A Stock Investment Model Using Decision Tree with Technical and Counter Indexes

• Main Authors: Ching-Chih Liu, 劉敬之
• Other Authors: Wei-Ping Lee
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/48247611267818661259

碩士 === 中原大學 === 資訊管理研究所 === 99 === The trend of stock market is a subject to be concerned by most people. Many investors expect to predict the stock price, which is affected by lot of factors, such as human interference, political issue, macro-economic or other unknown factors. Therefore the accuracy of some related models cannot be satisfied by people who concern. Some methods applied to establish the stock price forecasting model by scholars, including Genetic Algorithms(GA), Artificial Neural Network(ANN), decision tree algorithms, SVM(Support Vector Machine) and so on. However, data mining technology has been used to forecast for a long time. Recently, decision tree algorithms become a popular one in the study of the stock price prediction and the study has shown good results. This research applies decision tree algorithm to forecast the stock price of listed and OTC market in Taiwan. The target of research is to study TSEC Taiwan 50 sectors during the period from 2nd May 2002 to 31st March 2010. Experimental results show that the ROI (return on investment) of the stock price index prediction model established by combining the decision tree algorithm with the technical and counter indexes variables of stock can get a good result comparing to the ranking of the return on ROI of Trust Fund in the same period; and also ahead to the increase of market index during the same time period.