دانلود مقاله ISI انگلیسی شماره 78483
ترجمه فارسی عنوان مقاله

# مدلهای برنامه ریزی خطی بر اساس نسبت امگا برای پیگیری مشکل شاخص پیشرفته

عنوان انگلیسی
Linear programming models based on Omega ratio for the Enhanced Index Tracking Problem
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
78483 2016 19 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : European Journal of Operational Research, Volume 251, Issue 3, 16 June 2016, Pages 938–956

ترجمه کلمات کلیدی
ردیابی شاخص افزایش ؛ نسبت امگا؛ بهینه سازی سبد سهام - برنامه ریزی خطی؛ برنامه ریزی خطی عدد صحیح مختلط
کلمات کلیدی انگلیسی
Enhanced index tracking; Omega ratio; Portfolio optimization; Linear programming; Mixed integer linear programming
پیش نمایش مقاله

#### چکیده انگلیسی

Modern performance measures differ from the classical ones since they assess the performance against a benchmark and usually account for asymmetry in return distributions. The Omega ratio is one of these measures. Until recently, limited research has addressed the optimization of the Omega ratio since it has been thought to be computationally intractable. The Enhanced Index Tracking Problem (EITP) is the problem of selecting a portfolio of securities able to outperform a market index while bearing a limited additional risk. In this paper, we propose two novel mathematical formulations for the EITP based on the Omega ratio. The first formulation applies a standard definition of the Omega ratio where it is computed with respect to a given value, whereas the second formulation considers the Omega ratio with respect to a random target. We show how each formulation, nonlinear in nature, can be transformed into a Linear Programming model. We further extend the models to include real features, such as a cardinality constraint and buy-in thresholds on the investments, obtaining Mixed Integer Linear Programming problems. Computational results conducted on a large set of benchmark instances show that the portfolios selected by the model assuming a standard definition of the Omega ratio are consistently outperformed, in terms of out-of-sample performance, by those obtained solving the model that considers a random target. Furthermore, in most of the instances the portfolios optimized with the latter model mimic very closely the behavior of the benchmark over the out-of-sample period, while yielding, sometimes, significantly larger returns.