- author = "Akbar Esfahanipour and Maryam Tayari",
- title = "A genetic network programming model for portfolio optimization by generating risk-adjusted trading rules",
- booktitle = "10th International Industrial Engineering Conference (IIEC2014)",
- year = "2014",
- address = "Tehran, Iran",
- month = "27-28 " # jan,
- keywords = "genetic algorithms, genetic programming, genetic network programming, portfolio optimization, technical trading rules, risk-adjusted measures, Tehran Stock Exchange (TSE)",
-
URL = "
https://www.researchgate.net/profile/Akbar-Esfahanipour/publication/A-genetic-network-programming-model-for-portfolio-optimization-by-generating-risk-adjusted-trading-rules.pdf",
-
long_url = "https://www.researchgate.net/profile/Akbar-Esfahanipour/publication/284794164_A_genetic_network_programming_model_for_portfolio_optimization_by_generating_risk-adjusted_trading_rules/links/5688beb208ae19758399acc3/A-genetic-network-programming-model-for-portfolio-optimization-by-generating-risk-adjusted-trading-rules.pdf",
- size = "7 pages",
- abstract = "Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties: 1) we use conditional Sharp ratio as a risk-adjusted measure for generating trading rules, 2) in our GNP model, binary trading rules have been extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. We applied our GNP model on ten stocks from Tehran Stock Exchange (TSE). The numerical results show that our proposed model with three signals outperformed the previous model with two signals of buy and sell in terms of excess return and excess risk adjusted return.",
-
notes = "IIEC2014-GNPPortfoliooptimization.pdf
https://www.en.symposia.ir/IIEC10
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran",
