通过深度学习进行先进的投资,以优化风险调整的投资组合.
1Department of Economic Information Systems, University of Economics, Hue University, Hue, Vietnam.
PloS one
|August 19, 2025
概括
深度学习模型提高了对投资者的风险偏好多样化的投资组合优化. 长期短期记忆 (LSTM) 模型的表现优于1D-CNN,导致更高的投资绩效和风险调整后的回报.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 机器学习应用 机器学习应用
背景情况:
- 传统的投资组合优化面临着市场波动和投资者风险偏好多样化的挑战.
- 集成先进的预测模型对于提高投资组合绩效至关重要.
- 深度学习为财务预测和资产配置提供了新的方法.
研究的目的:
- 开发和评估针对投资者风险偏好的投资组合优化深度学习框架.
- 为了比较长短期记忆 (LSTM) 和一维卷积神经网络 (1D-CNN) 在投资组合构建的财务预测中的有效性.
- 在与深度学习预测集成时,评估不同投资组合框架 (预测的平均差异,风险平价投资组合,最大提款投资组合) 的性能.
主要方法:
- 利用VN-100股 (2017-2024) 的每日回报数据来训练和测试深度学习模型.
- 结合LSTM和1D-CNN预测模型与MVF,RPP和MDP投资组合框架.
- 根据2023-2024测试期间的风险调整回报和总回报评估的投资组合业绩.
主要成果:
- 与1D-CNN相比,LSTM在预测股票回报方面表现出更高的准确性和稳定性.
- 使用LSTM预测构建的投资组合通常表现优于使用1D-CNN的投资组合.
- LSTM+MVF组合产生了最佳的风险调整回报,而LSTM+MDP实现了最高的总回报.
结论:
- 深度学习模型,特别是LSTM,显著改善了投资组合优化结果.
- 将预测模型定制为特定的投资组合框架,可以根据风险概况提高投资绩效.
- 未来的研究应该探索将多样化的数据源和交易成本纳入更强大的投资组合策略.
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