Is there persistence in private equity returns?
Is there persistence in private equity returns?
Our new research finds that 36% of private equity funds that were in the top quartile for one vintage were also top quartile for the general partner’s (GP’s) next. 64% were in one of the top two quartiles. And only 16% dropped to the bottom.
This is according to our analysis of 3,512 private equity funds raised since 1980 (only GPs with at least two funds are included, and only funds with at least seven years are included, as performance of younger funds is still fluctuating and not definitive).
Performance disclaimers repeatedly tell us that past performance is not a guide to the future and may not be repeated. And that is true. But is there a nuance to consider between public and private equity funds on this front?
Our analysis suggests that the past performance of private equity funds may provide some useful information to help think about how they might perform in future.
This is a very different picture to what we see with public equity funds. A recent study found that there was a greater chance that a public equity fund in the top quartile for one five year period dropped to the bottom for the next, than maintained its standing.
It’s a similar story with the worst performing private equity funds. GPs with a fund ranked in the bottom quartile tend to have their next in the bottom quartile too. Relatively few jump to the top.
Digging down a level, our research shows this holds strongly for funds investing in both North America and Europe, and for both buyouts and venture capital. Importantly, this persistency is statistically significant (see Description of our analysis, at the end, for more details).
It also holds for funds investing in Asia, but not to the same degree of statistical significance – partly influenced by there being fewer funds in this category, which limits the conviction with which we can draw firm conclusions.
Large funds have been less able to maintain good performance than small and medium-sized ones
When we analysed funds by size, the results were striking. Good performance persistence is greatest among small funds, strong and significant among medium-sized funds, but weak among large ones.
Bad performance, however, is persistent across all sizes. i.e. a manager with a bottom quartile fund is likely to remain bottom quartile with their next fund.
This is an important result for private equity investors, especially given that many allocate to large funds run by large managers. Poring over their past performance may be less worthwhile than you realise – the performance disclaimer was right all along. But it can be more useful when looking at the long tail of small and medium-sized funds which make up the bulk of the industry (in terms of number of funds).
Our analysis provides evidence of performance persistence in private equity – in all major areas apart from large funds. Top performers have been more likely to perform well in their next fund. Poor performers have been more likely to continue toiling.
You should never make an investment decision based on past performance alone. But this suggests investors may not want to ignore it either.
Description of our analysis:
The data used in this study comes from the Preqin Performance database for Private Capital. This is subject to a degree of selection bias as voluntary contributions by fund managers are unlikely for poorly performing funds. However, Preqin seeks to limit this by using a range of other approaches, including the Freedom of Information Act (FOIA) and systematic processing of regulatory filings, press releases, news and websites.
There are 9,834 funds in the Preqin database. To be included in our analysis, funds must be either ‘Closed’ (fundraising is closed) or ‘Liquidated’. Only funds older than seven years are analysed. This is to avoid analysing funds whose performance is still fluctuating and not definitive. Only funds with vintage year after 1980 are analysed. Either the net IRR or the net MOIC of each fund must be available (ideally both). Funds comprised of a single asset and GPs with less than two funds are not considered. Applying these filters reduces the number of funds in our universe to 3,512.
Our universe is influenced by survivorship bias as the probability of a GP launching a second fund will be dependent on sufficiently good performance from its first fund. As we are only looking at GPs with at least two funds, the funds in our universe are likely to be higher quality then the universe overall.
Funds are allocated to performance quartiles based on the combination of their IRR and MOIC ranking, compared with benchmark peers (regions, strategies, fund sizes or all together). For funds which have reached the end of their life, this is based on realised returns. For funds which are still going, it will be partly based on unrealised returns, which are based on estimated values.
We form contingency tables and run chi-squared tests to test for statistical significance for different slices of the data (regions, strategies, fund sizes or all together). The null hypothesis is that the performance of a fund has no association with the performance of the next fund. P-values are calculated as a measure of statistical significance.
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