Quantitative study on candlestick pattern for Shenzhen Stock Market

Abstract

Shenzhen Stock Market grows rapidly yet is still a young market when compared with Hong Kong, New York and London markets. Its daily turnover reaches billions US dollars. A good prediction of stock price will bring us substantial pecuniary reward. Technical analysis is a widely adopted financial prediction tool in worldwide stock markets. Candlestick pattern is one of the most efficient methods in technical analysis. However, does candlestick pattern prediction works for Shenzhen stocks? Candlestick patterns are always defined by fuzzy terms, could we have a quantitative definition of these patterns? We perform a quantitative study on these two major research problems in this paper. We study the morning star pattern in this work and the method in this paper could be extended to other patterns easily. So, we propose adopting the Radial Basis Function Neural Networks trained with Localized Generalization Error Model to predict whether or not the stock price will increase after the appearance of it. Then, we extract the patterns from the neural network to provide a quantitative definition of the morning star pattern for a particular stock. Experimental results show that our modification to the morning star pattern prediction prevents up to 69% of false prediction of the morning star pattern. We also provide a quantitative measure of the morning star patterns for two of the Shenzhen stocks.