Applying Cross-Validation to Improve the Graphical Lasso Method in Optimal Portfolio Selection

Abstract

This study focuses on improving the efficiency of the Graphical Lasso method in optimal portfolio selection through the machine learning technique Cross Validation (CV). Graphical Lasso, a powerful tool for estimating the covariance matrix to optimize asset allocation, often encounters difficulties in selecting the tuning parameter α, requiring user expertise. By integrating the CV technique, this study aims to automate and optimize this process, enhancing the model's accuracy and stability. Data was collected from the Ho Chi Minh City Stock Exchange during the period from 01/01/2019 to 31/12/2023. The results show that GraphicalLassoCV not only enhances investment performance but also reduces risk compared to traditional methods, helping to build an optimal portfolio and providing significant practical value for investors.