Risk-adjusted returns. Three words that are probably in the track record section of every private equity pitch deck you’ve ever seen. Taking a step back to think about this, though, do we really have a well-defined idea of what risk in private equity really is, and how this relates to the returns generated by the asset class?
Prof. Oliver Gottschalg began our recent LP-only webinar with this question, laying out the stepping stones he would take to get to a framework that not only delivers meaningful insights into how risk and returns are linked in private equity, but reliably predicts outperformance from one fund vintage to the next.
Defining returns and redefining risk
Over years of analysis of the HEC Buyout Database, Gottschalg Analytics has developed the PERACS Alpha metric. This gauges annualised outperformance above the broad global stock market, and is persistent across vintage years, so past top-quartile performers are more likely to again be in the top quartile in the subsequent fund.
When it comes to risk, there’s not a standard metric that adequately captures meaningful risk in private equity. Loss ratio and volatility over time may work well in other asset classes, but in PE we don’t learn anything from them that helps in fund selection as they aren’t indicative of future returns.
Gottschalg discussed how he as addressed this problem by adopting the Lorenz Curve and Gini Coefficient (that those of you that were paying attention in your Economics classes will remember is used to measure inequality in wealth distribution). In this application, a portfolio of deals is plotted from least to most successful left to right on the x-axis and returns are plotted on the y-axis. This curve gives a measure of risk that captures both downside risk, how all loss-making deals impact the portfolio returns, as well as upside risk, where returns are heavily skewed to the upside by one exceptional deal. Benchmarking this over fund vintages shows that a fund’s risk coefficient is highly correlated with that of the subsequent fund. We now have a measure of risk that is predictive of future performance.
With these definitions in place, Gottschalg demonstrated that risk and return are negatively correlated in PE; skilled managers generate higher returns and minimise loss-making deals, and so have lower risk coefficients.
A risk-return framework for due diligence
In the final section of the webinar, Gottschalg introduced the Risk-Return G Score, a quantitative measure of the attractiveness of a single PE fund. Plotting fund returns on the x-axis and its stability coefficient (1 minus the Risk Coefficient, to show lower risk higher up the axis), the G Score of the fund is the percentage of funds in the sample that are below (riskier) and to the left (lower returning) of the fund in question.
To answer how predictive this metric is assessing potential fund performance, a dataset that includes 213 pairs of predecessor and successor funds with a minimum of five realised deals was used to measure this correlation. The data shows that the subsequent funds of top quartile G Score funds delivered an outperformance of 5.1% in PERACS Alpha, where top decile predecessor funds outperformed by 11.3% in absolute terms, meaning alpha would have improved to 16% from 5% by using this selection metric.
The bottom line
It’s easy enough to understand returns in private equity, but risk is more complex. Using Prof. Gottschalg’s PERACS Risk Coefficient gives a single measure that captures both negative and positive risk, isolating manager skill.
Combining this measure of risk with returns over a sample of funds provides a predictive indicator of future fund performance: managers of top decile funds generate on average 11% more alpha in subsequent funds, a significant performance boost. Applying this framework enables LPs to identify funds with the potential to outperform.
This blog is a summary of IPEM's exclusive webinar series for LPs in partnership with Gottschalg Analytics, exploring critical topics for LPs in today's private equity market.