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by David Lai (Author)
This study investigates the performance of the weight optimization by comparing the performance of the portfolios of fund of funds (FoF) constructed by the Markowitz Mean-Variance (MV) model or Genetic Algorithm (GA) to that of S&P 500 and that of equal weight portfolio of Mutual funds. The chosen target funds are denominated in U.S. dollar or euros, and are chosen from the European market, United European market, Emerging market, Pacific market, South Asia market, Asia Pacific Zone market, American market, and Global market. The study period started on February 1, 1998 and ended on December 1, 2006. The Markowitz Mean-Variance model is a famous investment theory in portfolio selection problems. But Markowitz Mean-Variance model requires the assumption that the securities must follow the normal distribution. On the contrary, Genetic Algorithm is a methodology with artificial intelligence that is free of the assumption of normal distribution, and it can also be applied to the portfolio selection and optimization problems. In this thesis, we test whether the Genetic Algorithm can beat the traditional Markowitz Mean-Variance model or not.
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by David Lai (Author)
This study investigates the performance of the weight optimization by comparing the performance of the portfolios of fund of funds (FoF) constructed by the Markowitz Mean-Variance (MV) model or Genetic Algorithm (GA) to that of S&P 500 and that of equal weight portfolio of Mutual funds. The chosen target funds are denominated in U.S. dollar or euros, and are chosen from the European market, United European market, Emerging market, Pacific market, South Asia market, Asia Pacific Zone market, American market, and Global market. The study period started on February 1, 1998 and ended on December 1, 2006. The Markowitz Mean-Variance model is a famous investment theory in portfolio selection problems. But Markowitz Mean-Variance model requires the assumption that the securities must follow the normal distribution. On the contrary, Genetic Algorithm is a methodology with artificial intelligence that is free of the assumption of normal distribution, and it can also be applied to the portfolio selection and optimization problems. In this thesis, we test whether the Genetic Algorithm can beat the traditional Markowitz Mean-Variance model or not.
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