Strategy insights 
Finding the sweet spot in today’s private credit market
September 4, 2024
July 12, 2024 | 9 min read
Over the past 10 years, the secondaries market has tripled in size.1 Add to this an explosion of data available as well as new entrants into the markets, and secondary investors are facing a new set of challenges — along with a new set of opportunities. In this context, managers are finding their tried-and-true methods are less effective in isolation and are consequently seeking new solutions to complement existing practices. The question is: how can secondary managers keep up with the growth and evolution of the market — if not stay ahead of it? In this piece, we examine some key secondary market dynamics and look specifically at how certain advanced data analysis tools can provide an important piece of the puzzle in building or maintaining a competitive advantage.
Growth of secondary markets and available data
While portfolio sales of LP interests remain a core part of the market, secondaries have evolved from their historic expressions of distress to a more strategic tool suitable for a wide array of situations. While GP-led transactions represented less than 20% of deal volume 10 years ago, today they have grown to represent approximately 50%.2 And still, there is evidence that the market is not growing fast enough.
While this is anecdotal, our own deal logs show $150-200b of potential deal volume in each of the past two years. The fact that just over $100b actually traded in each year from 2022 to 20233 is partly due to insufficient capital on the buy side to absorb all the assets sellers would like to sell. Additionally, as the market continues to mature, we are seeing new entrants enter the market. These two factors — maturation and new entrants — are in turn driving increased competition and efficiency in the secondary market, although in our view, the market overall remains less competitive than many other parts of the private markets landscape. Given this backdrop, we believe buyers are becoming more selective and seeking solutions to suit their specific needs and interests. This, together with evolving motivations for sellers, helps account for the dispersion in deal types we’re seeing today.
Secondary market expanding volume and complexity
Source: Preqin as of September 2023
Meanwhile, there is a well-known and documented explosion of data. Unlike in public markets, where data is widely and commercially available, private market data is, by definition, both more limited and more difficult to source. There are no Bloomberg style terminals, for instance, as there are in public markets. And so as private market data has grown, it has grown asymmetrically, with some private managers building an intentional stockpile of data and others falling behind. This differentiated access has created an uneven playing field and has also sparked a race to eke out competitive advantages based on data analytics. Specifically, one trend we have observed is the advantage gained when one is able to quickly and accurately underwrite assets and extend a firm offer to a seller early in a sale process. But how do we get there in a way that is fast, effective, reliable, and repeatable?
How accurate, fast, high-confidence bids are a competitive advantage
If we focus our aperture solely on broadly diversified LP-led deals, historically secondary firms have spent significant time manually analyzing underlying funds and assets, and firms that don’t possess the data in-house, need to first source information on the portfolio for sale. This process is repeated for every single deal. While we remain committed to a robust, bottom-up due diligence process leveraging our extensive proprietary data set, we have also seen the benefit of additional insights that new analytic tools can provide, especially during times of market volatility. As investors work through this new landscape, they are trying to answer a few essential questions:
- How can investors accurately price assets and have a high degree of conviction around bids?
- How can investors analyze investment potential returns under different market conditions and confidently evaluate uncertainty?
- And, importantly, how can investors evaluate the quality of an asset to decide whether it’s worth adding it to their portfolio?
While we have always heavily relied on in-house data and proprietary information during our due diligence process, in order to enhance our investment analysis capabilities, we as a firm have been working to create new ways of addressing two fundamental challenges shared by secondary investors across private markets:
- Estimating a portfolio’s current NAV and the effective purchase price relative to a negotiated price that relies on reference date NAV.
- And making this estimate during volatile or trending market conditions, during which time reference-date information is even less reliable.
- Estimating potential cash flows from an investment, and the associated impact on return assumptions, across a variety of potential forward-looking market scenarios.
You can imagine how, in the context of evaluating a developed portfolio that might comprise 200 or more underlying companies, a quantitative approach could provide a meaningful advantage in reaching confidence for a firm proposal, especially as deals are often priced relative to reference data that is 1-2 quarters (or more) stale — data, which, in volatile markets, is a significant challenge to accurately update. By introducing advanced analytical tools, managers can create ways to generate real-time data updates, and to produce answers to timely questions in response to changes in market conditions.
