Your Data Framework Guide to Public and Private Convergence
May 13, 2024
Read Time: 6 minutes
Unified Data
Institutional asset managers strategizing for ways to gain an edge over competitors are embracing the concept of sectors without borders. Asset class convergence is reshaping institutional asset managers’ product mix and leading them to transform the very core of their business.
Today, investment managers are recalibrating their traditional 60/40 model by expanding into the private markets for better returns, greater diversification, and lower volatility. In addition, many institutions are leveraging private investments in their funds because of appealing valuations and the power to attract new investors. Multiple factors, including access to more capital, have spurred investment growth in recent years. In addition, private companies are viewed as offering a higher degree of innovation and growth potential; many also remain private for longer, compelling traditional asset managers to enter the private markets and capitalize on early investment opportunities.
When institutions begin to ‘converge’ their public and private investments, many find their systems are not set up to manage the new investments or the associated data — for multiple reasons. Private and public markets have different datasets, track their data in different systems, have different investment horizons and lock-up periods, and more.
With both the investments and data in multiple systems, firms don’t have a holistic view of their holdings or returns. And with no standard way to calculate performance, the decision-making process can often turn subjective. As a result, many firms often simply calculate separate performance results for their public and private investments. The disparity in calculation methodologies limits comprehensive oversight and prohibits the creation of blended strategies and portfolios.
The convergence of public and private investments has become a defining feature of modern finance. Many organizations, however, fail to fully grasp the potential implications and risks associated with this convergence. With a growing emphasis on multi-asset class strategies to boost returns and ensure sustained growth, it is crucial for firms to create a sophisticated data and operations framework that enables them to navigate this new paradigm.
As companies navigate the complexities of managing diverse portfolios spanning liquid and illiquid assets, the need for a robust data framework becomes increasingly apparent. A data framework should be capable of handling the unique demands of both public and private investments, addressing key challenges, and revealing new possibilities for businesses.
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Implementing the right data and operations framework lies at the heart of addressing the challenges of converging public and private investments. A comprehensive data model should facilitate seamless integration of various types of data across the lifecycle of both asset classes, from securities and deal setup to ongoing portfolio management.
With holdings data in one easily accessible system, ops and reporting teams can get the data they need, while portfolio managers can holistically see exposure. Firms also gain the ability to create blended funds and strategies across liquid and illiquid assets. Blended strategies enable firms to offer a diverse mix of public and private strategies by combining various investment styles, helping to differentiate their offering while reducing portfolio risk. Being able to better manage returns during periods of volatility promotes a competitive advantage. And a data model that can ingest transactions, holdings, and lifecycle events across illiquid and tradeable assets is often the edge firms need to set their business apart.
One of the key challenges in managing public and private investments together is harmonizing industry classifications. A robust data framework can help translate private asset classifications to classification schemes of listed assets, enabling firms to blend holdings and analyze exposure across industries. This integration is another essential element of creating blended funds and strategies that leverage the strengths of both asset classes.
A critical aspect of any data framework is its ability to accurately calculate performance across public and private investments. While standardized methods exist for tradeable assets, such as P&L calculations, private assets often require different metrics, such as IRR and various multiples, and there is certainly no standard to combine the two. Harmonizing disparate methodologies into a unified system is essential for generating comprehensive performance reports that reflect the true value of a diversified portfolio.
A flexible data system with self-service tools lets users create their own data framework and extend the model with as many user-defined fields as necessary. A data framework that creates an ecosystem where you can easily integrate new models and datasets into the existing universe of data and information is invaluable as you enter new asset classes and scale your organization.
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When asset classes converge, firms have two different methods to calculate performance and no widely accepted way to unify them. Systems must be able to calculate IRR, P&L, multiples, and more on both liquid and illiquid assets, while also giving users the ability to configure flexible performance and customize IRR calculations across holdings. You should be able to:
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When it comes to implementing a data framework, companies must decide whether to build from scratch, buy an existing solution, or work with a partner to support their data initiatives. While building a custom platform offers flexibility, leveraging a purpose-built data platform can significantly reduce time to market. By choosing the right approach, firms can expedite the implementation process and streamline reporting capabilities.
As traditional asset management portfolio experience a fundamental shift, a partner can offer the right expertise and technology to help you evaluate your buy, buy, or partner options.
The benefits of implementing a comprehensive data framework extend far beyond streamlined analytics. With the right infrastructure in place, firms can launch blended strategies that differentiate them in the market, allocate capital more effectively between liquid and illiquid investments, and provide investors with faster, more holistic reporting. Furthermore, a robust data framework opens the door to advanced data science initiatives, enabling more sophisticated analyses and insights.
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In an interconnected financial landscape, the convergence of public and private investments presents both challenges and opportunities for firms. By investing in a comprehensive data framework capable of handling the complexities of both asset classes, companies can unlock new possibilities for portfolio management, reporting, and data-driven decision-making.
To learn more about embracing convergence to stay competitive, support innovation, and drive growth, watch our fireside chat here.
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