU - Hirasawa, Kotaro

PY - 2014/9/16

Y1 - 2014/9/16

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84908604560&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908604560&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900421

DO - 10.1109/CEC.2014.6900421

M3 - Conference contribution

AN - SCOPUS:84908604560

SN - 9781479914883

SP - 731

EP - 738

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

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

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this