School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK.
Department of Computer Science and School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.
Neural Netw. 2022 Sep;153:399-410. doi: 10.1016/j.neunet.2022.06.023. Epub 2022 Jun 23.
This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-constrained portfolio selection problem is formulated as a mixed-integer optimization problem and reformulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is customized and applied for solving the reformulated portfolio optimization problem. In the two-timescale duplex neurodynamic approach, two recurrent neural networks operating at two timescales are employed for local searches, and their neuronal states are reinitialized upon local convergence using a particle swarm optimization rule to escape from local optima toward global ones. Experimental results on four datasets of world stock markets are elaborated to demonstrate the superior performance of the neurodynamic optimization approach to three baselines in terms of two major risk-adjusted performance criteria and portfolio returns.
本文探讨了基于神经动力学优化的投资组合选择问题。投资组合选择问题被表述为一个具有马科维茨风险收益框架中变量权重的双凸优化问题。此外，基于约束条件的投资组合选择问题被表述为混合整数优化问题，并重新表述为双凸优化问题。定制了一种双时间尺度双层神经动力学方法，并将其应用于重新表述的投资组合优化问题的求解。在双时间尺度双层神经动力学方法中，使用两个在两个时间尺度上运行的递归神经网络进行局部搜索，并且在局部收敛时使用粒子群优化规则重新初始化神经元状态，以从局部最优解逃向全局最优解。通过对世界股票市场的四个数据集的实验结果进行阐述，证明了神经动力学优化方法在两个主要的风险调整绩效标准和投资组合回报方面优于三个基线的性能。
