A Novel Approach to Dynamic Portfolio Trading System Using Multitree Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Somaye Mousavi and Akbar Esfahanipour and 
                 Mohammad Hossein Fazel Zarandi",
  title =        "A Novel Approach to Dynamic Portfolio Trading System
                 Using Multitree Genetic Programming",
  journal =      "Knowledge-Based Systems",
  year =         "2014",
  volume =       "66",
  pages =        "68--81",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Dynamic
                 Portfolio Trading System, Technical indices, Trading
  ISSN =         "0950-7051",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0950705114001403",
  DOI =          "doi:10.1016/j.knosys.2014.04.018",
  size =         "14 pages",
  abstract =     "Dynamic portfolio trading system is used to allocate
                 one's capital to a number of securities through time in
                 a way to maximise the portfolio return and to minimise
                 the portfolio risk. Genetic programming (GP) as an
                 artificial intelligence technique has been used
                 successfully in the financial field, especially for the
                 forecasting tasks in the financial markets. In this
                 paper, GP is used to develop a dynamic portfolio
                 trading system to capture dynamics of stock market
                 prices through time. The proposed approach takes an
                 integrated view on multiple stocks when the GP evolves
                 and generates a rule base for dynamic portfolio trading
                 based on the technical indices. In the present
                 research, a multitree GP forest has been developed to
                 extend the GP structure to extract multiple trading
                 rules from historical data. Furthermore, the consequent
                 part of each trading rule includes a function rather
                 than a constant value. Besides, the transaction cost of
                 trading which plays an important role in the
                 profitability of a dynamic portfolio trading system is
                 taken into account. This model was used to develop
                 dynamic portfolio trading systems. The profitability of
                 the model was examined for both the emerging and the
                 mature markets. The numerical results show that the
                 proposed model significantly outperforms other
                 traditional models of dynamic and static portfolio
                 selection in terms of the portfolio return and risk
                 adjusted return.",

Genetic Programming entries for Somayeh Mousavi Akbar Esfahanipour Mohammad Hossein Fazel Zarandi