极大极小组合优化的双时间尺度神经动力学方法

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI:10.1109/ICIST52614.2021.9440640

Man-Fai Leung, Jun Wang

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引用次数: 2

摘要

本文研究了基于双时间尺度神经动力学优化的资产配置问题。将经典均值-方差框架下的投资组合优化问题重新表述为极小极大投资组合选择问题，并提出了一种双时间尺度神经动力学方法来求解该问题。神经动力学方法结合了一个在两个不同时间尺度上运行的递归神经网络(RNN)。计算结果表明了所提出的资产配置方法的有效性和性能。

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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A Two-Timescale Neurodynamic Approach to Minimax Portfolio Optimization

This paper is concerned with asset allocation based on two-timescale neurodynamic optimization. The portfolio optimization in classical mean-variance framework is reformulated as a minimax portfolio selection problem and a two-timescale neurodynamic approach is developed to solve the problem. The neurodynamic approach incorporates a recurrent neural network (RNN) operating on two different timescales. Computational results show the efficacy and performance of the developed approach to asset allocation.

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2021 11th International Conference on Information Science and Technology (ICIST)

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