在高频交易中用于投资组合分析的神经网络
IEEE transactions on neural networks and learning systems
|September 13, 2023
概括
这项研究引入了新型的神经网络,用于高频交易组合选择的软max等级. 与传统的解决方案相比,新方法提供了更准确和更具成本效益的解决方案.
科学领域:
- 计算金融是指计算金融.
- 金融中的机器学习
背景情况:
- 高频交易 (HFT) 对传统的投资组合选择提出了独特的挑战.
- 在现代金融市场中,对及时准确的投资组合解决方案的需求至关重要.
研究的目的:
- 提出用于高频交易环境中的投资组合选择的新型神经网络模型.
- 首次使用软max技术解决投资组合优化中的方程约束.
主要方法:
- 开发新型的神经网络架构,采用软max等级.
- 理论分析以证明全球趋同到最佳的投资组合选择解决方案.
- 使用真实股票市场数据进行实证验证.
主要成果:
- 拟议的神经网络模型在投资组合选择中表现出有效性.
- 与MATLAB专用解决方案相比,实现了5.50%和5.47%的成本降低.
- 已验证的开发方法的全球收特性.
结论:
- 具有软max等级的新型神经网络为高频交易组合选择提供了优越的策略.
- 这种方法可以显著降低成本,并提高比现有方法的准确性.
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