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Private Equity Insights, June 2013

Asia Private Equity Institute (APEI), SMU

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Asia Private Equity Institute (APEI) Private Equity Insights Q2 2013

Contents

• An Introduction to the APEI • The Value of Human Capital in Private Equity by Melvyn Teo • Update on the Institute’s Activities

An Introduction to the APEI The vision of the Asia Private Equity Institute is to be the premier research and knowledge hub for private equity and venture capital activities in the Asia Pacific region. According to Preqin, “Asia PE funds raised $62 billion in 2011, double the $31 billion raised in 2010. This 100% growth rate was approximately twice that of the global PE fund raising market, and Asia funds have now taken over Europe as the 2nd largest fund-raising market after North America.” Despite the surge in interest for private equity in Asia, most academic and practitioner research on private equity remains focused on US and Europe. As the first Asia-focused academic research centre on private equity and venture capital, APEI seeks to fill this knowledge gap. To do so, APEI will be an integrated platform that (i) conducts high quality academic and applied research on private equity and venture capital (ii) educates practitioners and disseminates new ideas and methods, thereby raising the standards and level of professionalism in the industry (iii) elevates the profile of the private equity and venture capital industry in Singapore and Asia. Some of the activities of the institute include a quarterly private equity insights newsletter that parlays academic findings into key lessons for general partners and limited partners, a closed door quarterly investment roundtable which facilitates an exchange of investment ideas between key industry players in an intimate setting, and an annual private equity conference where academics, general partners, limited partners, and industry experts discuss and debate topical issues that resonate with the industry.



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The Value of Human Capital in Private Equity Melvyn Teo1 Executive summary We explore the value of human capital in private equity. We find that funds with financial, technical, management, and networking skills outperform funds without such skills. Specifically, funds with top quintile expertise levels deliver IRRs that are 5.4 percent per year greater than do funds with bottom quintile expertise levels. Funds with many experts are also able to generate 50 cents more for every dollar invested and outperform public markets by 31 percent more relative to funds with few experts. Experts are more valuable for buyout, venture capital, and infrastructure than for growth and real estate. Funds that specialize in few industries and funds that are more hands-on in their investment approach tend to benefit more from partner expertise. While a host of skills are relevant in private equity, evidence suggests that strategic management skills are more important than technical expertise and industry knowledge. Finally, large funds are able to ameliorate some of the diseconomies of scale they face by employing more experts. What is the value of human capital in private equity? How important are financial, technical, management, and networking skills in private equity? Given the surfeit of professionals with investment banking and management consulting experience in the industry, clearly financial acumen and strategic management expertise are prized in private equity. But do savvy financial and management skills really translate to better investment performance? If so, what other skills sets are important in private equity? General partners who are managing teams of investment professionals in private equity firms as well as limited partners who are evaluating those teams benefit from knowing the answers to these questions. In this issue of the private equity insight, we measure the impact of expertise on the investment performance of private equity firms by leveraging on multi-dimensional fund level expertise information. By doing so, we also provide insight into how private equity firms add value to their portfolio companies. Our analysis centers on the Preqin private equity performance and cash flow databases. The performance database includes information on fund IRRs and multiples of invested capital while the cash flow database includes monthly capital calls and distributions information for a subset of funds in the performance database. The Preqin database also offers information on fund characteristics such as investment region, vintage, investment type, fund expertise, etc. Preqin obtains its data primarily from public filings by pension funds, from Freedom of Information Act (FOIA) requests to public pension funds, and also voluntarily from GPs and LPs. Therefore, the fund sample is not entirely free of self-selection bias. Nonetheless, Harris, Jenkinson, and Kaplan (2011) argue that the consistency of returns from three distinct datasets: Burgiss, Preqin, and Cambridge Associates, despite very different sample selection criteria, suggests that they are likely to represent reliable measures of private equity performance. Moreover, most of our analysis will focus on differences in the cross-section of fund performance. Therefore, our results are less affected by selection issues unless that there are systematic differences in sample selection across the fund groups that we focus on, e.g., high versus low expertise level funds.

1 Melvyn Teo is Professor of Finance and Co-Director, Asia Private Equity Institute (APEI) at the Singapore Management University. E-mail: [email protected]. Phone: +65-6828-0735.



