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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3096917, 11 pages
https://doi.org/10.1155/2017/3096917
Research Article

K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

1College of Electronics and Information Engineering, Tongji University, Shanghai 200092, China
2Rabun Gap-Nacoochee School, Rabun Gap, GA 30568, USA
3Shanghai Baosight Software Co., Ltd., Shanghai 200092, China

Correspondence should be addressed to Lv Tao; moc.361@oatvlrepus

Received 23 December 2016; Revised 31 March 2017; Accepted 5 April 2017; Published 22 May 2017

Academic Editor: Anna M. Gil-Lafuente

Copyright © 2017 Lv Tao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line 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 K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that the predictive power of a pattern varies a great deal for different shapes and each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.