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

Xianneng Li, Wen He, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages731-738
Number of pages8
ISBN (Print)9781479914883
DOIs
Publication statusPublished - 2014 Sep 16
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing
Duration: 2014 Jul 62014 Jul 11

Other

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

Fingerprint

Network Programming
Graph in graph theory
Genetic Network
Genetic Programming
Probabilistic Model
Probabilistic Modeling
Directed graphs
Reinforcement learning
Reinforcement Learning
Stock Market
Directed Graph
Profit
Profitability
Demonstrate
Statistical Models
Model
Financial markets

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

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 (pp. 731-738). [6900421] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900421

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 proceedingConference 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
Li X, He W, Hirasawa K. Creating stock trading rules using graph-based estimation of distribution algorithm. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 731-738. 6900421 https://doi.org/10.1109/CEC.2014.6900421
Li, Xianneng ; He, Wen ; Hirasawa, Kotaro. / Creating stock trading rules using graph-based estimation of distribution algorithm. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 731-738
@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 = "9",
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.",

}

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.

ER -