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Our analysis covers private equity funds that invest around the world. There are 4,111 funds in Preqin with performance data as of March 2013. Panel A of Table 1 reports the number, average size, IRR, investment multiple, and public market equivalent (PME) for these funds.2 PME is calculated using the method of Kaplan and Schoar (2005). First, all cash flows from (distributions) and to (capital calls) the fund are discounted using the total realized return of the MSCI World Index from the fund’s inception to the distribution date as the discount rate.3 The discounted outflows and inflows are then summed to obtain the total discounted outflows and total discounted inflows to the fund. PME is simply the ratio of total discounted outflows to the total discounted inflows and reflects the after fee return to private equity investments relative to public equities.4 As shown in Table 1, the average fund in our sample delivers an IRR of 11.94 per year, generates cash flows that are 1.54 times that of invested capital, and outperforms public equities by about 20 percent. Table 1: Description of data

The database includes self-reported information on whether a fund has financial, technical, industry, operational, management, strategic, marketing, recruiting, and networking expertise. We analyze both the individual expertise score broken down by skill set as well as a fund level aggregate expertise score that measures the total number of different skills that a fund reports. Since there are nine types of skills captured by the expertise information, the fund level aggregate expertise score varies from zero to nine. As reported in Table 1 the average expertise level is 2.90. Also, buyout and venture capital funds tend to have

2 The number and average fund size reported in Table 1 are for funds with IRR information. The number of funds with multiple information is comparable while the number of funds with PME information is significantly lower. 3 Kaplan and Schoar (2005) use the S&P 500 return for the calculation of PME as they investigate US focused funds. 4 Ending NAVs are treated as true values as in Kaplan and Schoar (2005).



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the highest expertise levels. In addition, expertise levels appear to have fallen over time. The average expertise level for the pre-1996 vintages of 3.27 dominates the average expertise level for the 2006-2010 vintages of 2.61. General partners investing in the United States, Europe, Asia, and the Middle East and Israel appear to be more skilled than general partners focused on Africa and Latin America. Figure 1: Number of funds with expertise

Figure 2: Distribution of fund expertise levels



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Figure 1 graphs the number of funds with expertise. Funds are most likely to report that they have networking skills. The next two most common skill sets are financial and strategic expertise, consistent with the preponderance of general partners with investment banking and management consulting experience in the private equity industry. Technical and marketing skills appear to be less common amongst the funds in our database. Turning to the distribution of fund aggregate expertise levels in Figure 2, we find that there are a large number of funds that do not have any expertise in the nine areas considered. Conditional on reporting at least one type of expertise, funds are most likely to report expertise in five different areas. There are concerns that, for marketing reasons, some funds may attempt to game the system and report that they possess expertise in all nine areas considered. It is comforting to note from Figure 2 that there are very few funds that report having skills sets in all nine areas and that therefore unsurprisingly, our basic inferences are unchanged when we remove funds with expertise level equal to nine from the analysis. To understand the impact of fund expertise on investment performance, we first sort funds based on whether they possess expertise in each of the nine individual skill areas and evaluate the spread in performance between experts and non-experts. The performance metrics that we analyze include fund IRR, investment multiple, and public market equivalent (PME). The results in Table 2 broadly indicate that expertise in each of the nine skill areas is important for fund performance. For example, funds with financial expertise (who presumably have ex-investment bankers and/or ex-financial consultants on board) are able to outperform funds without financial expertise by 3.67 percent per year. They also deliver on average 26 cents more for every dollar invested, and outperform public equities by 16 percent per year more than do funds managed by general partners who lack financial skills. Table 2: The performance of funds with and without expertise

The inferences from analyzing fund aggregate expertise levels mirror those from analyzing individual expertise. When we sort funds into five equal number groups or quintiles and report their performance in Table 3, we find that funds with high expertise levels consistently outperform funds with low expertise levels regardless of the performance metric used. Funds in the top expertise quintile are able to deliver IRRs that are 5.40 percent per year higher than those delivered by funds in the bottom expertise quintile. Similarly, for every dollar invested, funds in the top expertise quintile return 50 cents more than do funds in the bottom expertise quintile. Finally, skilled funds outperform public equities by 31 percent more than do unskilled funds. These results underscore the value of human capital in private equity.



