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