# Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

arXiv: https://arxiv.org/abs/2507.16433 · HTML: https://mlsanity.com/paper/2507.16433/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 69 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Understanding and Improving Information Transfer in Multi-Task Learning](https://mlsanity.com/paper/2005.00944/) (2020)
- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [Adaptive and Robust Multi-Task Learning](https://mlsanity.com/paper/2202.05250/) (2022)
- [Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances](https://mlsanity.com/paper/1802.04885/) (2018)
- [Robust angle-based transfer learning in high dimensions](https://mlsanity.com/paper/2210.12759/) (2022)
- [Integrative Factor Regression and Its Inference for Multimodal Data Analysis](https://mlsanity.com/paper/1911.04056/) (2019)
- [Multitask Learning and Bandits via Robust Statistics](https://mlsanity.com/paper/2112.14233/) (2021)
- [New Perspectives on k-Support and Cluster Norms](https://mlsanity.com/paper/1512.08204/) (2015)
- [Transfer Learning for High-dimensional Quantile Regression via Convolution Smoothing](https://mlsanity.com/paper/2212.00428/) (2022)
- [Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity](https://mlsanity.com/paper/2306.06291/) (2023)
- [Multi-Task Learning with Summary Statistics](https://mlsanity.com/paper/2307.02388/) (2023)
- [Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models](https://mlsanity.com/paper/2002.01800/) (2020)
- [Transfer Learning for Portfolio Optimization](https://mlsanity.com/paper/2307.13546/) (2023)

## Abstract (arXiv)

Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of factor modeling, we propose a novel data-adaptive multi-task learning methodology that quantifies and learns the relatedness among the principal temporal subspaces (spanned by factors) across multiple sectors under study. This approach not only improves the simultaneous estimation of multiple factor models but also enhances multi-sector portfolio optimization, which heavily depends on the accurate recovery of these factor models. Additionally, a novel and easy-to-implement algorithm, termed projection-penalized principal component analysis, is developed to accomplish the multi-task learning procedure. Diverse simulation designs and practical application on daily return data from Russell 3000 index demonstrate the advantages of multi-task learning methodology.