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

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.