Look-Through Analysis for Secondary Private Equity Investors
Why Look-Through Analysis Is Essential for Secondary Private Equity Investors
In the world of secondaries, the value of look-through analysis is rarely disputed. If anything, the surprise is how many LPs still underutilize it.
Unlike primary investors—those who commit at a fund’s inception—secondary buyers step into portfolios mid-flight. To price those interests accurately, they need to know what they’re actually buying. That means going beyond fund-level data and looking through to the underlying portfolio companies.
In secondary transactions, exposure-level transparency isn’t just helpful—it’s essential. It drives better decision-making across three critical points in the workflow:
screening new opportunities, pricing them accurately, and actively managing the portfolio post-close.
Let’s dive in.
How Secondary Investors Evaluate Private Equity Deals
Every secondary deal starts in the same place: a data room.
Inside, buyers typically find a patchwork of documents—capital account statements, quarterly reports, financials, LP notices, and sometimes Excel trackers—all relating to the funds being sold. From that data room, investors must answer two foundational questions:
- Should we dig deeper into this deal? (Screening)
- What price should we offer to hit our return targets? (Pricing)
Both of these depend on the same core capability: extracting and analyzing look-through data from unstructured fund reports.
Use Case 1: Screening and Portfolio Fit
Secondary investing may be opportunistic, but every opportunity still needs to fit within the broader book. Before committing time or capital to a deal, LPs use look-through data to understand how it complements—or conflicts with—their existing exposures.
Typical questions include:
- Are we doubling down on sectors we’re already overweight?
- Does this push us above our target for a geography or theme?
- Is this deal adding diversification—or clustering risk?
By mapping exposures across vintage years, strategies, and regions, buyers can pre-screen deals for strategic fit, pacing discipline, and diversification goals. This kind of bottom-up portfolio view is especially critical for programmatic secondary allocators.
Use Case 2: Pricing and Underwriting Secondary Opportunities
Once a deal clears the screening hurdle, the next task is pricing—and look-through is the engine that drives valuation.
There are two basic approaches:
Top-Down Pricing
This method starts with fund-level characteristics: strategy, vintage, age, historical DPI/TVPI, and headline exposures. Based on those, buyers project future distributions and NAV trajectories, then discount them to today using their target IRR.
Buyers might reference comparable secondaries transactions or apply a discount-to-NAV range based on market conditions. This method is quick—but shallow. It can’t see what’s actually in the portfolio.
Bottom-Up Pricing
This is where look-through analysis becomes indispensable. Sophisticated buyers extract the fund's investment schedule and underlying company financials. Then they:
- Match portfolio companies to public/private comps
- Benchmark sector exposure against current market views
- Model upside potential based on valuation re-rating, exit timelines, and macro conditions
In a NAV-driven secondaries market, you’re not buying a blind pool—you’re buying a set of marked portfolio companies. Understanding what those assets are, how healthy they are, and how much upside they offer is the foundation of accurate pricing and IC approval.
Use Case 1: Bottom-Up Due Diligence of Secondary Opportunities
There are two ways to underwrite a secondary deal: top-down and bottom-up.
A top-down approach relies on surface-level indicators—fund age, vintage year, track record, and overall strategy. It’s fast, but imprecise.
The bottom-up approach is more robust. It involves drilling into the underlying portfolio companies to build a view on current value and future upside. This is where look-through analysis becomes a key differentiator.
Sophisticated buyers start by extracting the fund investment schedule and financials from GP reports. They then identify portfolio companies, benchmark them against relevant comps, and model sector-level return scenarios.
In a NAV-based secondary market, you're not buying a blind pool—you’re buying a bundle of companies with specific exposures. Understanding those exposures is the foundation of accurate pricing, IC approval, and competitive edge.
Use Case 3: Active Portfolio Management Post-Close
The value of look-through doesn’t stop at the point of purchase.
Secondary LPs use exposure-level data to monitor their positions over time—and make proactive decisions when portfolio companies start to exit, sectors shift, or valuation dislocations emerge.
Examples include:
- Selling a fund position that has hit its return target
- Rebalancing after key exits or write-downs
- Reducing exposure to a volatile sector or geography
In a market where liquidity windows open and close quickly, timing is everything. Structured, up-to-date look-through data allows secondaries to act—not react.
Conclusion: Look-Through Analysis in Secondaries Is the New Baseline
Unlike primary investing, secondary private equity investing demands clarity under pressure. You’re not betting on a strategy—you’re underwriting a live portfolio.
That’s why look-through analysis is foundational. It powers sharper screening, more confident pricing, and more agile post-close management. But to do it well, LPs need more than a view—they need a system.
That starts with defining what data to track across asset classes and strategies, and building the infrastructure to collect and structure it efficiently from unstructured GP reports.
For LPs scaling modern secondaries programs, exposure-level data isn’t a luxury—it’s table stakes.
Confident secondary underwriting starts with structured exposure data from VDRs
Related Blogs
Please find here related blogs
Subscribe for the
exclusive Updates!
Heading 1
with a request body that specifies how to map the columns of your import file to the associated CRM properties in HubSpot.... In the request JSON, define the import file details, including mapping the spreadsheet's columns to HubSpot data. Your request JSON should include the following fields:... entry for each column.