One way to think of this is in parallel paths. First, there is the rigor of bottom-up due diligence, which does and should, for a variety of reasons, underpin the analysis of a deal. Second, there is what we might call the highly iterative part of deal analysis — a path that is always asking and evaluating questions. This path is asking a set of questions not only about the deal, but its strategic suitability to the firm, changing market conditions, etc. The answers to many of these questions are asked numerous times throughout a process, as a firm works toward confidence in a bid, positively or negatively. The best processes are often ones in which a question isn’t asked just once, but numerous times as the facts change. Answering some of these questions used to take a lot of manual computation — until advanced quantitative analytical tools introduced significant time saving efficiencies, as they can stress test a hypothesis or question by instantly providing an output.
In a market that is growing and yielding more data than ever, and especially within the context of a calendar year that still has the same number of days as it did before, advanced analytical tools that process vast amounts of data harvested from private markets can help managers find efficiencies in certain key areas of the deal-making process. These lead to impactful insights at remarkable speed and high degrees of confidence as they serve as a complement to a robust bottom-up diligence process. Below find two of several examples for how robust data and calibrated modeling can provide an additive perspective and scale decision-making.
Example 1: How NAV Nowcasting can provide real time insight into bid strategy
In response to these shared, systematic challenges described above, we built a tool that provides a timely top-down estimate of portfolio valuations based on public market returns. The tool leverages a model design which originated in meteorology, called “NAV Nowcasting”, which generates current estimates using observable data, in this case NAV changes using the public markets. This real-time regression model estimates a de-smoothed beta for the portfolio relative to its relevant public market proxy, accounting for the stage (buyout or venture), geographic region, and public/private composition of the portfolio as well as the valuation smoothing typically seen in private markets. The result is an estimated valuation change which is more accurate than industry standard NAV estimation approaches, such as rolling forward cash flows. Since the estimates are generated in real-time, the deal teams can react in the moment during the competitive bid processes.
Last year we used this tool to help us bid on a deal comprised of a diversified portfolio of more than 20 partnerships, which was a subset of a much larger portfolio the seller was seeking to divest. The seller was focused on securing a high price for the interests they sought to sell, and as such it was incumbent upon our deal team to shape the portfolio to balance acquiring what we believed to be high-quality partnerships at a price that we found compelling and the seller could accept. In this case, being able to estimate the current NAV and corresponding effective bid price was essential — a challenge we know investors face across the private markets industry. In addition to our bottom-up analysis and due diligence on the portfolio, we utilized the NAV Nowcasting tool to estimate the current value of the portfolio, which showed that public market appreciation in the 2+ quarters since the reference date could imply an attractive discount at close despite what appeared to be a higher headline price as of a stale reference date.
Conversely, the NAV Nowcasting tool is as valuable or arguably more valuable in a volatile, declining market environment. In 2022, where many public indices declined 25-30%+, the overall volatility and “denominator effect” pressures brought many private market sellers to the table. While attractive headline discounts were available in the market, our team was concerned that public market devaluations might translate to private market valuations. We utilized the NAV Nowcasting tool to estimate the value of portfolios for sale in this environment, which suggested that even with expected private market valuation declines, we would still be acquiring portfolios at double-digit discounts.
In the LP-led secondary example shown below, the model helped inform pricing strategy for a large, traditional secondary deal to complement the bottom-up, asset-level analysis.
NAV Nowcasting example
Source: HarbourVest QIS data. The above is modified from actual HarbourVest deal data for illustrative purposes.
Example 2: How simulation-based projections can help inform views on diversified project quality and value
In addition to being able to successfully navigate competitive bid processes, another challenge faced by investors is how they can accurately forecast cash flows (capital calls and distributions) within the context of a diversified deal. Many deals involve funds with a variety of vintage years, geographies, strategies, and sectors, which raises a range of questions, including:
- When will capital calls take place?
- When will distributions be received?
