Moving average crossovers for short-term equity investment with L-Gem based RBFNN

Gao Yang CAI; Wing W. Y. NG; Patrick P. K. CHAN; Michael FIRTH; Daniel S. YEUNG

doi:10.1109/ICMLC.2010.5580985

Moving average crossovers for short-term equity investment with L-Gem based RBFNN

Gao Yang CAI, Wing W. Y. NG, Patrick P. K. CHAN, Michael FIRTH, Daniel S. YEUNG

Department of Finance and Insurance

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy.

Original language	English
Title of host publication	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages	1684-1688
Number of pages	5
Volume	4
DOIs	https://doi.org/10.1109/ICMLC.2010.5580985
Publication status	Published - 1 Jan 2010

Keywords

Equity market
L-GEM
Moving Average crossover
RBFNN

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10.1109/ICMLC.2010.5580985

Cite this

@inproceedings{e61b8e8c7a304480b69d0782e71f2b91,

title = "Moving average crossovers for short-term equity investment with L-Gem based RBFNN",

abstract = "The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy.",

keywords = "Equity market, L-GEM, Moving Average crossover, RBFNN",

author = "CAI, {Gao Yang} and NG, {Wing W. Y.} and CHAN, {Patrick P. K.} and Michael FIRTH and YEUNG, {Daniel S.}",

year = "2010",

month = jan,

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doi = "10.1109/ICMLC.2010.5580985",

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Moving average crossovers for short-term equity investment with L-Gem based RBFNN. / CAI, Gao Yang; NG, Wing W. Y.; CHAN, Patrick P. K. et al.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 4 2010. p. 1684-1688.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - CAI, Gao Yang

AU - NG, Wing W. Y.

AU - CHAN, Patrick P. K.

AU - FIRTH, Michael

AU - YEUNG, Daniel S.

PY - 2010/1/1

Y1 - 2010/1/1

N2 - The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy.

AB - The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy.

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KW - L-GEM

KW - Moving Average crossover

KW - RBFNN

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DO - 10.1109/ICMLC.2010.5580985

M3 - Conference paper (refereed)

VL - 4

SP - 1684

EP - 1688

BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

ER -

Moving average crossovers for short-term equity investment with L-Gem based RBFNN

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Keywords

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