Genetic Programming for the Investment of the Mutual Fund with Sortino Ratio and Mean Variance Model

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

@MastersThesis{Chen:mastersthesis,
  author =       "Hung-Hsin Chen",
  title =        "Genetic Programming for the Investment of the Mutual
                 Fund with Sortino Ratio and Mean Variance Model",
  school =       "Computer Science and Engineering, National Sun Yat-sen
                 University",
  year =         "2010",
  address =      "Kaohsiung, Taiwan",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, trading
                 strategy, return, Sortino ratio, risk",
  URL =          "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0824110-122030",
  URL =          "http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search-c/getfile?URN=etd-0824110-122030&filename=etd-0824110-122030.pdf",
  size =         "142 pages",
  abstract =     "In this thesis, we propose two
                 genetic-programming-based models that improve the
                 trading strategies for mutual funds. These two models
                 can help investors get returns and reduce risks. The
                 first model increases the return by selecting funds
                 with high Sortino ratios and allocates the capital
                 equally, achieving the best annualized return. The
                 second model also selects funds with high Sortino
                 ratios, but reduces the risk by allocating the capital
                 with the mean variance model.

                 Most importantly, our model uses the genetic
                 programming to generate feasible trading strategies to
                 gain return, which is suitable for the market that
                 changes anytime. To verify our models, we simulate the
                 investment for mutual funds from January 1999 to
                 December 2009 (11 years in total). The experimental
                 results show that our first model can gain return from
                 2004/1/1 to 2008/12/31, achieving the best annualized
                 return 9.11%, which is better than the annualized
                 return 6.89% of previous approaches. In addition, our
                 second model with smaller downside volatility can
                 achieve almost the same return as previous results.",
  notes =        "In english",
}

Genetic Programming entries for Hung-Hsin Chen

Citations