How data can help LPs decide whether to sell

What did you find in your research that can help LPs decide whether to sell their fund interest on the secondaries market?

Funds’ cashflows tend to correlate with performance. The timing of capital deployment and distributions, the time to break-even and the depth of the J-curve in negative territory give an indication of the performance of a strategy and a region for a given vintage year.

But instead of giving a precise figure, the analysis puts a year in a performance bucket such as “very high”‘, “high”, “medium” or “low”.

Instead of attributing a year to a performance bucket immediately, the method first excludes some of them. For example, after three years, there are hints that a given year will not be a high performer, or a low performer. Armed with this, LPs can answer two questions: is my single fund in line with this first analysis for the year? (Which sheds some light on whether this is a general trend or the fund manager’s specific performance.) And, if this is a “low performer” should I sell early to free up capital?

How can LPs tell whether the fund itself is a poor performer or if it is simply the vintage?

The analysis I undertook is good at assessing overall performance buckets for vintages, but is subject to having enough quality data. I could not really draw conclusions for Europe, for example, as the data I used was not reliable enough.

The conclusion holds true with individual vintages, and top and bottom quartiles for each vintage year. This means that macroeconomic environments are in fact materialised in the J-curves, and this is what LPs might want to assess before making the difficult choice to exit.

Why is it important to LPs to know early what the vintage’s performance will be?

LPs can use the information to feed in their risk management system. This might result in lower capital held for solvency or prudential reasons they know a fund will not make a loss.

If the fund is in trouble, it is important to know if the vintage as a whole is in trouble. You might end up with a fund which is not an outlier, and just fell in a bad year. Not dumping a bad performer might secure your seat in the next fund.

Going through a tough time with the GP, knowing that everyone else is in the same boat, can also give you the opportunity to negotiate a good deal or simply accept the macro situation. It might also prevent you from selling on the secondaries market and accepting a significant discount when there is no way up.

What correlation did you find between a fund’s performance and its J-curve and how can this help potential sellers?

Before two years of activity, correlations do not give any clear information about performance attribution. From years three to five, some performance buckets drop in terms of correlation.

From year six and on, the performance category to which the vintage year will most likely belong appears clearly. Though this is far from perfect, this method might resound differently depending on the LPs. The more hands-off ones might get out early and try to reallocate. More active ones will engage. Some LPs might buy a “low” performer at a discount and possibly accept a short term underperformance in exchange for a seat in the next fund.

This method, despite some shortcomings, is versatile enough to suit LPs in all their variety. It might prove to be a positive step in the march towards a more efficient private equity market, as LPs are given an extra tool to monitor and execute their own investment programme.