Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@MastersThesis{Chia-Lan.Chang:masters,
  author =       "Chia-Lan Chang",
  title =        "Dynamic Proportion Portfolio Insurance with Genetic
                 Programming and Market Volatility Factors Analysis",
  school =       "National Central University, Jungli",
  year =         "2005",
  address =      "Taiwan",
  month =        "30 " # jun,
  keywords =     "genetic algorithms, genetic programming, DPPI, CPPI,
                 market volatility, principal component analysis, PCA",
  URL =          "http://ir.lib.ncu.edu.tw/handle/987654321/13148",
  broken =       "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?urn=92423002&filename=92423002.pdf",
  broken =       "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/view_etd_e?URN=92423002",
  size =         "45 pages",
  abstract =     "This thesis proposes a dynamic proportion portfolio
                 insurance (DPPI) strategy based on the popular constant
                 proportion portfolio insurance (CPPI) strategy. The
                 constant multiplier in CPPI is generally regarded as
                 the risk multiplier. It helps investor easily to
                 understand how to allocate the capital among risky and
                 risk-free assets and straightforward to implement. The
                 risk multiplier in CPPI is predetermined by the
                 investor's view-point and fixed to the end of
                 investment duration. However, since the market changes
                 constantly, we think that the risk multiplier should
                 change accordingly. When the market becomes volatile,
                 the predetermined large risk multiplier will lead to
                 loss of insurance and DPPI may solve this kind of
                 problem. This research identifies factors relating to
                 market volatility. These factors are built into
                 equation trees by genetic programming. We collected
                 five stocks of American companies' financial data and
                 the market information of New York Stock Exchange as
                 input data feeding genetic programming. Experimental
                 results show that our DPPI strategy is more profitable
                 than traditional CPPI strategy.

                 Because the equation trees are all different, there is
                 no method to analyse the factor contributions to the
                 results of the risk multiplier. We use principal
                 component analysis to see the effect of factors, and
                 the experimental results show that among the market
                 volatility factors, risk-free rate influences the
                 variances of risk multiplier most.",
}

Genetic Programming entries for Chia-Lan Chang

Citations