HPOSS: 一种层次化的投资组合优化堆叠策略,以减少模型集合的概括错误
Luan Carlos de Sena Monteiro Ozelim1, Dimas Betioli Ribeiro1, José Antonio Schiavon1
1Aeronautics Institute of Technology (ITA), São José dos Campos, São Paulo, Brazil.
这项研究引入了替代模型的新堆叠策略,将模型选择重新定义为金融投资组合优化问题. 这种经济堆叠方法显著提高了模型性能,并减少了所需的模型数量.
科学领域:
- 计算工程 计算工程 计算工程
- 机器学习 机器学习
- 金融建模金融建模
背景情况:
- 替代模型对于替代工程中昂贵的计算模拟至关重要.
- 选择单个替代品或合奏的传统方法通常依赖于经验或基本错误指标.
- 现有的模型堆叠技术可能无法最佳地利用单个模型的概括能力.
研究的目的:
- 引入基于概括错误的替代模型的新型堆叠策略.
- 将代理模型选择问题转化为金融投资组合优化框架.
- 通过使用金融方法论,证明堆叠代孕模型在样本之外的性能有所改善.
主要方法:
- 将概括错误重新解释为随机变量的第二个非中心时刻.
- 采用定向泡层次树 (DBHT) 聚类算法进行模型选择.
- 使用金融投资组合优化技术 (例如,等级风险平价) 来确定堆叠权重.
主要成果:
- 通过评估单个模型残留物的线性组合,开发了一种新的堆叠方法.
- DBHT算法有效地将124个训练模型过到23个,保持多样性.
- 来自等级风险平价 (HPR) 的堆叠权重显示出优异的样本外表现.
结论:
- 提出的经济堆叠策略显著改善了代用模型组合的样本外表表现.
- 将模型堆叠转化为投资组合优化提供了一个强大的权重计算框架.
- 这种新的方法为寻求提高代孕模型效率和准确性的从业者提供了一个强大的工具.
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