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Table 3: Funds sorted by expertise level

Next, we sort the funds in our database into 100 groups based on fund IRR. We define group IRR and group expertise as the average IRR and fund expertise level, respectively, of the funds belonging to a group. Figure 3 plots group IRR against group expertise (where groups are represented by bubbles on the chart). The pattern of bubbles and the polynomial line of best fit illustrate the positive relationship between expertise level and IRR. Figure 3: Expertise levels for funds grouped by IRR

Does the value of human capital vary across investment regions, fund types, and vintage years? In Tables 4, we first sort funds by investment region and then by fund expertise level. Doing so allows us to compare the IRR of high expertise versus low expertise funds within each investment region. We find that highly skilled funds outperform lowly skilled funds in all of the investment regions considered save for Africa. In addition, the expertise spread is statistically significant at the five percent level for three of the seven investment regions.



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Table 4: Funds sorted by investment region and expertise level

In Table 5, we explore the effects of expertise on the performance of various fund types. We find that skilled general partners tend to outperform less skilled general partners in buyout, infrastructure, venture capital, and to a lesser extent, real estate. In contrast, the performance of growth funds appears to be largely unrelated to the types of expertise reported in our dataset. The statistically reliable performance spread between skilled and unskilled funds for buyout and venture capital may reflect the financial and management skills needed in buyout to understand debt covenants and effect corporate restructurings as well as the networking and marketing skills needed in venture capital to connect startup firms to suppliers and clients. Indeed, we find in untabulated results that financial and management expertise are especially relevant for buyout funds while marketing and networking skills are particularly helpful for venture capital firms. Table 5: Funds sorted by fund type and expertise level

Is the impact of fund expertise robust across time? We find in Table 6 that the expertise spread is positive for all of the vintage year groups considered. However, there is some evidence that time has moderated the impact of expertise on fund performance. This is consistent with the view that as the number of funds and assets under management in the private equity industry increase, it becomes harder for private equity funds to generate alpha in general. Indeed, it is clear also from Table 6 that average fund IRRs have decreased in the recent vintage years.



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Table 6: Funds sorted by vintage and expertise level

Are our results simply a by-product of capacity constraints at the fund level? If small funds are on average highly skilled while large funds are on average lowly skilled then capacity constraints may well explain the apparent outperformance of skilled versus unskilled funds. To understand the relationship between fund size and expertise, we first group funds into size quintiles based on assets under management and then compute the expertise spread within each size quintile. We find that the expertise spread is remarkably strong across the various size quintiles suggesting that capacity constraints are not driving our findings. A plot of fund size against fund expertise reveals that contrary to the concerns raised earlier, very large funds tend to have more experts on board than do other funds. This is unsurprising given that general partners with significant expertise are also more likely to attract significant capital. Table 7: Funds sorted by size and expertise level

Figure 4: The relationship between fund size and expertise level



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Another way to distinguish the impact of fund expertise from capacity constraints is to estimate regressions on fund performance with both expertise and size as independent variables. In that effort, we estimate ordinary least squares regression on fund IRR, fund multiple, and public market equivalent with both fund expertise and the log of fund size as explanatory variables, and report the regression coefficients in Table 8. In parentheses are t-statistics from White (1980) standard errors. For each performance metric, two regressions specifications are estimated. One without fixed effects and one with investment region, fund type, and vintage year fixed effects. The fixed effects allow us to control for the possibility that the differential performance between skilled and unskilled funds may be explained by a preponderance of highly skilled funds investing in a specific region or belonging to a particular fund type or vintage year. Table 8: Regressions on fund performance

