Key Takeaways
- • Florida State Board of Administration committed an additional $150 million to JPMorgan Asset Management's Elan Fund, expanding its total commitment to $300 million, to gain rapid exposure to machine learning-driven hedge fund strategies without building internal expertise.
- • Despite managing $209 billion in assets, SBA maintains only two fund-of-hedge funds in its portfolio (Elan Fund and a BlackRock vehicle), each distributing $400–$450 million to quantitative managers, demonstrating selective use of the structurally disadvantaged fund-of-funds model.
- • SBA's strategic investments unit employed the fund-of-funds vehicle specifically to reduce equity risk and access specialized, esoteric managers more efficiently than direct hiring would have permitted, prioritizing speed of deployment over fee optimization.
- • The decision signals a niche but persistent use case for funds-of-funds among large institutional allocators: accessing emerging or highly specialized strategies—such as machine learning—where building internal capabilities or conducting direct due diligence would be time-prohibitive or inefficient.
- • SBA has transitioned from fund-of-funds reliance to direct manager selection in other categories since its initial 2014 Elan Fund commitment, indicating a disciplined approach to outsourcing only where strategic advantage justifies the additional fee layer.
Summary
The Florida State Board of Administration, which oversees $209 billion in state assets, made an unconventional move in spring 2019 by committing $150 million to JPMorgan Asset Management's Elan Fund—a fund-of-hedge funds structure—on top of an existing $300 million allocation. This decision stands in sharp contrast to prevailing institutional practice over the past decade, during which large allocators have systematically eliminated fund-of-funds vehicles in favor of direct manager selection to avoid redundant fee layers.
According to Trent Webster, senior investment officer at SBA, the rationale centered on machine learning strategies. "JP Morgan came to us with the concept of investing in new machine learning funds and asked if we'd be interested, and we said yes," Webster explained. The allocation was designed to reduce equity risk within SBA's strategic investments mandate while gaining rapid access to esoteric, specialized managers. Rather than building internal expertise or conducting protracted due diligence on emerging machine learning hedge funds, SBA determined that deploying capital through JPMAM's curated vehicle was more efficient. "We did it that way to get a significant allocation quickly at the time, rather than build up the expertise," Webster noted.
The decision reflects SBA's pragmatic, selective approach to fund-of-funds structures. The organization maintains only two such vehicles in its entire portfolio—the Elan Fund and a BlackRock-managed counterpart—each distributing approximately $400–$450 million to quantitative managers on SBA's behalf. Webster acknowledged that SBA typically builds direct relationships with hedge funds, stating "Generally, we'll do it ourselves." However, the firm has deliberately tasked its remaining fund-of-funds managers with identifying highly specialized or emerging strategies that warrant outsourced manager selection. SBA's initial Elan Fund commitment in late 2014 was structured to provide exposure to managed futures and quantitative strategies, demonstrating the vehicle's role as a specialized access point rather than a general-purpose allocation mechanism.
The SBA case illustrates a narrowing but persistent niche for fund-of-funds among large, sophisticated allocators: accessing novel or difficult-to-evaluate strategies where the speed and expertise of an established vehicle justifies retaining the fee layer. Rather than viewing fund-of-funds as universally obsolete, SBA treats it as a tactical tool for specific strategic objectives.
According to Trent Webster, senior investment officer at SBA, the rationale centered on machine learning strategies. "JP Morgan came to us with the concept of investing in new machine learning funds and asked if we'd be interested, and we said yes," Webster explained. The allocation was designed to reduce equity risk within SBA's strategic investments mandate while gaining rapid access to esoteric, specialized managers. Rather than building internal expertise or conducting protracted due diligence on emerging machine learning hedge funds, SBA determined that deploying capital through JPMAM's curated vehicle was more efficient. "We did it that way to get a significant allocation quickly at the time, rather than build up the expertise," Webster noted.
The decision reflects SBA's pragmatic, selective approach to fund-of-funds structures. The organization maintains only two such vehicles in its entire portfolio—the Elan Fund and a BlackRock-managed counterpart—each distributing approximately $400–$450 million to quantitative managers on SBA's behalf. Webster acknowledged that SBA typically builds direct relationships with hedge funds, stating "Generally, we'll do it ourselves." However, the firm has deliberately tasked its remaining fund-of-funds managers with identifying highly specialized or emerging strategies that warrant outsourced manager selection. SBA's initial Elan Fund commitment in late 2014 was structured to provide exposure to managed futures and quantitative strategies, demonstrating the vehicle's role as a specialized access point rather than a general-purpose allocation mechanism.
The SBA case illustrates a narrowing but persistent niche for fund-of-funds among large, sophisticated allocators: accessing novel or difficult-to-evaluate strategies where the speed and expertise of an established vehicle justifies retaining the fee layer. Rather than viewing fund-of-funds as universally obsolete, SBA treats it as a tactical tool for specific strategic objectives.
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