A Stock Pattern Recognition Algorithm Based on Neural Networks

Authors:
Xinyu Guo;Xun Liang;Xiang Li
Affiliations:
Peking University, China;Peking University, China;Peking University, China
Venue:
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Year:
2007

Citing 0
Cited 3

Predicting stock trends through technical analysis and nearest neighbor classification

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A method for automatic stock trading combining technical analysis and nearest neighbor classification

Expert Systems with Applications: An International Journal
Particle swarm intelligence tunning of fuzzy geometric protoforms for price patterns recognition and stock trading

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent studies show that stock patterns might implicate useful information for stock price forecasting. The patterns underlying the price time series can not be discovered exhaustively by the pure man power in a limited time, thus the computer algorithm for stock price pattern recognition becomes more and more popular. Currently, there are mainly two kinds of stock price pattern recognition algorithms: the algorithm based on rule-matching and the algorithm based on template-matching. However, both of the two algorithms highly require the participation of domain experts, as well as their lacks of the learning ability. To solve these problems, the paper proposes a stock price pattern recognition approach based upon the artificial neural network. The experiment shows that the neural network can effectively learn the characteristics of the patterns, and accurately recognize the patterns.