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在投资组合优化中基于风险的资产配置的机器学习方法
Sanjay Agal1, Krishna Raulji2, Niyati Dhirubhai Odedra3
1Department of Artificial Intelligence and Data Science, Faculty of Engineering and Technology, Parul University, Vadodara, Gujarat, India. sanjay.agal32685@paruluniversity.ac.in.
Scientific reports
|November 26, 2025
概括
本研究引入了一种新的机器学习框架,用于基于风险的动态资产配置,其性能优于传统和深度学习方法. 适应性策略提高了风险调整后的回报,并减少了波动性市场的提款.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 计算金融是指计算金融.
背景情况:
- 传统的投资组合优化方法由于静态风险预算和依赖历史数据而面临限制.
- 现有的方法很难动态地适应不断变化的市场条件和实时指标.
研究的目的:
- 引入一种新的机器学习框架,用于基于风险的动态资产配置.
- 通过启用适应性风险约束来解决传统投资组合优化的局限性.
- 实现优越的风险调整性能,计算效率和模型可解释性.
主要方法:
- 整合长期短期记忆 (LSTM) 网络用于波动性预测.
- 实施可差异化的风险预算层和调节机制.
- 投资组合权重的端到端培训,具有适应性风险约束和稀疏注意力机制.
主要成果:
- 达到了1.38的夏普比率,超过了传统的风险平价 (55%) 和深度学习 (23%) 策略.
- 演示了计算效率,在不到25毫秒的时间内处理50个资产组合.
- 在压力时期减少了41%的最大提款,并在COVID-19危机期间表现出积极的风险管理.
结论:
- 这种新的框架为投资组合优化建立了一个新的范式,将金融理论和实践联系起来.
- 该模型能够以高效和可解释的方式在复杂的市场上进行导航,这表明了机构的准备.
- 为投资组合经理提供了一个强大的工具,用于适应性,风险意识的投资策略.
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