用于具有核心限制的投资组合分析的神经网络
IEEE transactions on neural networks and learning systems
|September 6, 2023
概括
本研究介绍了三个新的动态神经网络,以解决复杂的投资组合优化问题,考虑交易成本和枢纽制约因素. 与平均投资组合选择方法相比,新模型可显著降低投资成本,高达56.71%.
科学领域:
- 计算金融是指计算金融.
- 机器学习 机器学习
- 运营研究 运营研究
背景情况:
- 经典的马科维茨投资组合分析模型在现代金融中存在局限性.
- 交易成本和枢纽性约束是经典模型未解决的关键因素,特别是在高频交易中.
- 机器学习工具在解决复杂的优化问题方面表现有前途.
研究的目的:
- 为非凸的投资组合优化提出新的动态神经网络模型.
- 通过结合交易成本和枢纽性约束来解决经典模型的局限性.
- 证明机器学习在财务优化中的有效性.
主要方法:
- 开发三个新型动态神经网络,专门用于投资组合优化.
- 蓄意设计神经动力学以利用问题的结构特征.
- 对于拟议的模型,全球收的严格数学证明.
主要成果:
- 从2021年11月到2022年11月使用道斯指数 (DJI) 的真实股票市场数据进行实验验证.
- 提出的动态神经网络模型的证明有效性.
- 与平均投资组合选择相比,实现了高达56.71%的综合投资风险和回报成本的显著降低.
结论:
- 拟议的动态神经网络有效地解决了与交易成本和枢纽性约束有关的非凸式投资组合优化问题.
- 机器学习提供了一种强大的方法来克服传统金融模型的局限性.
- 新型模型在投资组合成本降低和绩效方面取得了实质性的改进.
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