L-GEM based MCS aided candlestick pattern investment strategy in the Shenzhen stock market
Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Shenzhen Stock, RBFNN, Candlestick pattern, L-GEM, EMH
An integral part of China's economic reforms is the privatization of state-owned enterprises (SOEs) and listing the profitable units of the SOEs on the stock market. The two stock exchanges in Shanghai and Shenzhen were opened nearly twenty years ago. The Shenzhen stock exchange market is young and energetic. Moreover, it practices a T+1 settlement rule instead of real time trade as in Hong Kong or other exchange markets. One important research question is whether there are patterns that can be identified in stock prices that can be used to develop profitable investment strategies. If strategies can be found, then this represents a violation of the efficient market hypothesis. In this work, we propose an investment strategy by using Radial Basis Function Neural Networks (RBFNN) trained by Localized Generalization Error Model (L-GEM) and 4 stock price candlestick patterns. Every base RBFNN in the Multiple Classifier System (MCS) recognizes the occurrence of a particular candlestick pattern and the MCS combines opinions from the 4 base RBFNNs by a weighted sum to provide a final prediction. If the MCS predicts an increase for the next day, it will buy the stock and sell it within three days whenever the opening price is higher than the buy-in price or else after three days have passed. Experimental results with stocks in Shenzhen market show that our investment strategy statistically significantly outperforms a random investment, i.e. the EMH is invalid in this case.
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Paper presented at the 8th International Conference on Machine Learning and Cybernetics, Jul 12-15, 2009, Baoding, China.
ISBN of the source publication: 9781424447053
Xiao, W., Ng, W. W. Y., Firth, M., Yeung, D. S., Cai, G.-Y., Li, J.-C., & Sun, B.-B. (2009). L-GEM based MCS aided candlestick pattern investment strategy in the Shenzhen stock market. In Proceedings of the 2009 International Conference on Machine Learning and Cybernetics (pp. 243-248). New York: IEEE. doi: 10.1109/ICMLC.2009.5212499