Sparse Unit-Sum Regression
Methodology
2019-07-11 v1 Computation
Abstract
This paper considers sparsity in linear regression under the restriction that the regression weights sum to one. We propose an approach that combines - and -regularization. We compute its solution by adapting a recent methodological innovation made by Bertsimas et al. (2016) for -regularization in standard linear regression. In a simulation experiment we compare our approach to -regularization and -regularization and find that it performs favorably in terms of predictive performance and sparsity. In an application to index tracking we show that our approach can obtain substantially sparser portfolios compared to -regularization while maintaining a similar tracking performance.
Cite
@article{arxiv.1907.04620,
title = {Sparse Unit-Sum Regression},
author = {Nick Koning and Paul Bekker},
journal= {arXiv preprint arXiv:1907.04620},
year = {2019}
}