Application of Stock Investment Strategy Based on Intelligent Multi-layer Selective Model and Cycle Trend Analysis

• Main Authors: Ying-Chun Lu, 盧盈君
• Other Authors: Ting-Cheng Chang
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/78442625211582840095

碩士 === 嶺東科技大學 === 經營管理研究所 === 96 === In this research, for Intelligent Multi-layer selective System and business cycle in stock investment, we attempt to use Data-mining technique to set up a multi-layer selective model for providing recommendations on picking stocks. Our investment model mainly included five steps. First, by Neuro-Network -sensitivity analyzing all financial ratios of K-means analyze those related to financial attribute quality were screened out and entering into decision tree training. Second, well performed companies we required were filtered out by using the standard share selection process from decision tree and the concepts of investment master, Warren Edward Buffett and Benjamin Graham. Third, determining share selection sequence by grey relational analyze. Fourth, a cycle trend analysis, were obtained as a return on its investment portfolio and the initial basis for comparison. Finally, three analysis by comparison, that the most effective investment decision-making. In our study, we regarded all listed companies (except financial industry) in Taiwan market as our selection targets and we chose January 2005 to September 2007 as our proving period and conducted 9 practical operations in this period. This research got the best result form cycle trend analysis derived that their annual average rate of return was 42.81%。