Analysis of Stock Market Data Using SVM

• Main Authors: Yi-Chang Lan, 蘭宜昌
• Other Authors: Yi-Chia Tsai
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/99632165717725249524

碩士 === 淡江大學 === 資訊工程學系碩士在職專班 === 94 === 　　Stock market is a popular financial tool. There are many factors that affect stock prices. Computer technology has been constantly improving, and there is a growing interest in applying more sophisticated mathematical tools in studying the stock market. 　　Support Vector Machine (SVM) based on structural risk minimization theory is a modern algorithm of learning machine. Lots of scholars apply SVM to different kinds of problems due to many attractive features and promising empirical performance. 　　In this paper, we try to use SVM as an analytical tool and apply it for analyzing Taiwan stock market. Return series constructed from The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series and cutting by embedded dimension are regarded as the original data. Support vector machine is applied to construct analytical model for the stock index fluctuation simulation. 　　Results reveal that the fluctuation of TAIEX is random walk in general. In amount of training history data, it shows that long training data period is not strikingly helpful to predict the trend of the stock index, but using medium-term or short-term training data is good for catching the future stock index''s tendency.