Predicting Top Quartile Private Equity Performance
Predicting Top Quartile Private Equity Performance
October 6th, 2017
“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 private equity?
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.
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).
Interestingly, the only place where Harris et al. find persistence is among the “lower end of the performance distribution.”
For investors, this presents a significant challenge in their manager selection. Even though funds have realised top quartile performance, a thorough due diligence process must be carried out to uncover potential risks.
“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
This is not the only research that highlights the challenge investors face in predicting top quartile funds, either.
NAVs during fundraising poor indicator of final 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).
Figure 2. IRR Development of an Exemplary US Buyout Fund., Figure 3. Cumulative Abnormal Changes in NAVs
(Source: How Fair are the Valuations of Private Equity Funds?, Jenkinson et al, 2013)
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.
Predicting Top Quartile Performance
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.
This can include analysis such as:
- Valuation bridges
- Gauge whether value was delivered through operational improvement, market dynamics, financial engineering and/or M&A activity and how this measures up to a manager’s proposed strategy.
- Sensitivity Analysis
- Understanding what deals have driven a fund manager’s performance and how sensitive the fund level performance is to them is critical. This can be done through a simple exclusion of specific deals based on IRR, TVPI, size etc. to remove outliers.
- More sophisticated approaches include the use of box plots, return curves and impact charts to determine what proportion of deals have had a positive or negative impact on performance.
- Performance attribution
- Private equity is a people business so understanding the history, skillset, and performance of the current team is critical.
- Investigate if strong performance in early funds by retired professionals propped up an overall track record, how have current team members impacted deals, what succession plans are in place and more.
- Integrating both quantitative data and qualitative data can help you effectively evaluate a fund manager’s team. “Data is important – it is the integration of the quantitative assessment and qualitative work that get you to the end conclusion,” advised one industry practitioner in our whitepaper.
Explore the data and considerations behind leveraging this data and analysis to enhance fund selection in our whitepaper “Enhancing Private Equity Manager Selection with Deeper Data”