P-Trees通过经济引导的树结构提升资产定价效率，显著超越传统测试资产。
Growing the Efficient Frontier on Panel Trees
- 基于经济逻辑构建树模型，实现高维排序与可解释性结合
- 测试资产使有效前沿显著提升，阿尔法无法被基准模型解释
- 稀疏建模捕捉复杂收益特征，性能逼近过参数大模型
我们提出一类新的树模型——P-Trees，用于分析（非平衡）个体资产收益面板，通过经济引导实现高维排序并保持可解释性。在均值-方差有效框架下，P-Trees构建的测试资产显著提升了有效前沿，其阿尔法无法被基准定价模型解释。P-Trees切点组合构成可交易因子，能恢复定价核，在投资与横截面定价上表现优于主流可观测及潜因子模型。此外，P-Trees以稀疏结构捕捉资产收益复杂性，实现接近仅由过度参数化大型模型才能达到的样本外夏普比率。
原文摘要 · Abstract (English)
We introduce a new class of tree-based models, P-Trees, for analyzing (unbalanced) panel of individual asset returns, generalizing high-dimensional sorting with economic guidance and interpretability. Under the mean-variance efficient framework, P-Trees construct test assets that significantly advance the efficient frontier compared to commonly used test assets, with alphas unexplained by benchmark pricing models. P-Tree tangency portfolios also constitute traded factors, recovering the pricing kernel and outperforming popular observable and latent factor models for investments and cross-sectional pricing. Finally, P-Trees capture the complexity of asset returns with sparsity, achieving out-of-sample Sharpe ratios close to those attained only by over-parameterized large models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。