通过两个样本图表内核推理评估投资组合多元化. 一个关于ESG查影响的案例研究
Ragnar L Gudmundarson1,2, Gareth W Peters3
1Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh, United Kingdom.
PloS one
|April 16, 2024
概括
本研究引入了一种基于图形的新型机器学习方法,以评估环境,社会和治理 (ESG) 选规则如何影响投资组合多样化的好处. 这些发现有助于资产管理者更有效地评估ESG战略.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 资产和财富管理者需要强大的框架来评估投资组合多样化.
- 环境,社会和治理 (ESG) 投资需要根据特定的选规则选择资产.
- 在不同的选规则中比较多样化的好处是一个复杂的挑战.
研究的目的:
- 提出一种新的机器学习框架,用于比较根据各种选规则构建的投资组合的多元化效益.
- 引入一种评估ESG选规则对投资组合多样化影响的方法.
- 为资产管理者提供一个工具来评估投资组合多样化的结构差异.
主要方法:
- 将选规则表示为图形序列,其中节点是资产,边缘代表部分相关性.
- 使用一个内核两个样本测试,一个机器学习假设测试框架,来比较图表序列.
- 使用图核来分析图形数据在两个样本测试框架内.
主要成果:
- 核心的双样本图表测试有效地确定图表序列 (因此投资组合) 是否来自相同的分布.
- 在各种现实的场景中展示了框架的力量.
- 将该方法应用于S&P500数据,以展示资产管理中的实际应用.
结论:
- 拟议的图形内核双样本测试框架提供了一个统计学上合理的方法来评估ESG选对投资组合多样化的影响.
- 拒绝零假设表明ESG选对多元化有重大影响,而拒绝不拒绝则表明没有重大影响.
- 这种方法可以提高资产管理者实施ESG战略的决策能力.
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