Estimating the Return on Investment Opportunities in Financial Markets and Establishing Optimized Portfolio by Artificial Intelligence

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@Article{Karimi:2013:IJARBSS,
  author =       "Farzad Karimi and Alireza Zare'ie and 
                 Mehdi SalemiNajafabadi",
  title =        "Estimating the Return on Investment Opportunities in
                 Financial Markets and Establishing Optimized Portfolio
                 by Artificial Intelligence",
  journal =      "International Journal of Academic Research in Business
                 and Social Sciences",
  year =         "2013",
  volume =       "3",
  number =       "7",
  pages =        "279--288",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, financial
                 markets, return, artificial neural network (ANN)",
  publisher =    "Human Resource Management Academic Research Society",
  ISSN =         "2222-6990",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:e69a23681426dba85ce87feb5cda6d9e",
  URL =          "http://hrmars.com/hrmars_papers/Estimating_the_return_on_investment_opportunities_in_financial_markets_and_establishing_optimized_portfolio_by_Artificial_Intelligence1.pdf",
  DOI =          "DOI:10.6007/IJARBSS/v3-i7/45",
  size =         "10 pages",
  abstract =     "This project is looking for increasing return on
                 investment, by presenting models based on artificial
                 intelligence. Investment in financial markets could be
                 considered in short-term (daily) and middle-term
                 (monthly) basis/ hence the daily data in Tehran Stock
                 Exchange and the rates of foreign exchange and gold
                 coins have been extracted for the period Mar. 2010 to
                 Sep. 2012 and recorded as the data into the neural
                 networks and the genetic programming model. Also the
                 monthly rate of return and risk of 20 active companies
                 of the stock exchange, and the monthly risk values of
                 foreign exchange and gold coin, as well as bank
                 deposits were used as genetic algorithms in order to
                 provide optimum investment portfolios for the
                 investors. The results obtained from executing the
                 models indicates the efficiency of both methods of
                 artificial neural network and also genetic programming
                 in the short-term financial markets predictions, but
                 artificial neural networks show a better efficiency.
                 Also the efficiency of genetic algorithm was approved
                 in improving the rate of return and risks, via
                 identifying the optimum investment portfolios.",
  notes =        "Mehdi Salemi Najafabadi",
}

Genetic Programming entries for Farzad Karimi Alireza Zare'ie Mehdi Salemi Najafabadi

Citations