Predicting Top Quartile Private Equity Performance
“Past performance is not an indicator of future results.”
A well-known, well-used adage in the investment world, but how applicable is it to top quartile private equity funds?
According to a number of research studies on the asset class, extremely.
One of the first studies to investigate the persistence of PE performance was published in 2014 by Robert Harris, Tim Jenkinson, Steven Kaplan and Rudiger Stucke. Their research found top quartile persistence among private equity managers is low: only 19% of buyout funds raised after 2001 that were a successor to a top quartile performer have repeated this level of performance.
Interestingly, the only place where Harris et al. find persistence is among the “lower end of the performance distribution.”
McKinsey carried out their own research on this topic in 2017, using more recent data than the aforementioned study: McKinsey analysis is based on vintages from 1995 through 2013, while Harris et. al used data from 1984 through 2008. McKinsey found top quartile persistency to be even lower with just 12% of buyout funds repeating top quartile performance, the result of a steady decline since 1995. The research also finds manager’s persistency of performance in general to be low (figure 1).
For investors, this presents a significant challenge in their manager selection: high-level performance numbers alone can’t be relied on for prudent investment decision making.
“This shift makes it quite difficult for even the most astute LPs to predict how fund managers will perform.”
McKinsey, Global Private Equity Report 2017
NAVs during fundraising poor indicator of final performance
The lack of persistence of performance is not the only data point that highlights the challenges facing LPs in using high-level numbers to predict performance.
Research published by Jenkinson, Sousa and Stucke in 2013 found no evidence of correlation between manager’s NAVs during fundraising, and the realized performance of those investments.
Jenkinson et al. report that while private equity valuations are generally conservative and understate subsequent distributions over the life of a fund, this does not hold true when follow-on funds are being raised. Their research suggests that there is no statistically significant relationship between IRRs reported on fund n – 1 at both four and two quarters before a manager holds a first close on fund n, and the final performance of fund n – 1.
While Jenkinson et al. highlight this using what they even deem to be an extreme example (Figure 2) their results suggest that it is “by no means an isolated case” as displayed in the cumulative NAV data (Figure 3).
This may not be intentional or artificial NAV inflation by the managers, but could simply be a result of managers choosing to return to market when they can point to a strong track record.
One Step Closer to Predicting Top Quartile Performance
The difference between top and bottom quartile fund performance in private equity is far greater than public equity funds; 12.9 percentage points vs. 1.5 percentage points. So the cost of a bad decision for LPs is significant, and consequently increases the need for a process and practice that is tailored to maximizing the opportunity for top quartile returns.
To combat the issue of NAVs presented during fundraising not reflecting final performance, Jenkinson, Sousa and Stucke suggest LPs should carefully consider the weight they put on IRRs reported by managers during fundraising that contain portions of unrealized investments. They also suggest using public market equivalent analysis instead of IRR in this evaluation, as their research showed that this increases the predictability of future performance significantly.
In addition to assessing metrics other than IRR, investors also need to look beyond headline numbers in their assessment of a private equity fund manager. These metrics alone are proven to not be reliable indicators of future success.
Leveraging and analyzing granular data on performance at the fund and portfolio company level is critical to understand the true drivers of a manager’s returns and how they align with the new fund’s strategy.
Explore more of the portfolio analytics techniques leading LPs are using to look past the headline performance numbers, understand true drivers of a GP’s returns, and maximize the opportunity for top quartile investment decisions.
The logic and use case for Zero- and Neutrally-weighted IRRs to combat the traditional calculation’s flaws.
Return Curve Analysis
How this adaptation of the Lorenz Curve can help you understand return concentration and manager risks.
PME Applications and Considerations
The background to the burgeoning analysis technique, and the key questions and considerations to consider when choosing a methodology and index selection.