Further Analysis of Candlestick Patterns’ Predictive Power

Abstract

Since the candlestick patterns were mined, there is a contentious dispute on whether the candlestick patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering and pattern knowledge mining to research the predictive power of candlestick patterns. In addition, we propose the similarity match model and nearest neighbor-clustering algorithm to solve the problem of similarity match and clustering of candlestick series, respectively. The experiment includes testing the predictive power of the Morning Star pattern and Evening Star pattern with the testing dataset of the candlestick series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1) There have some spurious patterns in the existing candlestick patterns. However, after further classification of a spurious pattern based on its shape feature, those patterns with special shapes still have predictive power. (2) Some patterns do have the predictive power. (3) As there is no precise mathematical definition to describe the existing patterns’ predictive power, it is essential to give the mathematical formula for improving the candlestick patterns’ prediction performance.