EURO 2024 Copenhagen
Abstract Submission

EURO-Online login

3673. Cardinality Constraints Meet Large-scale Portfolio

Invited abstract in session TB-52: Mixed Integer Optimization II, stream Combinatorial Optimization.

Tuesday, 10:30-12:00
Room: 8003 (building: 202)

Authors (first author is the speaker)

1. Yuan Chen
Department of Statistics and Operations Research, University of Wien
2. Immanuel Bomze
Dept. of Statistics and OR, University of Vienna
3. Nikolaus Hautsch
Department of Statistics and Operations Research, University of Vienna
4. Bo Peng
University of Vienna

Abstract

In financial econometrics, the focus is often on improving covariance matrix estimations rather than addressing optimization problems with constraints for better portfolio management. This paper argues that combining these advanced estimation methods with optimization that includes specific limits, like cardinality constraints, enhances decision-making and investment strategies. Cardinality constraints limit the number of assets in a portfolio, potentially making simpler estimators like the sample covariance sufficient for investment decisions, especially when dealing with large dimensions that typically introduce significant estimation errors affecting portfolio performance.

We also address the issue of managing portfolios when there are fewer data points than assets, leading to non-invertible, noisy covariance matrices. Cardinality constraints simplify this challenge, making it possible to aim for a global minimum variance portfolio despite these limitations.

Empirically, we find that smaller portfolios, constrained by cardinality to include only a subset of available assets, can achieve diversification similar to market portfolios while reducing transaction costs and simplifying analysis. This suggests focusing on smaller, strategically selected portfolios could offer investors efficient and cost-effective outcomes.

Keywords

Status: accepted

Back to the list of papers