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

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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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 languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages1684-1688
Number of pages5
Volume4
DOIs
Publication statusPublished - 1 Jan 2010

Fingerprint

Moving average
Radial basis function
Equity
Neural networks
Crossover
Factors
Investment strategy
Prediction
Shenzhen
Investors
Technical analysis
Stock exchange
Financial markets
Experiment
Profit
Stock prices
Emerging markets

Keywords

  • Equity market
  • L-GEM
  • Moving Average crossover
  • RBFNN

Cite this

CAI, G. Y., NG, W. W. Y., CHAN, P. P. K., FIRTH, M., & YEUNG, D. S. (2010). Moving average crossovers for short-term equity investment with L-Gem based RBFNN. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (Vol. 4, pp. 1684-1688) https://doi.org/10.1109/ICMLC.2010.5580985
CAI, Gao Yang ; NG, Wing W. Y. ; CHAN, Patrick P. K. ; FIRTH, Michael ; YEUNG, Daniel S. / Moving average crossovers for short-term equity investment with L-Gem based RBFNN. 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 4 2010. pp. 1684-1688
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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.",
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CAI, GY, NG, WWY, CHAN, PPK, FIRTH, M & YEUNG, DS 2010, Moving average crossovers for short-term equity investment with L-Gem based RBFNN. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. vol. 4, pp. 1684-1688. https://doi.org/10.1109/ICMLC.2010.5580985

Moving average crossovers for short-term equity investment with L-Gem based RBFNN. / CAI, Gao Yang; NG, Wing W. Y.; CHAN, Patrick P. K.; FIRTH, Michael; YEUNG, Daniel S.

2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 4 2010. p. 1684-1688.

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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CAI GY, NG WWY, CHAN PPK, FIRTH M, YEUNG DS. Moving average crossovers for short-term equity investment with L-Gem based RBFNN. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 4. 2010. p. 1684-1688 https://doi.org/10.1109/ICMLC.2010.5580985