| Summary: | 碩士 === 淡江大學 === 資訊管理學系碩士班 === 103 === In the rapid changes stock market, investors want to get profit that they must carefully choose stocks, buy or sell the stocks at the appropriate time and with the best capital allocation strategy. How to make the right investment strategy is the subject of investors. There are many ways to assist the development of decision for stock investment strategy. But when investors consider too many investment criteria, it is easy to lose objectivity and proper analysis to determine the importance of the evaluation criteria. Especially when investors take into account new guidelines when to make investment decision. The traditional decision making methods just can only re-evaluate the decision problem and cannot be accumulated the importance of criteria before adding new guidelines. It is difficult to make decision. Furthermore, investors often miss the good opportunity to do stock transaction and do not know how to allocate investment funds.
For this reason, this study integrated co-evolutionary genetic algorithm with Markov decision process to help investors develop a dynamic stock investment strategies system. The genetic algorithms have the ability of parallel search in breadth space. The co-evolutionary mechanism has simulated human thought patterns. These making the process of the genetic algorithm can implement with adjustment in the dynamic changes environments. And by Markov decision process, the decision system can inform investors when must to adjust the portfolio and to identify the appropriate stock timing, the stock selection and the capital allocation. The complete stock investment strategy allows investors to obtain excess returns when invest in the stock market.
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