Sparsity and stability for minimum-variance portfolios

Abstract

The popularity of modern portfolio theory has decreased among practitioners because of its unfavorable out-of-sample performance. Estimation risk tends to affect the optimal weight calculation noticeably, especially when a large number of assets are considered. To overcome these issues, many methods have been proposed in recent years, but only a few address practically relevant questions related to portfolio allocation. This study therefore uses different covariance estimation techniques, combines them with sparse model approaches, and includes a turnover constraint that induces stability. We use two datasets of the S&P 500 to create a realistic data foundation for our empirical study. We discover that it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover. Moreover, we find that simply using LASSO is insufficient to lower turnover when the model’s tuning parameter can change over time.

Company classification using machine learning

“…Others penalize the optimal weights by using practical constraints or some form of regularization (see, e.g. Jagannathan and Ma, 2003;Brodie et al, 2009;DeMiguel et al, 2009a;Fan et al, 2012;Fastrich et al, 2015;Husmann et al, 2019). Another line of research focuses on directly improving the estimation of the expected returns (see, e.g.…”