Blog Reimagining Multi-Asset Hedge Fund Operations: AI, Automation, and the Future of Multi-Strategy Performance

Reimagining Multi-Asset Hedge Fund Operations: AI, Automation, and the Future of Multi-Strategy Performance

October 2, 2025

Read Time: 5 minutes

Authors: Premal Desai

Innovation & Tech

Hedge Funds

The multi-strategy hedge fund model continues to shine as Hedge Fund Research’s (HFR) Multi-Strategy Index surged +7.5 percent in August, leading all strategies. However, according to HFR’s monthly analysis, performance dispersion between high performing funds and lower performing funds expanded in August.ⁱ

A granular analysis of what has been driving success for some hedge funds would parse out market drivers in different sectors and dive into results for event-driven, macro-systematic, equity hedge, or other high-performing strategies. Of course, this will constantly change from quarter to quarter as geopolitical and economic forces fluctuate. One thing is certain: Competition among funds has intensified in 2025, with fund launches outpacing liquidations in Q1 by 121-73.ⁱⁱ

Here are some impactful ways that multi-strategy hedge funds can gain competitive advantages in a crowded marketplace, including delivering lifecycle automation and control across all asset classes to drive multi-strategy, multi-asset growth.

Multi-asset hedge fund operations and data governance

Digital transformation has enabled us to get more sophisticated by making data and operations simpler. Many managers still struggle to manage funds with assets like credit, interest rate derivatives, equities, and FX, challenged to track them in a single, precise, and adaptable view. To effectively manage operations across so many diverse asset classes, firms need centralized data models and integrated platforms that can handle a wide variety of financial instruments and data types.

Funds must have systems that can handle comprehensive asset-class coverage and can make sense out of the intricacies of public-private class convergence. By unifying data from disparate systems, firms can gain a comprehensive view of risk and exposure across all managers and asset classes. Our Aquata data platform, purpose built for the investment industry, supports all asset classes and geographies, including more complex instruments like alternatives, digital assets, private debt — and the unstructured data that comes with them. Moreover, different departments at the firm need to view information in different forms for their unique purposes.

Aquata handles different data formats and levels of granularity, such as representing a swap as a single asset or as two separate legs. Leadership needs unified enterprise visibility into the fund portfolio; fund accountants need to view performance separated by strategy; risk managers need to see position-level exposure; and treasury needs access to data on collateral requirements and margin usage.

For a deep dive into how funds reduce operational costs by optimizing financial workflows, see our previous article Streamline Financial Operations to Support Front-Office Ambitions

Multi-manager transparency: allocations, fees, and performance

The UK-based Man Group employs a multi-manager platform, perhaps the most complicated hedge fund business model, in which they gain exposure to diverse segments, asset classes, and sectors. In doing so, they can execute virtually any strategy, and trade in multi-asset portfolios. It is working; Man Group has grown its asset base by 25% in five years.ⁱⁱⁱ

To make these funds excel, they must have the operational agility to look at the overall portfolio but also drill down to manager-by-manager data within their roster of independent investment teams. Transparency into manager-level exposures and risk from a top-level perspective is a necessity in understanding how different strategies interact. However, precise allocations to track performance and management fees for optimized fund performance is no simple task, especially in firms still using fragmented systems and manual data quality management processes.

Solving complex multi-manager allocations and performance

In a multi-manager structure, the firm incentivizes each manager to perform with performance and management fees and evaluates them based on their portfolio. If a firm incorrectly tracks allocations or the performance of individual managers, they risk inaccurate accruals or payments, errant investor reporting, and a skewed appraisal of liquidity and cash flow. Further, the firm will be hamstrung when it comes to rewarding and extending capital to the best managers.

Arcesium’s multi-asset hedge fund operations platform, called Opterra, includes modules like Financial Data Stack for mastering reference data across asset classes, markets, and life cycle events, Universal Book of Record (UBOR) investment accounting, and PerformA investor accounting. It is designed expressly to help funds thrive in the kinds of complexity like multi-manager structures, offering them separate strategy views, allocations, and performance. For more information on the critical factors for hedge funds to consider when moving to or investing in a multi-manager platform model, see our previous article The Rise of Multi-Manager Hedge Funds.

The aforementioned Man Group is a behemoth fund, boasting over $190 billion in AUM. However, multi-strats are no longer the sole domain of large shops. Technology has changed the playing field.

Small- and medium-sized funds embracing multi-strategy approach

According to HFR’s monthly analysis, performance dispersion expanded in August 2025, as the top decile advanced by an average of +10.7 percent while the bottom decile fell by an average of -2.7 percent. These strikingly mixed results, including a year-over-year top-to-bottom dispersion of 62.4 percent, suggest a success gap instead of consistency across the industry.^iv Competition is unrelenting, and smaller hedge funds shouldn’t be left to face barriers to entry of multi-asset strategies.

Cloud data architecture and cloud-native investment platforms not only make scaling the businesses easier but also lower the total cost of ownership and costs of implementation. By using a vendor-provided, cloud-based platform, firms can avoid the high costs and resource drain of building their own systems. By installing the ideal hedge fund infrastructure, firms of any size can achieve scalability in a unified platform that can also support multiple asset classes and scales efficiently. Critically, this also helps smaller funds avoid the cost-prohibitive need to hire 10, 20, or 50 people or invest heavily in their technology stacks when scaling AUM.

