Moving average-based stock trading rules from particle swarm optimization

N. M. Kwok; G. Fang; Q. P. Ha

doi:10.1109/AICI.2009.418

Moving average-based stock trading rules from particle swarm optimization

N. M. Kwok, G. Fang, Q. P. Ha

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

12 Citations (Scopus)

Abstract

![CDATA[Trading rules derived from technical analysis are valuable tools in making profits from the financial market. Among those trading rules, the moving average-based rule has been the most widely adopted choice by a large number of investors. Buy/sell signals are identified when curves of long/short averages cross each other. With an attempt to optimize the rule and maximize the trading profit, this paper propose the use of the particle swarm optimization algorithm to determine the appropriate long/short durations when calculating the averages. Trading signals are subsequently generated by the golden cross strategy. The best combination of long/short durations is determined by comparing the profits that can be made among alternative durations. Real-world indices, covering three years approximately, from several established and emerging stock markets are used to verify the effectiveness of the proposed method.]]

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009): Shanghai, China, 7-8 November 2009
Publisher	IEEE
Pages	149-153
Number of pages	5
ISBN (Print)	9780769538167
DOIs	https://doi.org/10.1109/AICI.2009.418
Publication status	Published - 2009
Event	International Conference on Artificial Intelligence and Computational Intelligence - Duration: 1 Jan 2011 → …

Conference

Conference	International Conference on Artificial Intelligence and Computational Intelligence
Period	1/01/11 → …

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10.1109/AICI.2009.418

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@inproceedings{6dc7991fbbe04182a33d0c280da855e6,

title = "Moving average-based stock trading rules from particle swarm optimization",

abstract = "![CDATA[Trading rules derived from technical analysis are valuable tools in making profits from the financial market. Among those trading rules, the moving average-based rule has been the most widely adopted choice by a large number of investors. Buy/sell signals are identified when curves of long/short averages cross each other. With an attempt to optimize the rule and maximize the trading profit, this paper propose the use of the particle swarm optimization algorithm to determine the appropriate long/short durations when calculating the averages. Trading signals are subsequently generated by the golden cross strategy. The best combination of long/short durations is determined by comparing the profits that can be made among alternative durations. Real-world indices, covering three years approximately, from several established and emerging stock markets are used to verify the effectiveness of the proposed method.]]",

author = "Kwok, {N. M.} and G. Fang and Ha, {Q. P.}",

year = "2009",

doi = "10.1109/AICI.2009.418",

language = "English",

isbn = "9780769538167",

pages = "149--153",

booktitle = "Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009): Shanghai, China, 7-8 November 2009",

publisher = "IEEE",

note = "International Conference on Artificial Intelligence and Computational Intelligence ; Conference date: 01-01-2011",

}

Kwok, NM, Fang, G & Ha, QP 2009, Moving average-based stock trading rules from particle swarm optimization. in Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009): Shanghai, China, 7-8 November 2009. IEEE, pp. 149-153, International Conference on Artificial Intelligence and Computational Intelligence, 1/01/11. https://doi.org/10.1109/AICI.2009.418

Moving average-based stock trading rules from particle swarm optimization. / Kwok, N. M.; Fang, G.; Ha, Q. P.
Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009): Shanghai, China, 7-8 November 2009. IEEE, 2009. p. 149-153.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Moving average-based stock trading rules from particle swarm optimization

AU - Kwok, N. M.

AU - Fang, G.

AU - Ha, Q. P.

PY - 2009

Y1 - 2009

N2 - ![CDATA[Trading rules derived from technical analysis are valuable tools in making profits from the financial market. Among those trading rules, the moving average-based rule has been the most widely adopted choice by a large number of investors. Buy/sell signals are identified when curves of long/short averages cross each other. With an attempt to optimize the rule and maximize the trading profit, this paper propose the use of the particle swarm optimization algorithm to determine the appropriate long/short durations when calculating the averages. Trading signals are subsequently generated by the golden cross strategy. The best combination of long/short durations is determined by comparing the profits that can be made among alternative durations. Real-world indices, covering three years approximately, from several established and emerging stock markets are used to verify the effectiveness of the proposed method.]]

AB - ![CDATA[Trading rules derived from technical analysis are valuable tools in making profits from the financial market. Among those trading rules, the moving average-based rule has been the most widely adopted choice by a large number of investors. Buy/sell signals are identified when curves of long/short averages cross each other. With an attempt to optimize the rule and maximize the trading profit, this paper propose the use of the particle swarm optimization algorithm to determine the appropriate long/short durations when calculating the averages. Trading signals are subsequently generated by the golden cross strategy. The best combination of long/short durations is determined by comparing the profits that can be made among alternative durations. Real-world indices, covering three years approximately, from several established and emerging stock markets are used to verify the effectiveness of the proposed method.]]

UR - http://handle.uws.edu.au:8081/1959.7/562552

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5375738

U2 - 10.1109/AICI.2009.418

DO - 10.1109/AICI.2009.418

M3 - Conference Paper

SN - 9780769538167

SP - 149

EP - 153

BT - Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI 2009): Shanghai, China, 7-8 November 2009

PB - IEEE

T2 - International Conference on Artificial Intelligence and Computational Intelligence

Y2 - 1 January 2011

ER -

Moving average-based stock trading rules from particle swarm optimization

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