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Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

arXiv 2507.16433 ↗First posted 22 Jul 2025stat.ME cs.LGcited by 0

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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.

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References · 15

  1. 2020Understanding and Improving Information Transfer in Multi-Task Learningcited by 114
  2. 2020Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimalitycited by 90
  3. 2021Transfer Learning under High-dimensional Generalized Linear Modelscited by 70
  4. 2020Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Controlcited by 38
  5. 2022Adaptive and Robust Multi-Task Learningcited by 34
  6. 2018Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distancescited by 25
  7. 2022Robust angle-based transfer learning in high dimensionscited by 16
  8. 2019Integrative Factor Regression and Its Inference for Multimodal Data Analysiscited by 11
  9. 2021Multitask Learning and Bandits via Robust Statisticscited by 11
  10. 2015New Perspectives on k-Support and Cluster Normscited by 9
  11. 2022Transfer Learning for High-dimensional Quantile Regression via Convolution Smoothingcited by 7
  12. 2023Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneitycited by 7
3 more
  1. 2023Multi-Task Learning with Summary Statisticscited by 7
  2. 2020Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Modelscited by 1
  3. 2023Transfer Learning for Portfolio Optimizationcited by 1

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