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

1 Citation (Scopus)


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
Number of pages5
Publication statusPublished - 1 Jan 2010


  • Equity market
  • L-GEM
  • Moving Average crossover


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