Growing the efficient frontier on panel trees
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Article number | 104024 |
Journal / Publication | Journal of Financial Economics |
Volume | 167 |
Online published | 18 Feb 2025 |
Publication status | Online published - 18 Feb 2025 |
Link(s)
DOI | DOI |
---|---|
Attachment(s) | Documents
Publisher's Copyright Statement
|
Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85217919680&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(7983f9d6-4d82-4c7c-8486-32390ebf1394).html |
Abstract
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. © 2025 The Authors.
Research Area(s)
- Decision tree, Factors, Generative models, Interpretable AI, Test assets
Bibliographic Note
Research Unit(s) information for this publication is provided by the author(s) concerned.
Citation Format(s)
Growing the efficient frontier on panel trees. / Cong, Lin William; Feng, Guanhao; He, Jingyu et al.
In: Journal of Financial Economics, Vol. 167, 104024, 05.2025.
In: Journal of Financial Economics, Vol. 167, 104024, 05.2025.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Download Statistics
No data available