کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4944319 1437983 2017 54 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hybrid bi-objective portfolio optimization with pre-selection strategy
ترجمه فارسی عنوان
بهینه سازی نمونه کارها با دوبعدی ترکیبی با استراتژی پیش انتخاب
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Classical Markowitz mean-variance model is widely used for portfolio assets selection and allocation, which aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio variance. Many numerical approaches and metaheuristic algorithms have been proposed to effectively solve this portfolio optimization problem under an ideal condition. However, introducing various realistic constraints inadvertently leads to a non-convex search space, which has hindered the application of many classic, exact algorithms such as quadratic programming (QP). The increasing size of available assets and complex constraints has made the effectiveness of metaheuristic algorithms deteriorated. This paper proposes a hybrid bi-objective algorithm combining with the respective advantages of local search algorithm, evolutionary algorithm and QP with a pre-selection strategy. The algorithm first down select the assets that have greater contribution to the Pareto frontier by applying the pre-selection strategy. Then local search and evolutionary algorithm combined with QP are employed to fully exploit the useful assets combination modes to lead the search process toward the frontier direction quickly. The experimental study demonstrates that the proposed hybrid approach can obtain faster and better convergence compared with eight state-of the-art multi-objective evolutionary algorithms. The results also show that the proposed method with the pre-selection strategy always displays a closer proximity to the Pareto frontier compared with k-means strategy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 417, November 2017, Pages 401-419
نویسندگان
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