A Stock Trading Decision Support System Based on Neural Networks and Quantified Patterns

• Main Authors: Yao-Jen Chan, 詹耀仁
• Other Authors: Chin-Laung Lei
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/22444795111820086771

碩士 === 國立臺灣大學 === 電機工程學研究所 === 92 === Since the technique of artificial intelligence has been getting maturer in recent years, many researchers have been trying to build stock trading decision support systems based on neural networks. However, the influence of stock patterns has not been considered in previous researches and we know that is an important part in the filed of technical analysis. Thus, in this research, we propose a new method which could quantify head and shoulders patterns and we form the inputs of neural networks with the quantified results and eighteen types of technical indicators. This could let our system has the ability to consider the influences of stock patterns and technical indicators simultaneously. The sample data in this research are six quoted companies and two indices in Taiwan stock market. Experiment period is from 1999 to 2003. The average accuracy is greater than 60%. If we focus on the period which head and shoulders patterns appear, the accuracy is greater than 75%. Thus, we conclude that it is effective to predict stock markets by quantified patterns. We believe that the accuracy could be further improved by introducing more quantified patterns.