The Finance industry was one of the first to understand the potential of the immense quantity of information it generated, and to adapt its processes accordingly with the view to make more money, faster and with fewer resources. Trading algorithms have existed for decades and have reached such levels of sophistication that progress is measured in milliseconds. Optimizations now often rely on minimizing cable length between machines and network. But chasing milliseconds supposes one critical ingredient – liquidity - so that buying and selling orders can be fulfilled quickly.

Illiquid markets operate at a completely different pace and cable length could never be a good excuse to miss out on a Private Equity deal for example. In this type of market, executing orders at the speed of light makes no difference but finding liquidity can be critical to reach the objective of making more money, faster and with fewer resources. And data can be key to achieve this given illiquid markets are often the least transparent in terms of information.

Although relatively unknown to the general public, the Private Equity market is far from negligible: each year since 2012, between 200 and 300 billion dollars have been raised worldwide by funds buying out private companies using debt. And every year since 2015 except in 2016, more than 500 billion dollars have been deployed in this type of transaction.

In this industry, the market convention is to express company valuations (EV) as a product of operating profit before depreciation and amotization (EBITDA) and a multiple (EV Multiple). The profit made by these funds on a transaction comes from 3 main sources:

• Deleveraging through cash generation during the holding period
• Increase in EBITDA: which can result from concrete actions by Management teams, but also from favorable market conditions.
• Increase in multiple: mainly influenced by market dynamics since all companies with a similar profile (size, sector, etc.) can expect to have a similar acquisition multiple

Breakdown of value creation in Private Equity (illustrative)

Any data-driven approach in the Private Equity industry will necessarily involve monitoring these 3 sources of value creation. And it is essential to track a large enough number of companies so that any market dynamics identified can be deemed statistically significant. That can be a major hurdle because Private Equity funds generally have very concentrated portfolios with less than 30 assets, often less than 20 and sometimes less than 10. Obtaining representative samples would require investments in tens of funds, potentially hundreds. Even assuming that capital is not a limiting factor, gaining exposure to such a large number of funds can take years given the relatively long cycles in the industry : for a given asset manager, a fund is typically raised every 3-to-5 years, deployment takes 3-to-5 years and harvesting is at least as long.

The solution to this problem can be found in an even lesser-known niche of the Private Equity market: the segment of secondary transactions on the PE portfolios. In this segment, some transactions may be initiated by Private Equity funds seeking to return capital to their original Limited Partners (LPs) without necessarily having to sell the illiquid assets. But c.70% of the time, it is the LPs themselves who initiate the sale of their PE funds exposures, primarily to rebalance their balance sheets between asset classes, sometimes in connection with regulatory constraints. Secondary transactions volumes have soared over the past 20 years to reach nearly 90 billion dollars in 2019 (data updated in August 2021).

There are very few buyers on this market as only about ten platforms - each with $5bn+ funds - capture the bulk of the volume. Each transaction can represent several billion dollars and involve exposures to hundreds of funds and therefore thousands of companies. These transactions are the fastest way to build up a PE portfolio that is both mature and extremely diversified.

Leveraging the large volume of information produced by these massive platforms, statistically significant conclusions can be drawn, which improves visibility on future cash flow in particular when it comes to M&A transactions on underlying companies. Higher returns are an indirect consequence of improved cashflow visibility as it allows for incremental leverage, even marginally.

In levered secondary transactions on PE portfolios, the return enhancement effect is even more noticeable for a lender taking a data-driven approach. Indeed, a lender can, at the same time, reduce the relative risk and increase returns:

• Assuming that a lender would receive a market interest rate for a loan secured by this type of portfolio¹ irrespectively of their data-analysis capabilities, the better the visibility on future cashflows, the lower the risk associated for a given remuneration.
• Lenders are typically repaid in priority out of the sales proceeds from the underlying companies (a mechanism known as cash sweep). A lender can therefore “cherry-pick” portfolios with fast repayment profiles. As a lender can expect to collect an upfront fee at inception of each transaction (from 0.5% to 1.0% of the facility amount), the faster the loan is repaid, the shorter the period over which the fee is amortized. So the better the performance.
• Provided that sophisticated information systems are implemented, including a certain degree of artificial intelligence, the data-driven approach also allows to minimize the operating costs associated with the origination and management of the loan portfolio.

This type of approach is becoming the norm among major Secondaries players, that now often finance their portfolios acquisitions with debt. However, on the lenders’ side, data-focussed players are yet to emerge. But let’s trust the financiers not to miss out on this opportunity for too long...