Constructing Static and Dynamic Investment Strategy Portfolios by Genetic Programming

• Main Authors: Jia-Li Hou, 侯佳利
• Other Authors: Jiah-Shing Chen
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/43192787780181937713

博士 === 國立中央大學 === 資訊管理研究所 === 96 === The study comes up with a framework of portfolio, dividing investment issues into four quadrants based on two dimensions: capital allocation frequency and allocation approach. In allocation approach, there are linear and non-linear. In capital allocation frequency selection approach, there are static and dynamic allocation approaches. In the framework, static allocation, based on the assumption that if investment duration is identical, is to complete capital allocation selection at the beginning of duration; dynamic allocation, based on the assumption that each investment period is different, is to allocate capital when needed. In traditional financial area, investment portfolios are linear and static investment issue, which is take all investment duration are the same, and to buy in at the beginning of period, therefore, invest decision is to directly allocate capital on multiple investment objectives by static allocation, in order to gain the greatest profit or minimize the risk probability.[Huang, 2008; Li, 2008] And reconsidering investment decision for next duration at the end of duration. The framework of the research takes “investment strategy” as investment objectives. The research is to make pairs of investment objectives and transaction rules, and allocate capital on investment strategies rather on investment objectives directly. And the research comes up a solution of non-linear capital allocation approach, including planning a capital allocation tree by soft computing and genetic algorithms, calculating every capital weight on every investment strategies, and providing static and dynamic capital frequency strategies. The research takes 30 stocks in Dow Jones Industrial Average of U.S. stock market、textbook、academic researches and 9 technical indexes which are commonly used in investment markets to comprise 81 simple transaction rules and constitute 2,430 investment strategies which are planned by genetic algorithms. And experiment test of research is based on 1999 to 2006 stock market data, the outcome of experiment shows that static and dynamic and non-linear portfolios gains greater profit and smaller probability of risk, comparing to buy-in strategy.