Enhancing an existing algorithm for small-cardinality constrained portfolio optimisation

Nathan Phelps and Adam Metzler

Journal of the Operational Research Society, 2024, vol. 75, issue 5, 967-981

Abstract: The efficient frontier (EF) allows an investor to (in theory) maximise their return for a given level of risk. Portfolios on the EF may contain many assets, making their management difficult and possibly expensive. An investor may wish to impose an upper bound on the number of assets in their portfolio, leading to so-called cardinality constrained efficient frontiers (CCEFs). Recently, a new algorithm was developed to find CCEFs for small cardinalities. Relative to other algorithms for this problem, this algorithm is very intuitive, and its authors demonstrated that it performs at nearly the state-of-the-art. However, we have found that the algorithm seems to struggle in certain situations, particularly when faced with both bonds and equities. While preserving its intuitiveness, we modified the algorithm to improve its CCEFs. This improvement comes with longer runtimes, but we think many practitioners will prefer the algorithm with modifications. Some practitioners may prefer other algorithms, due to the runtimes or because some points on our CCEFs still fall short of optimality. However, in addition to its intuitiveness, our modified algorithms (and the original version) find low-risk points on the CCEF that a state-of-the-art algorithm does not.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2023.2224823 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:75:y:2024:i:5:p:967-981

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2023.2224823

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().