Applying Genetic Algorithm and Grey Decision in Stock Selective Models

• Main Authors: Dan-Lon Lin, 林典蓉
• Other Authors: Tsung-Nan Chou
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/83416424227450741790

碩士 === 朝陽科技大學 === 財務金融系碩士班 === 96 === In the time of inflation, our wealth will be decreased year after year if the bank deposit is the major investment strategy. Therefore, the study of the Investing and Financial Management are crucial to the future of our wealth management. In recent years, people have proposed a general idea that both wealth management and investing become parts of our daily lives. Most public investors select stocks for investment under the guidance of the bank consultants or brokers. However, these financial institution staff might rely on the historical data or subjective judgment to predict trend and result in high risks. For the purpose of reducing the investment risk, this study tried to construct a sophisticated stock selecting model which is capable of providing a rational investment approach and reducing the risks from human subjective judgment. This study used financial ratios as criteria to evaluate the electronic industry company listed in Taiwan Stock Exchange (TSEC). Three different models applying the Genetic Algorithm and Grey Decision Making are built to select promising stocks. Among three models, the integrated model combining Genetic Algorithm and Grey Decision Making demonstrates higher evaluation performance. In addition, the experiment results find that Accounts Receivable, Turnover Ratio, Current Ratio, Return on Assets Before Interest and After Tax (ROA), Market-To-Book Equity Ratios and Earnings Per Share (EPS) are important factors which provide higher weighting to the serious stock-selective models.