Regularization methods for sparse ESG-valued multi-period portfolio optimization with return prediction using machine learning

Zhongming Wu

doi:10.1016/j.eswa.2023.120850

2023-12-01

PRE

DOI：10.1016/j.eswa.2023.120850

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摘要

En 中文

In the context of global sustainable development, environmental, social, and governance (ESG) investment has become a frontier topic in the field of asset management. This paper focuses on ESG-valued multi-period portfolio selection problem which incorporates ESG factor into traditional portfolio optimization. Using ESG-valued returns instead of traditional returns, we propose an ESG-valued fused LASSO model to promote an optimal sparse multi-period portfolio strategy. The objective of the model is to minimize a combination of returns and risks with respect to ESG ratings based on classical Markowitz mean-variance framework. The sparsity of the portfolio at each period and the turnover across periods is achieved by the l(1) regularization approach. To improve the out-of-sample performance of the ESG-valued model, we introduce two machine learning methods, namely random forest and support vector regression, to predict the ESG-valued returns. We develop a symmetric alternating direction method of multipliers to solve the regularized optimization model. Additionally, some other sparsity-driven penalty functions are discussed which results in a general framework of multi-period portfolio optimization. Finally, numerical experiments on several real datasets from both in-sample and out-of-sample demonstrate the positive effect of ESG in multi-period portfolio optimization.

Keyword:

ESG portfolio

Multi-period portfolio optimization

Regularization

Symmetric alternating direction method of multipliers

Machine learning