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通过引导和机器学习方法基于场景的投资组合优化:从德黑兰股票市场发展理论和经验证据
Morteza Amini1, Sajedeh Javadi1, Majid Soleimani-Damaneh1
1School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
PloS one
|February 19, 2026
概括
本研究介绍了一种混合模型,将机器学习,引导和场景优化相结合,以实现更好的投资组合管理. 它通过准确预测回报和模拟不确定性,为传统方法提供了强大的替代方案.
科学领域:
- 量化金融 量化金融
- 机器学习应用 机器学习应用
- 投资组合优化 投资组合优化
背景情况:
- 预测未来的资产回报率和量化它们的不确定性是投资组合优化的关键但困难的挑战.
- 传统方法通常依赖于强大的分布假设或预定义的不确定性集,限制了它们的适用性.
研究的目的:
- 为投资组合优化提出一种新的混合方法,该方法集成了机器学习,引导和场景优化.
- 提高回报预测的准确性和回报不确定性的建模.
主要方法:
- 使用机器学习来预测回报率,并启动模拟不确定性.
- 从引导样本生成多个返回轨迹,将每一个视为一个场景.
- 应用基于场景的平均偏差优化来选择不确定性下的投资组合.
主要成果:
- 在德黑兰证券市场数据上的实证验证表明,与传统技术相比,投资组合表现优越.
- 混合模型产生了与实际市场价格密切一致的最佳投资组合.
- 理论分析证实了结果的场景优化问题是一个凸的程序,产生高效的解决方案.
结论:
- 引导,机器学习和场景优化的结合为经典的投资组合优化方法提供了引人注目的灵活替代方案.
- 这种方法避免了限制性的分布假设和预定义的不确定性集,提供了更大的稳定性.
- 该框架在计算上是可处理的,可以适应各种机器学习和引导技术,适合大规模应用.
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