A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation

Yan Jin, R. Qu, J. Atkin
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引用次数: 10

Abstract

This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming application. It is based on the standard Markowitz model with additional practical constraints such as cardinality on the number of assets and quantity of the allocated capital. Computational experiments have been conducted and analysis has demonstrated the performance and effectiveness of the proposed approach.
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约束投资组合优化的基于群体的增量学习方法
本文研究了一种利用精确和启发式方法对投资组合优化中的资产选择和资本配置进行优化的混合算法。该方法由基于自定义种群的增量学习过程和数学规划应用程序组成。它基于标准的马科维茨模型,并带有额外的实际约束,如资产数量和分配资本数量的基数。计算实验和分析证明了该方法的性能和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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