The focus of a hedge fund will always be on alpha generation. What’s changing is how funds get there. Hedge funds are shifting focus and expanding into net-new asset classes and investment strategies in pursuit of alpha. Examples include cryptocurrencies, private equity, private credit, real estate, and infrastructure. There has also been an uptick in multi-manager funds and separately managed account platforms, which both come with their own set of challenges. Based on how the front office is structured, the middle and back offices must accommodate dynamic requirements around onboarding and offboarding managers, increased demand for high-quality data, and greater reporting complexity.
The pressure on hedge funds to perform has never been higher, with fierce market competition and investors demanding better returns. The hunt for returns and alpha generation is pushing managers to re-examine certain investment strategies. Over the last five years, the industry has moved toward private investments. In many cases, there is no clear distinction between hedge funds and private market funds – those lines are now blurred.
There is an ever-pressing need for data availability and accessibility. Front, middle, and back offices can no longer operate in silos. The front office needs real-time access to quality data to drive their research and generate effective investment strategies. The underlying technologies have also become more sophisticated and reliant on high-quality data throughout the investment lifecycle. A centralized, harmonized view of investment data is now required.
Firms never want middle- and back-office operations to dictate their strategy or where they can invest. As firms move into new strategies and verticals, middle and back offices must revisit their processes and technology to support front-office decisions.
The demand for diversified data is driving investment in flexible, scalable technology. If the front office decides to move into a new strategy or vertical, firms need confidence in the middle and back-offices’ ability to support those decisions effectively and efficiently.
Historically, many hedge funds captured and stored data in various systems, creating silos of disparate data sources. Firms have tried to address this by centralizing the data via data warehouses. But all too often, it becomes a dumping ground that fails to improve usability. Firms are challenged with preserving data integrity and quality while accommodating ever-evolving reporting requirements and demands for direct access. Funds without a Chief Data Officer or Head of Data Strategy may struggle to implement a consistent data strategy across functions – effective strategies rarely evolve organically in this space. The cost-sensitive environment further increases the difficulty of developing an effective, holistic data strategy.
Funds are leveraging artificial intelligence (AI) and machine-learning models more. Firms are experimenting with AI and building out data analysis teams, which could place additional demands on their data operations functions. The intended usage is wide-ranging – from automating reconciliations and streamlining workflows to driving investment decisions. While there is uncertainty as to how this technology will evolve, firms must be prepared to nimbly incorporate and support AI and machine learning.
Robert (Bob) Longden is a Senior Vice President for Sales & Partnerships at Arcesium. Bob joined D.E. Shaw’s technology organization in 2014 as part of the initiative to formally launch Arcesium. Prior to that, Bob spent 13 years at Acorn Systems as Vice President of Sales and Professional Services, selling and delivering costing and profitability solutions in the financial services vertical. Bob earned a bachelor’s degree in economics and a certificate in finance from Princeton University.
This article originally appeared in the Preqin Global Report 2024: Hedge Funds.
