Constructing Static and Dynamic Investment Strategy Portfolios by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{hou:thesis,
  author =       "Jia-Li Hou",
  title =        "Constructing Static and Dynamic Investment Strategy
                 Portfolios by Genetic Programming",
  school =       "Information Management, National Central University",
  year =         "2008",
  type =         "Doctoral Dissertation",
  address =      "Taiwan",
  month =        "8 " # jan,
  keywords =     "genetic algorithms, genetic programming, Portfolio,
                 Artificial Intelligence, Capital Allocation, Investment
                 Strategy, Linear Capital Allocation, Non-Linear Capital
                 Allocation",
  URL =          "http://ir.lib.ncu.edu.tw/handle/987654321/13036",
  URL =          "http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=90443001",
  broken =       "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search/view_etd?URN=90443001",
  size =         "117 pages",
  abstract =     "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.",
  notes =        "Language zh-TW.Big5 Chinese. Locked for two years. Feb
                 2013 available.",
}

Genetic Programming entries for Jia-Li Hou

Citations