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بهینهسازی سبد سرمایه گذاری رمزارزی در شرایط عدم اطمینان با بکارگیری روش تحلیل پوششی دادهها-برنامه ریزی استوار | ||
راهبرد مدیریت مالی | ||
مقاله 5، دوره 11، شماره 3 - شماره پیاپی 42، مهر 1402، صفحه 99-126 اصل مقاله (731.12 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22051/jfm.2023.42255.2766 | ||
نویسندگان | ||
آذر غیاثی1؛ علیرضا حمیدیه* 2 | ||
1دانشجوی کارشناسی ارشد، گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران | ||
2استادیار، گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران | ||
چکیده | ||
بهینهسازی سبد سرمایهگذاری از موضوعات حیاتی حوزه مدیریت سرمایهگذاری است. نوسانات مختلف بازارهای مالی و عدمقطعیت پارامترها، بکارگیری مدلهای کلاسیک را با چالش جدی مواجه میکند. از این رو بهینهسازی مدلهای مالی در شرایط عدم اطمینان جهت انطباق با دنیای واقعی مورد توجه محققان قرار گرفته است. در پژوهش حاضر یک مدل ترکیبی بهینهسازی با بکارگیری همزمان روش تحلیل پوششی دادهها و بهینهسازی استوار به منظور ارزیابی ریسک با ورودیها وخروجیهای غیرقطعی توسعه یافته است. جامعه آماری پژوهش از درگاه کوین مارکتکپ استخراج شده است که در آن از دادههای روزآمد قیمت تعدیل شده 37 رمز ارز برتر انتخابی برای برآورد ریسک و ایجاد پرتفوی بهینه مورد استفاده قرار گرفته است. یک رویکرد دو مرحلهای برای انتخاب و بهینه سازی سبد سهام، افزایش استواری فرایند سرمایهگذاری و ارزیابی جامع سهام مبتنی بر معیارهای مالی پیشنهاد شده است. در مرحله اول، ارزیابی کارایی سهام منتخب با استفاده از روش تحلیل پوششی دادهای - برنامهریزی استوار[1] (RDEA) انجام می شود. سپس در فاز دوم، با استفاده از مدلهای میانگین نیم واریانس و میانگین انحراف مطلق استوار، میزان سرمایهگذاری در سهام واجد شرایط تعیین می شود. عملکرد رویکرد پیشنهادی در مطالعه موردی دادههای رمز ارز با عدمقطعیت فزاینده مورد ارزیابی قرار می گیرد. نتایج مقایسهای مدل های همتای استوار با دو سنجه ریسک نشان می دهد که مدل میانگین نیم واریانس عملکرد بهتری در انتخاب و بهینه سازی سبد سرمایهگذاری دارد [1]. Robust, Data Envelopment Analysis | ||
کلیدواژهها | ||
بهینهسازی سبدسهام؛ تحلیل پوششی دادهها؛ برنامه ریزی استوار؛ میانگین نیمه واریانس؛ میانگین انحراف مطلق | ||
عنوان مقاله [English] | ||
Optimizing the Cryptocurrency Investment Portfolio in Conditions of Uncertainty Using the Method of Data Envelopment Analysis - Robust Programming | ||
نویسندگان [English] | ||
Azar Ghiasi1؛ Alireza Hamidieh2 | ||
1Department of industrial engineering, Payame Noor University, Tehran, Iran | ||
2Department of industrial engineering, Payame Noor University, Tehran, Iran | ||
چکیده [English] | ||
Optimizing the investment portfolio is one of the vital issues in investment management. The various fluctuations of the financial markets and the uncertainty of the parameters make using classical models a severe challenge. Therefore, the optimization of financial models in conditions of uncertainty to adapt to the real world has been the focus of researchers. In the present research, a hybrid optimization model has been developed by applying data envelopment analysis and robust programming to assess risk with uncertain inputs and outputs. The statistical population of the research was extracted from the Coin Marketcap portal, where the updated data of the adjusted price of 37 selected top cryptocurrencies was used to estimate the risk and create an optimal portfolio. A two-step approach for selecting and optimizing the stock portfolio, increasing the stability of the investment process, and comprehensive evaluation of stocks based on financial criteria is proposed. In the first stage, the performance assessment of the selected stocks is done using the robust programming-data envelopment analysis (RDEA) method. Then, in the second phase, the investment amount in eligible stocks is determined using the half-variance average and absolute standard deviation models. The performance of the proposed approach is evaluated in a case study of cryptocurrency data with increasing uncertainty. The comparative results of robust peer models with two risk measures show that the mean semi-variance model performs better in choosing and optimizing the investment portfolio. | ||
کلیدواژهها [English] | ||
Portfolio Optimization, Data Envelopment Analysis, Robust Programming, Semi-Variance Mean, Absolute Deviation Mean | ||
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