How Multi-Manager Hedge Funds Can Tame Complexity and Optimize Performance

The Federal Reserve kept the rates constant at the recent March meeting and said it would wait for further clarity around US trade policy. Since then, clarity has not materialized and markets around the world have expressed concerns with a steep sell-off. Hedge funds had a turbulent March but are girding their forces to capitalize on macro volatility and securities dispersion.

Hedge Fund Research reported that its Fund Weighted Composite Index declined by -0.47 per cent in February, while its Multi-Manager/Pod Shop Index gained +0.92 percent for the month “as managers also navigated the policy and technology volatility.”^[i] But for managers running multiple portfolios under one umbrella, they must overcome major challenges in running the right data at-scale across portfolios that span many agreements, regions, and assets. Complexity can be their crutch, or it can be their competitive edge.

How can innovative hedge funds conquer the unique challenges of the multi-manager model and achieve the visibility needed to accurately gauge and track performance, risk, and allocations across the different portfolio managers?

Competition and complexity in a crowded field

Cost efficiency has never been more paramount in the high-cost, hyper competitive hedge fund ecosystem, as 2025 has seen expansions and new fund launches. A Barclays survey revealed that investors have an increased interest in allocating to statistical arbitrage and multi-manager fund sub-strategies for their 2025 allocations. Funds are always seeking impactful avenues to scale and differentiate their business from other firms in exposure to diverse segments, asset classes, and sectors. With more allocators wanting access to multi-manager hedge funds, and new ones on the rise, this space is only going to grow, leading to worsening data complexity.

Cash and margin management at PM-level and fund-level

Multi-manager funds can attract and empower specialized managers in different asset classes. Top talent operates somewhat independently, contributing their expertise. A fund of funds may have 50 hedge funds with 50 executing strategies like L/S equity, macro, quantitative - each of which receive different risk profiles and margin and treasury requirements.

For example, a single portfolio manager’s technology-heavy book may have rigorous margin requirements. But the multi-manager fund overall may benefit from margin offsets across its different managers, allowing it to optimize margin as a whole. The firm can also extend capital to certain managers or increase cash reserves where needed. However, to do this fund-level aggregation, while still maintaining accurate visibility into the individual P&Ls of each portfolio manager is a challenge. This requires granular fund-level and agreement-level data, and many traditional systems lack support for this level of granular tracking.

What’s more, everyone from PMs to risk officers needs to view the same information in real time in collateral management, margin agreements, and counterparty exposures. Multi-manager funds will need to move away from fragmented systems and data to have the capability to break down manager-level exposures and manager-level risk and view them from a top-level perspective.

An easy line of sight for optimal capital allocation

Firms flock to multi-manager strategies to achieve efficient capital allocation based on PMs’ performance, tighter risk controls, and swifter AUM expansion. To fully realize margin and capital deployment efficiencies, CIOs, risk leaders, and sector heads must clearly document the specific sources of those gains. This requires a clear line of sight. Firms need to get the exposure between the most talented managers exactly right, minimizing the correlation of their performance. This depends on the continuous flow of data between third-party entities and the operational platform — which requires smooth integration of various systems. Without modern operational infrastructure, the so-called strength in numbers (of managers) becomes an expensive, risky albatross.

However, with reliable, normalized data assembled in the same place, connecting all aspects of investment lifecycles and providing the right on-demand insights, hedge funds can avoid the problems many data science officers have experienced with disparate datasets and data redundancies. Only then can they correctly allocate and track performance and management fees for optimized fund performance, accurate investor reporting, and maximized revenue. Incorrectly tracking allocations or the actual performance of individual managers opens the possibility of inaccurate accruals or payments. Moreover, poor allocations hold up cash, resulting in financing impacts, margin impacts, and overall exposure impacts.

Scaling aggregate risk management

Hedging the overall risk exposure of numerous independent managers requires a certain level of customization in terms of attribution at specific levels for each investing entity. Relying on external fund managers presents complexity and potential risks. For example, if a firm has 30 different hedge funds, there is limited control over execution for each of those managers. Operation teams have a difficult task of following up with a hundred different touch points to confirm they have a handle on important issues within the portfolio, like ensuring trades are settled and cash wires flows are accurate. Risk leaders want the ability to track sophisticated P&L allocations and structures across all fund types to manage liquidity and counterparty risk. And they need to do so in an auditable system that can handle dozens of PMs, hundreds of positions, and the constant movement of capital.

Digital transformation for fee optimization

Hedge fund investors have been vocal about the need to reimagine fee structures in ways that align with performance. Not only are firms incentivizing their multiple PMs, but they are also incentivizing investors. Approximately 60% of investors reported successfully securing fee discounts and 22% got favorable liquidity terms last year. In the trenches of this well-documented battle for talent, speed and precision in fee management becomes a crucial talent retention factor.

Spreadsheets won’t do the trick any longer to glean precise key performance indicators and capital ownerships from multiple managers. Funds can adopt technology to automate complex fee calculations and allocation methodologies. Without precise tracking of performance and allocations across each manager, a well-diversified multi-manager Batmobile becomes just an average black car.

Performance, precision, and growth in the era of multi-manager funds

Firms’ commitment to modernizing data and infrastructure systems will play a pivotal role in their ability to make multi-manager strategies work. More PMs mean more files, more custodians, more touch points, and intimidating complexity. Scaling this model becomes arduous if not prohibitive without investment operations and data technology to help provide oversight of all external allocations. Beyond centralized visibility and control, firms need agility to compete today and the scalability to grow tomorrow via a robust, adaptable framework. Those that conquer the complexity win with enhanced returns through specialized management. Those that win use modern technology to redirect their efforts from the day-to-day operational workflows to higher-value decision-making that drives alpha.

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David Nable is Managing Director at Arcesium

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The views expressed in this article are those of the author and do not necessarily reflect the views of AlphaWeek or its publisher, The Sortino Group

Footnotes

[i] HEDGE FUND PERFORMANCE MIXED IN FEBRUARY AS TRADE/TARIFF VOLATILITY SURGES”, HFR, March 7, 2025.