在行业集团层面上进行增预测和投资组合构建:使用梯度增强决策树的因果机器学习方法
Gil Cohen1, Avishay Aiche1, Ron Eichel1
1Management Department, Western Galilee College, Acre 2412101, Israel.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
信息理论复杂度指标,如香农,显著提高基于机器学习的行业集团回报预测和投资组合构建. 这些增强模型产生了更有利可图和更稳定的投资策略.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 信息理论 信息理论
背景情况:
- 传统的金融模式经常与复杂的市场动态作斗争.
- 机器学习为财务预测提供先进的模式识别功能.
- 信息理论措施量化系统的复杂性和可预测性.
研究的目的:
- 调查信息理论复杂度指标在增强基于机器学习的行业组回报预测方面的有效性.
- 评估这些措施对投资组合建设战略的影响.
- 评估增强模型的经济性能和可解释性.
主要方法:
- 在过去三十年中,利用了25个美国GICS行业集团的每日回报数据.
- 增强梯度增强决策树模型与香农和模糊.
- 在每周,每月和每季度的估计中采用严格因果的滚动窗设计.
- 制定了最大利和最小风险的分配策略.
主要成果:
- 使用香农的每周最大利模型实现了超过30,000%的累积回报,显著超过基线和模糊模型.
- 和模糊在月/季度视野上提供了较小,强大的改进,降低了波动性并增强了下行保护.
- 行业配置稳定且可解释,利策略有利于周期性/增长行业,风险策略有利于防御性行业.
结论:
- 基于的复杂度指标显然提高了经济绩效和财务预测中的可解释性.
- 这些措施产生了更有利可图,稳定和透明的行业旋转策略.
- 这些发现支持将信息理论概念整合到机器学习框架中,以加强投资组合管理.
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