An Application of XCS Classifier System on Treasury Yield Rate Forecasting with Preliminary Knowledge Rule Base

• Main Authors: Tyng-Jiun Kuo, 郭庭君
• Other Authors: An-Pin Chen
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/08035173185424473785

碩士 === 國立交通大學 === 資訊管理研究所 === 93 === The stock market is continuously taken as the development key point in Taiwan. Moreover, stock derivatives are to weed through the old to bring forth the new, therefore the trading market is getting hot. By the government gradually opened each kind of interest rate derivatives in recent years which makes the bond market transaction growth to be rapid, therefore how to correctly forecast treasury yield rate is getting more and more important. But in the tradition, the interest rate forecasting mostly are using regression models or other statistics methods; few of them forecast interest rate by using artificial intelligence. Moreover the application of the artificial intelligence for financial forecast also mostly stresses on the stock and futures markets. It’s very few to apply on bond market. Therefore this research attempts to apply eXtended Classification System (XCS) to construct a treasury yield rate forecasting model which can adopt with the dynamic and self-learning environment, also innovatively uses XCS to help decision making on the bond investment strategy. This research provides the new research mechanism for the bond market related topics and gives successors a research reference and a research direction in the treasury yield rate forecasting.