What To Expect When You’re Expecting Alts in Your Portfolio

Alternative investments have become the ultimate misnomer in our industry. Once conservative institutional investors like insurers, pension funds, foundations, and endowments got into alts for diversification and yield, it became safe to say that the asset classes are set for the mainstream. The most alternative thing about them is how asset owners have to conduct day-to-day operations and manage data. Their incursion into traditional portfolio allocations has forced a structural and technological adaptation, a radical shift in operational and data management requirements.

The average asset owner’s tech stack is disparate, disconnected, and diverse, with a dozen (or dozens) of different systems for managing public equities, private markets, hedge funds, and more. Here is the operational reality of increasing alts exposure and how to rectify the data roadblocks so scaling becomes smooth and funds can make decisions with a timely, accurate view of portfolio exposures, liquidity and cash flow forecasting, and performance attribution.

How alts break the spreadsheet

The share of total institutional AUM held in alternatives is projected to rise to approximately 16% by 2027, up from 11% in 2015.ⁱ Bain predicts alts will grow twice as fast as public assets; KKR predicts more than $24 trillion in assets in 2028, up from $15 trillion.ⁱⁱ Institutional scaling of alts is still underway, and so is the digital data transformation required to support it.

Alts is an environment characterized by deliberate opacity, fragmented systems, a deluge of unstructured data, and managing exposure across public and private investments. To further complicate operations, most private market funds use highly custom management and incentive fee structures and bespoke (U.S. and European) waterfall logic, including unique hurdles that standard out-of-the-box calculators cannot natively model.ⁱⁱⁱ Further, the shift into alts classes introduces a mix of closed-ended funds, open-ended “evergreen” funds, co-investments, master-feeder funds, and separately managed accounts (SMAs).

As allocators increase their number of fund investments and managers, the math behind performance attribution and fees becomes completely untenable in Excel. For example, master-feeder structures create a web of direct and indirect exposures that are difficult to track manually once a firm reaches double-digit fund or investor counts. When expanding further into alts funds, limited partners (LPs) should check under the hood of their data flows to make sure the investments will not call for excessive manual grunt work or linear talent acquisition.

The core data challenges in alternatives portfolio management

Today’s leading asset owners have been constructing tech stacks, building and buying platforms for ops like accounting, risk, performance, and asset classes like public, private, and hedge funds. Larger LPs have a hodgepodge of legacy systems, best-in-breed point solutions, and internally built systems. Disparate systems used for different sleeves of the business require complex manual reconciliations to get a house view of total risk. Disconnected point solutions also force firms to perform data mapping exercises between different data schemas, which increases maintenance costs and the likelihood of data discrepancies.

When it comes to blending private market funds into the portfolio mix, an investor isn’t running into a minor technical difference. It’s a substantial challenge that informs how the system ingests data, governs it, and scales it. The public-private convergence problem can plague organizations lacking a modern data consolidation layer that automates and orchestrates everything through a golden-source data foundation.

H2 Data challenges for private market portfolio management

Unlike public markets, private markets have no exchanges or standardized data streams, so data doesn’t reconcile cleanly across systems. When investing in private markets for the first time or scaling those investments, institutional investors must put a concerted effort into evaluating how to combine internal tools, vendor solutions, and a platform-agnostic data layer to drive scale, transparency, and long-term agility.

That platform-agnostic data layer will corral impossibly large and disparate data sets into a consolidated, standardized single source, so the asset owner’s analysts, managers, and accountants can actually use the information with confidence. Subsequently, the fund can accurately navigate different valuation methodologies (public vs. private), prevent timing mismatches (daily vs. quarterly), and reconcile the inconsistent identifiers. A modern data foundation is indispensable for multi-strategy investing.

H2 Reporting challenges & look-through analysis private markets

Asset owners like pension funds and insurance companies operate with rigorous regulatory, board of directors, and end-client reporting obligations. GPs have their own data and transparency challenges to contend with. An LP board member or the CEO who sees trouble in the daily headlines about private credit defaults, sudden trade policy shifts, or a tech downturn may ask the head of private equity for net IRR, TVPI, DPI by fund and vintage or liquidity and cash flow forecasts. But a true view of risk and exposure requires analysis that looks through to the underlying portfolio companies. All major regulators, from the OCC and FDIC to the National Association of Insurance Commissioners, urge look-through analysis in financial risk management.