Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

TL;DR

This paper introduces a data-adaptive multi-task learning approach that improves multi-sector portfolio optimization by accurately estimating factor models across sectors, using a novel projection-penalized PCA algorithm.

Contribution

It proposes a new multi-task learning method for factor model estimation in multi-sector portfolios, enhancing optimization accuracy and model recovery.

Findings

Simulation studies show improved estimation accuracy.

Application to Russell 3000 data demonstrates practical benefits.

The method outperforms traditional approaches in portfolio optimization.

Abstract

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.