- How will NAV evolve over time?
- How will the portfolio evolve given current market conditions?
- How will the above factors change under a variety of market conditions, among other considerations?
To answer these kinds of questions — ones that are consistent across deals and investors — we developed a parametric simulation model that leverages historical relationships and current market conditions to forecast potential cash flow and return outcomes. Model parameters are calibrated using HarbourVest proprietary data and third-party providers and have the flexibility to incorporate current and future market trends. The output provides upside and downside scenario projections, enabling us to understand a number of market stress scenarios. This helps build confidence in the bid, and helps investors decide how to customize a portfolio to their needs and risk profile, among other things. These projections, in addition to fundamental bottom-up modeling, provide data-driven results in a dynamic and efficient manner, helping provide confidence in go-forward assumptions and overall portfolio quality.
This Cash Flow Forecasting model provides valuable insights in a few key ways:
Deal Screening: Particularly for highly diversified portfolios, the time and effort required to arrive at fulsome portfolio projections can be significant, requiring deal teams to update models for many funds on a bottom-up basis. Our automated model allows deal teams to project potential future portfolio performance in a matter of minutes, rather than days or weeks, allowing for a more efficient and informed decision on whether to pursue an opportunity in earnest.
Pricing/Returns Analysis: Once the decision has been made to pursue a transaction, the automated model is run in parallel to fundamental due diligence, providing more confidence in our bottom-up work due to the quantitative methodology employed.
Dynamic Stress Testing: While we seek to employ conservatism in our bottom-up, projections, it can be time consuming to manually stress test overall portfolio and market assumptions, including go-forward market return rates, downside scenarios, and distribution environments. Our forecasting model now allows for these variables to be quickly changed to provide increased confidence in portfolio performance under a variety of conditions.
Here is a recent example from an LP-led secondary transaction with 13 individual fund holdings.
Cash flow and return forecasting
Go forward cash flow forecast
Portfolio projected returns
Asset-level scenarios
In the above charts, the dotted lines reflect the 5th and 95th percentile outcomes, the shaded area represents the 25th to 75th percentile range of outcomes, and the solid line represents the median.
Source: HarbourVest QIS data.
Implications for private markets
In a secondaries market that is growing in size, with more asymmetrically available data, investors need new and more efficient approaches to help them maintain and enhance competitive advantage.
From our experience, being able to accelerate the speed and confidence in offering a firm proposal helps to secure deals early. Moreover, we have found that the application of advanced analytical tools, in ways that augment our established, fundamental diligence process, provide increased clarity and confidence in a fast-growing market of rising complexity. To that end, we have developed proprietary tools that deliver meaningful insights on private markets data — insights that are helping us and our clients to maintain a competitive advantage. Our highly experienced and growing Quantitative Investment Science team is constantly working with investment teams to develop new and unique insights.
If you would like to speak with a member of our Secondaries team or learn more about our Quantitative Investment Science (QIS) tools, please reach out.
Footnotes
Disclosure
HarbourVest Partners, LLC is a registered investment adviser under the Investment Advisers Act of 1940. This material is solely for informational purposes and should not be viewed as a current or past recommendation or an offer to sell or the solicitation to buy securities or adopt any investment strategy. The opinions expressed herein represent the current, good faith views of the author(s) at the time of publication, are not definitive investment advice, and should not be relied upon as such. This material has been developed internally and/or obtained from sources believed to be reliable; however, HarbourVest does not guarantee the accuracy, adequacy, or completeness of such information. There is no assurance that any events or projections will occur, and outcomes may be significantly different than the opinions shown here. This information, including any projections concerning financial market performance, is based on current market conditions, which will fluctuate and may be superseded by subsequent market events or for other reasons. The information contained herein must be kept strictly confidential and may not be reproduced or redistributed in any format without the express written approval of HarbourVest.
Nothing herein should be construed as a solicitation, offer, recommendation, representation of suitability, legal advice, tax advice, or endorsement of any security or investment and should not be relied upon by you in evaluating the merits of investing in HarbourVest funds or in any other investment decision.
