Optimal Portfolio Choice with Unknown Benchmark Efficiency

Raymond Kan () and Xiaolu Wang ()
Additional contact information
Raymond Kan: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Xiaolu Wang: Ivy College of Business, Iowa State University, Ames, Iowa 50011

Management Science, 2024, vol. 70, issue 9, 6117-6138

Abstract: When a benchmark model is inefficient, including test assets in addition to the benchmark portfolios can improve the performance of the optimal portfolio. In reality, the efficiency of a benchmark model relative to the test assets is ex ante unknown; moreover, the optimal portfolio is constructed based on estimated parameters. Therefore, whether and how to include the test assets becomes a critical question faced by real world investors. For such a setting, we propose a combining portfolio strategy, optimally balancing the value of including test assets and the effect of estimation errors. The proposed combining strategy can work together with some existing estimation risk reduction strategies. In both empirical data sets and simulations, we show that our proposed combining strategy performs well.

Keywords: portfolio choice; model efficiency; estimation risk; optimal combining (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.01767 (application/pdf)

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:inm:ormnsc:v:70:y:2024:i:9:p:6117-6138

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().