Size matters – small is beautiful 2.0
Unigestion’s private equity team have again joined forces with Professor Oliver Gottschalg, HEC Paris and PERACS, to continue their research on the optimal construction of private equity portfolios, this time focusing on global small-cap portfolios.
- Small is still beautiful – for private equity an optimally diversified global small-cap portfolio should outperform a more concentrated large-cap portfolio
- An optimally diversified global small-cap portfolio should deliver a similar performance, with less risk, to a comparable regional portfolio
- Historically, an optimally diversified Private Equity portfolio did not lose money
- Diversification and selection skills reduce volatility
- A diversified global small-cap portfolio can optimise the J-curve – intelligent portfolio construction can eliminate the J-curve
- Cycles matter: global portfolios deliver more robust performance; returns by regions are not fully synchronised
Introduction to our private equity study
In a study conducted in May 2015, Unigestion joined forces with Professor Oliver Gottschalg – an associate professor of strategy and business policy at the HEC Business School Paris whose research focuses on private equity – to challenge opinions held about the optimal construction of private equity portfolios. This included the consideration of choices about the number of funds, the size of funds and their geographic focus.
To do so, we leveraged data from alternative assets database Preqin, representing a decade of vintage years from 1998 to 2007, and created simulations of 1,000 random portfolios using a Monte Carlo methodology (figure 1). These simulations ranged from 1, 5, 10, 15, 20, 25, 30, 50 to 100 underlying funds. This approach was initially applied to the overall data set and then to specific sub-samples e.g. selection ability, geographic focus and fund size. For each of these portfolios, we then calculated the average return of all underlying funds, as well as the return dispersion (figure 3).
The return dispersion was calculated in two ways. Firstly, we used the traditional approach of measuring the observed portfolio returns between a minimum and maximum range. Secondly, we applied a more advanced approach, developed by Professor Gottschalg known as “PERACS Risk Curve”, to graphically capture and quantify the return dispersion of these portfolios, as a measure of risk.
Figure 1: Data set
We extended this research in July 2017, again working with Professor Gottschalg, to analyse optimal global small-cap portfolios, consisting of European, North American and Asian funds, and considered how such portfolios would perform during the course of a lifetime (cycle analysis). The data came from Preqin once more, but included four additional vintage years – covering 1998 to 2011 – as well as Asian buyout and growth funds. We used the same methodology as previously, but this time each portfolio had a 3-year investment span. This was achieved by randomly selecting an anchor vintage year for each and then adding the prior and subsequent years. In a change to the earlier study, we also leveraged quarterly cashflow data from Preqin which helped us gain insight into the life cycle performance of global small cap portfolios rather than focusing purely on their end of life performance.
Figure 2: Methodology (Portfolio Simulation)
Significant risk reduction through diversification
Our findings underlined the benefits of diversification that are already widely acknowledged among traditional asset classes. This observation was confirmed by the PERACS risk curves: the greater the number of funds in a portfolio, the flatter the risk curve, which indicated a more equally distributed return. We found that a portfolio only needed to have a minimum of 10 funds for even the worst performers from our sample to still deliver a positive return. However, the benefit of diversification for a portfolio was shown to reduce as the number of underlying funds became much larger. Indeed, these larger portfolios also had lower upside potential. On the other hand, the inclusion of Asian growth and/or buyout funds introduced further upside potential, albeit with more risk in the form of performance volatility. In summary, our study found that the optimal global private equity portfolio contains about 30 funds and should include a limited number of Asian funds (figure 3). It is demonstrated that a global portfolio of this size offers strong downside protection while retaining an attractive level of upside potential. This optimal portfolio size thus created the basis of our further analysis.
Figure 3a: Average portfolio return & return dispersion by portfolio size
Figure 3b: PERACS risk curve
Small-cap portfolios outperform portfolios of large-cap funds
We found that a global portfolio of 30 underlying small-cap funds outperformed global portfolios of larger-sized funds in terms of peak and average performance. From a risk perspective, our study found that no global portfolios experienced losses, irrespective of fund size, demonstrating the benefits of diversification.
Figure 4: Diversified global portfolios by fund size
A diversified global small-cap portfolio outperforms a concentrated large-cap portfolio
In practice, the building of a global small-cap portfolio also needs to be analysed at a geographic level. Since the maturity of private equity markets varies by region, it also makes sense to vary fund size classification by region. We therefore defined a global small-cap portfolio as consisting of 15 European buyout funds between $0-500m, 10 North American buyout funds between $0-1,500m and 5 Asian buyout or growth funds of any size. We also needed to consider the practicalities of investing in small-cap funds versus large-cap funds that investors face in terms of resource and volume limitations, relating to how much capital they are able to invest per fund. Essentially, an investor can choose to access either a concentrated portfolio of large-cap funds or a more diversified portfolio of small-cap funds. For this analysis we defined a concentrated large-cap fund as consisting of 10 underlying funds.
