کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4959865 | 1445956 | 2017 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Mean-VaR portfolio optimization: A nonparametric approach
ترجمه فارسی عنوان
بهینه سازی نمونه کارها میانگین: یک رویکرد غیر پارامتری
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
محاسبات تکاملی، بهینه سازی نمونه کارها محدود چند منظوره، ارزش در معرض خطر، شبیه سازی تاریخی غیر پارامتری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz's mean-variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 260, Issue 2, 16 July 2017, Pages 751-766
Journal: European Journal of Operational Research - Volume 260, Issue 2, 16 July 2017, Pages 751-766
نویسندگان
Khin T. Lwin, Rong Qu, Bart L. MacCarthy,