A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization

TL;DR

This paper introduces a novel Cholesky decomposition-based heuristic for selecting assets in sparse tangent portfolio optimization, aiming to reduce transaction costs and improve efficiency.

Contribution

It presents a new asset selection heuristic using surrogate formulation and re-optimization, enabling faster portfolio construction with fewer assets.

Findings

Faster optimization of sparse portfolios demonstrated.

Effective asset subset selection shown through numerical analysis.

Proposed heuristic offers a practical approach for large asset pools.

Abstract

In practice, including large number of assets in mean-variance portfolios can lead to higher transaction costs and management fees. To address this, one common approach is to select a smaller subset of assets from the larger pool, constructing more efficient portfolios. As a solution, we propose a new asset selection heuristic which generates a pre-defined list of asset candidates using a surrogate formulation and re-optimizes the cardinality-constrained tangent portfolio with these selected assets. This method enables faster optimization and effectively constructs portfolios with fewer assets, as demonstrated by numerical analyses on historical stock returns. Finally, we discuss a quantitative metric that can provide a initial assessment of the performance of the proposed heuristic based on asset covariance.