Our results showed that a diversified global small-cap portfolio outperformed a concentrated large-cap portfolio in terms of peak and average performance. It was also less risky as the diversification provided strong downside protection. The difference became even greater if only the upper median portfolios (i.e. a portfolio consisting only of 1st and 2nd quartile funds) were considered.
Figure 5: A diversified global small-cap portfolio versus a focused large-cap portfolio
What’s new? The introduction of dynamic risk parameters
So far, we have examined the performance characteristics of various portfolios by studying the maximum and average return. We also felt it was important to consider risk by analysing the minimum return and the PERACS risk curve. However, both of these risk measures are static and represent a snapshot of risk at a certain point in time. In this new study, we added a dynamic risk dimension by considering quarterly cash flow data of underlying funds over a prescribed period of time. Specifically, we looked at the return volatility over time by analysing the quarterly value changes (defined as the sum of all distributions minus the sum of all capital calls in that quarter plus the change in NAV versus the previous quarter).
The simplicity of the concept of return volatility has helped to make it a popular risk measure among the finance community. Indeed, the elegance of having one figure based on current or final performance data, and from which the level of risk can easily be derived, is understandably convincing. Yet, it is not without weakness as strong fund performance can sometimes mask large swings in value during the lifespan of that fund. Hence, we further investigated volatility by measuring the lowest value of a given portfolio during its life time (=low watermark).
Figure 6: Risk and return matrix
A global portfolio reduces return volatility over time
Our study demonstrated that the greater the number of funds in a portfolio, the lower the return volatility – again highlighting the benefits of diversification. We found that the lowest volatility was achieved by a portfolio containing 30 underlying funds (figure 7a). We took a closer look at this size of portfolio to compare the distribution of the quarterly value changes for both regional and global portfolios. Figure 7b ranks these quarterly value changes by size. Through this analysis, we observed that the value development is smoother for global portfolios than for regional portfolios (i.e. the change in quarterly value for global portfolios is generally smaller, irrespective of these changes being positive or negative). Yet, this smoothness does not say anything about the level of performance ultimately achieved by the portfolio. In figure 7c, we plot volatility against performance and found that a portfolio consisting of upper median funds (i.e. 1st and 2nd quartile funds) not only delivered a higher return, but achieved this with a lower level of volatility over time. In short, fund quality leads to lower volatility!
Figure 7a: Diversification reduces return volatility over time
Note: the portfolio measures in this analysis is the ‘median’ portfolio per sample size
Figure 7b: A global portfolio reduces return volatility over time
Notes: Median portfolio (based on the return volatility over time) in each region in the market $0-500m. Global portfolio consists of 15 European buyout (market size $0-800m), 10 North American buyout (market size $0-1500m) and 5 Asia buyout or growth funds; Underlying cash-flow data denominated in USD creating a possible FX bias. Quarterly value changes ranked by size (from the smallest to the largest)
Figure 7c: Selection skills bring lower volatility
A diversified global small-cap portfolio minimises the j-curve
As mentioned previously, we were also interested in establishing the lowest value point of a portfolio during its lifetime. Figure 8 shows the cumulative quarterly net gains of a global small-cap portfolio, which delivered a median-level performance. During the observed period, the lowest value of this portfolio stood at -3.5% (of a commitment of $10m). This is very low given the nature of private equity funds, which typically generate losses at the beginning of their life cycles when initial set-up costs and management fees have not yet been offset by the development of value in the underlying portfolio – the j-curve effect. In practice, the j-curve can be reduced by intelligent portfolio construction (i.e. by including secondary investments, late primary, good quality funds, etc.). The lowest value beyond such a j-curve can also be analysed. Assuming the recovery of these initial costs is achieved after four years (or 16 quarters), a median global small-cap portfolio should never dip below water again.
Figure 8: Quarterly value development of a diversified global small-cap portfolio
Cycles matter: global portfolios deliver more robust performance
Finally, we were curious to understand the effect of adding Asian funds to a global portfolio, with an aim to understanding how this would influence the stability of returns. Moreover, we considered how a global small-cap portfolio performs by vintage when compared to a regional small-cap portfolio. Our results (as shown in figure 9) indicated that global small-cap portfolios were more robust and benefitted from the asynchronicity of regional performance. In particular, returns from European and Asian funds seem almost negatively correlated. During the vintage years of 2000-2004, European portfolios performed strongly while Asian portfolios suffered, whereas the opposite could be observed during the vintage years of 2005-2008.
Figure 9: Cycles analysis: global portfolios vs regional-focused portfolios
A research study conducted by Professor Oliver Gottschalg (HEC Paris and PERACS), Mark Zünd (Unigestion), Partner – Private Equity, Co-Head Investments, Paul Newsome (Unigestion), Partner – Private Equity, Co-Head Investments, Dr Ralf Gleisberg (Unigestion), Partner – Private Equity, Ramun Derungs (Unigestion), Associate.
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