JOURNAL ARTICLE

Asset Allocation with Premium North American Sports Franchises.

  • Published In: Journal of Private Markets Investing, 2026, v. 24, n. 3. P. 9 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Baran, Zach 3 of 3

Abstract

Sports franchises and related private investments have increasingly attracted attention from institutional investors, yet their role in strategic asset allocation remains underexplored. This article analyzes premium North American sports franchises as a distinct private asset class using the Ross–Arctos Sports Franchise Index, or RASFI, a transaction-based benchmark covering Major League Baseball, the National Basketball Association, the National Football League, and the National Hockey League. We document the historical return, volatility, and correlation characteristics of these assets and relate them to core economic fundamentals, including league governance, revenue sharing, monopsony power in talent markets, and capital structure constraints. Using a series of portfolio allocation exercises, we evaluate the implications of adding sports franchise equity to diversified portfolios under varying assumptions about expected returns, risk, and parameter uncertainty. Even under conservative scenarios, the results suggest that premium sports franchises exhibit properties that can improve portfolio efficiency relative to traditional and alternative asset classes. We conclude by discussing the limitations of standard mean–variance frameworks in this context and outlining directions for future research on robust portfolio construction for illiquid private assets. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Private Markets Investing. 2026/04, Vol. 24, Issue 3, p9
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:3069-5864
  • DOI:10.3905/jpmi.2026.1.014
  • Accession Number:193016473
  • Copyright Statement:Copyright of Journal of Private Markets Investing is the property of With Intelligence Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.