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

Authors

Gao Yang CAI, Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006, Guangzhou, China
Wing W. Y. NG, Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006, Guangzhou, China
Patrick P. K. CHAN, Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006, Guangzhou, China
Michael FIRTH, Department of Finance and Insurance, Lingnan University, Hong Kong, Hong Kong
Daniel S. YEUNG, Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006, Guangzhou, China

Document Type

Conference paper

Source Publication

2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

Publication Date

1-1-2010

Volume

4

First Page

1684

Last Page

1688

Keywords

Equity market, L-GEM, Moving Average crossover, 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.

DOI

10.1109/ICMLC.2010.5580985

ISBN

9781424465262

Publisher Statement

Copyright © 2010 IEEE. Access to external full text or publisher's version may require subscription.

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

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, Qingdao, 2010 (pp.1684-1688). doi: 10.1109/ICMLC.2010.5580985