At about $4 billion AUM, Eisler Capital is smaller than the very big multi-strategy firms ^v like Citadel or Millennium yet large enough to operate across multiple managers/strategies. Deephaven, Verdad’s multi-asset long-short hedge fund has $92 million AUM.^vi The cloud evens the playing field.

AI-driven multi-asset hedge fund operations

It hasn’t slipped my mind. AI is ready for its close up, center stage in the investment management industry. Hedge funds’ AI adoption has grown from 18% in 2024 to 46% in 2025, with larger hedge funds (50+ employees) advancing much faster, at nearly 75%.

Hedge Fund AI Operations

“This pattern suggests that larger firms possess the necessary resources, expertise, and data volumes to effectively implement AI solutions. However, the notable exception – smaller hedge funds achieving 39% adoption compared to 25% for firms with 6-10 employees – indicates that startups may be bypassing traditional scaling constraints through cloud-based AI services.”

- Jacob Wolinsky, Hedge Fund Alpha^vii

AI can enhance cross-asset investment data integration by ingesting, normalizing, and transforming data at scale. Aquata and its AI Copilot helps funds harmonize and govern data in real time across all assets and geographies, enabling unified analytics and self-service reporting. Self-service AI tools help non-technical users across the fund grab whatever insights they need with real-time data harmonization and analytics — while in the background, the data quality and governance engines ensure those insights are up to date, precise, and easy to understand.

Multi-asset hedge fund operations infrastructure drives competitive advantage

Innovative operational and data infrastructure removes barriers, labor, cost, time, and complexity burdens when expanding or launching multi-asset and multi-manager strategies. It also opens the door to higher level decision making that moves the needle on revenue generating functions like margin efficiencies and capital deployment efficiencies.

5 Key Takeaways

Q1: Why are multi-asset hedge fund operations critical in 2025?

A1: They unify data and processes across asset classes, helping funds manage complexity, scale efficiently, and stay competitive amid growing dispersion.

Q2: How does lifecycle automation benefit hedge funds?

A2: Automation reduces manual processes, ensures accurate allocations, improves risk visibility, and lowers operational costs across all strategies.

Q3: What challenges do multi-manager funds face?

A3: They require transparency into manager-level exposures, performance tracking, and allocations — without which risk, reporting, and incentive structures break down.

Q4: How can AI transform multi-asset hedge fund operations?

A4: AI ingests and harmonizes data in real time, enabling unified analytics, self-service reporting, and stronger governance across geographies and assets.

Q5: Can smaller hedge funds adopt multi-asset strategies?

A5: Yes. Cloud-native platforms lower technology costs, reduce headcount needs, and let smaller funds scale into multi-strategy models previously reserved for mega-managers.

Authored By

Premal Desai

Premal Desai is a Senior Vice President overseeing the product team in India for Arcesium. Prior to his current role, Premal was co-head of Arcesium’s Financial Operations group in India.

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Bibliography:

[i] HFR, September 8, 2025. https://www.hfr.com/media/market-commentary/hfri-surges-in-august-driven-by-record-equities-expectations-for-lower-interest-rates/

[ii] Opalesque, July 8, 2025. https://www.opalesque.com/industry-updates/7735/hedge-fund-launches-up-liquidations-down-to-start.html

[iii] US News, 5 of the Top Hedge Funds in 2025, September 3, 2025. https://money.usnews.com/investing/funds/articles/top-hedge-funds-this-year

[iv] HFR, September 8, 2025. https://www.hfr.com/media/market-commentary/hfri-surges-in-august-driven-by-record-equities-expectations-for-lower-interest-rates/

[v] The Financial Times, January 4, 2024. https://www.ft.com/content/b1461e18-f96a-4ba7-9123-1b3580a6407c?desktop=true&segmentId=d8d3e364-5197-20eb-17cf-2437841d178a#myft:notification:instant-email:content/

[vi] Hedge Fund Alpha, May 27, 2025. https://hedgefundalpha.com/news/verdad-10-years-1b-in-aum/

[vii] Hedge Fund Alpha, Jacob Wolinsky, https://hedgefundalpha.com/news/ai-hedge-funds-data-usage/

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Reimagining Multi-Asset Hedge Fund Operations: AI, Automation, and the Future of Multi-Strategy Performance

Multi-asset hedge fund operations and data governance

Multi-manager transparency: allocations, fees, and performance

Solving complex multi-manager allocations and performance

Small- and medium-sized funds embracing multi-strategy approach

AI-driven multi-asset hedge fund operations

Multi-asset hedge fund operations infrastructure drives competitive advantage

5 Key Takeaways

Subscribe Today

Subscribe Today

Multi-asset hedge fund operations and data governance

Multi-manager transparency: allocations, fees, and performance

Solving complex multi-manager allocations and performance

Small- and medium-sized funds embracing multi-strategy approach

AI-driven multi-asset hedge fund operations

Multi-asset hedge fund operations infrastructure drives competitive advantage

5 Key Takeaways

Subscribe Today

Subscribe Today