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    • Advances in Mathematical Finance and Applications
    • Volume 5, Issue 1
    • مشاهده مورد
    •   صفحهٔ اصلی
    • نشریات انگلیسی
    • Advances in Mathematical Finance and Applications
    • Volume 5, Issue 1
    • مشاهده مورد
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    Comparison of Portfolio Optimization for Investors at Different Levels of Investors' Risk Aversion in Tehran Stock Exchange with Meta-Heuristic Algorithms

    (ندگان)پدیدآور
    Fotros, Mohammad HassanMiri, IdrisMiri, Ayob
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    نوع مدرک
    Text
    Research Paper
    زبان مدرک
    English
    نمایش کامل رکورد
    چکیده
    The gaining returns in line with risks is always a major concern for market play-ers. This study compared the selection of stock portfolios based on the strategy of buying and retaining winning stocks and the purchase strategy based on the level of investment risks. In this study, the two-step optimization algorithms NSGA-II and SPEA-II were used to optimize the stock portfolios. In order to determine the winning algorithm, the performance indexes, Set coverage and the Mean Ideal Distance were used. Finally, the active shares of 50 Tehran Stock Exchange com-panies were analysed (2007-2016). The results indicate that the SPEA-II algo-rithm can perform optimization and achieve a better performance than the NSGA-II. This algorithm could achieve better outcomes than the winning strategy during the selection period based on the risk-taking strategies in different months
    کلید واژگان
    Meta-Heuristic Algorithms
    Trading Strategies
    Performance Criteria
    Risk Management

    شماره نشریه
    1
    تاریخ نشر
    2020-01-01
    1398-10-11
    ناشر
    IA University of Arak
    سازمان پدید آورنده
    Faculty of Economic and Social Sciences,Hamedan university, Iran
    Faculty of Management and Accounting, Orumieh,Iran
    Faculty of Economics and Social Sciences,Hamedan university, Iran

    شاپا
    2538-5569
    2645-4610
    URI
    https://dx.doi.org/10.22034/amfa.2019.1870129.1235
    http://amfa.iau-arak.ac.ir/article_666241.html
    https://iranjournals.nlai.ir/handle/123456789/423017

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