Large-Scale Mean-Variance Optimization and Chunking Algorithm

38 Pages Posted: 11 Jan 2022

See all articles by Gilles Boevi Koumou

Gilles Boevi Koumou

Université Mohammed VI Polytechnique

Date Written: January 10, 2022

Abstract

We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization problems --- with weight upper bound constraints and without short sales --- when the size of mean-variance portfolios is much smaller than the number of assets, which is almost always the case. Our novel algorithm is built on the novel representation of mean-variance models in terms of the support vector data description --- an unsupervised machine learning algorithm designed for a one-class classification problem --- and the chunking algorithm, a decomposition algorithm for support vector machine.

Keywords: Mean-Variance Optimization, One-Class Classification, Machine Learning, Support Vector Machine, Support Vector Data Description, Chunking Algorithm, Quadratic Programming

JEL Classification: G11, D83, C61, C63

Suggested Citation

Koumou, Gilles, Large-Scale Mean-Variance Optimization and Chunking Algorithm (January 10, 2022). Available at SSRN: https://ssrn.com/abstract=3917437 or http://dx.doi.org/10.2139/ssrn.3917437

Gilles Koumou (Contact Author)

Université Mohammed VI Polytechnique ( email )

Rabat
Morocco

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