Created by W.Langdon from gp-bibliography.bib Revision:1.4685
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.",
Genetic Programming entries for Jia-Li Hou