Applying Data Mining to Analyze Sequential Patterns in the stock

• Main Authors: Yu-Ying Shih, 史育英
• Other Authors: 李鴻璋
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/86398410544973183876

碩士 === 淡江大學 === 資訊管理學系碩士班 === 94 === Abstract: Our research applies data mining technique to analyze stock and forward market in Taiwan. We build the Time Sequential Pattern by constructing the historical data of Bar-chart (or K-line) of the stock market futures to assist investors` decisions making. The performance of the stock market is the collection of all individuals` decision, and there are some timing relations between their investments. The phenomenon of sequential investment can be studied or explained by using Data Mining technique, especially the Sequential Pattern Analysis. The Sequential Pattern Analysis is used to analyze the sequential relation between two events. The technique has advanced greatly in recent years, so we hope that it would be a new research way by using this technique to analyze the behavior of the stock market. The object of this research is to generalize the characteristic of the time sequential pattern of investments in the stock market futures in Taiwan. Based on the historical bar-chart transaction data in the stock market, we mine for the sequential patterns of Taiwan stock exchange capitalization weighted stock index futures; use Data mining technique to discover some bar-chart combination sequence which may construct the behavior model to provide the investors with useful information and to assist them doing the correct decisions making .