The coefficient estimates from Table 8 reveal that fund expertise is positively related to fund performance even after simultaneously accounting for the impact of fund size, investment regions, fund types, and vintage year. After controlling for other factors, we find that a one standard deviation increase in fund expertise level, i.e., a 2.48 increase in expertise, precipitates a 2.06 percent per year increase in fund IRR, a 16.85 cents growth in cash flow for every dollar invested, and a 10.16 percent per year surge in PME. We note that since larger funds tend to have greater expertise levels, the analysis in Teo (2013) underestimates the impact of fund size. After accounting for the effects of fund expertise, we find that an increase in fund AUM from US$100m to US$3bn crimps fund IRR by 5.28 percent per year and not 4.08 percent per year as previously estimated. In other words, large funds are able to ameliorate the effects of size as they have more skilled partners and employees on board. To understand the differential impact of the various skill sets on fund performance, we first group the component expertise data into four skill sets: financial, technical, management, and networking. The variable financial skill takes a value of one if a fund has financial expertise and a value of zero otherwise. The variable technical skill takes a value of one if a fund has technical, industry, or operational expertise and a value of one otherwise. Similarly, the variable management skill takes a value of one if a fund has management or strategic expertise, while the variable networking skill takes a value of one if a fund has marketing, recruiting, or networking expertise. We do so in order to limit the number of variables that enter into the full multivariate regression and avoid possible multi-collinearity. Next, we estimate regressions on fund IRR with these variables as explanatory variables and report the results in Table 9. Since the skill variables may be highly correlated, to pre-empt potential multi-collinearity, we run regressions that feature only one skill variable as well as regressions that feature all four skill variables. The coefficients on the skill variables indicate that while all four skills sets are valuable in private equity, it is more important for private equity general partners to possess strategic vision and savvy management skills than for them to be technical gurus and industry experts.



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Table 9: Regressions on fund IRR

If expertise allows partners to provide astute financial, strategic, and technical advice to their portfolio companies then it must be that private equity firms that are hands-on in their investment approach benefit more from having experts on board. To investigate, we sort funds first by board representation and compute the expertise spread for funds that require board representation versus funds that do not require board representation. We also explore the effects of industry specialization on the performance impact of expertise. The results reported in Tables 10 and 11 tell a consistent story. Hands-on general partners, i.e., those that require board representation, tend to benefit more from having expertise. Funds that focus on a few industries, and therefore are able to devote more partner time to each portfolio company, are also better able to parlay expertise into better performance. Table 10: Sorts on board representation and expertise



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Table 11: Sorts on industry specialization and expertise

Conclusion In summary, we find that private equity funds benefit from the strategic and management expertise, financial acumen, technical and operational skills, and marketing and networking capabilities of their partners and employees.5 This is not entirely surprising, given the preponderance of general partners with management consulting and investment banking experience in the private industry. Indeed, our findings suggest that allocators of capital inherently understand this link and direct more capital to funds with greater expertise. Nonetheless, it is interesting that firms with skilled partners outperform firms without skilled partners net of fees. Why do skilled general partners not extract the rents fully from the capital providers or limited partners by raising management and performance fees? The answer must lie in the practice of general partners allocating significant personal capital in their own funds alongside the capital from limited partners. Still, expert general partners by raising significant capital are able to partially internalize some of the rents and monetize their valuable skills. References Harris, R., Jenkinson, T., Kaplan, S., 2011. Private equity performance: what do we know? Unpublished

working paper, University of Virginia. Kaplan, S., Schoar, A., 2005. Private equity performance: returns, persistence, and capital flows. Journal of

Finance 60, 1791-1823. Teo, M., 2013. Giants at the gates: capacity constraints in private equity. APEI newsletter Q1 2013. White, H., 1980. A heteroskedasticity–consistent covariance matrix estimator and a direct test for

heteroskedasticity. Econometrica 48, 817–838.

5 It is possible that private equity firms with good initial returns are able to leverage on their greater financial resources to attract employees with strong expertise. Therefore it may be that experts do not engender good performance but rather it is stellar performance that attracts experts. Conversations with general partners suggest that this is unlikely to explain our results. There is little incentive for a private equity fund, having raised capital and delivered good initial returns to take in additional partners or employees unless it is for the purpose of enhancing investment performance.



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Update on the Institute’s Activities Networking The APEI held our second closed-door investment roundtable discussion on 27th February 2013. The investment roundtables are private, by-invitation only events where private equity professionals gather to discuss investment opportunities in the region. It is hoped that the investment roundtables will facilitate information sharing and encourage co-investments and syndication. The participants for our second investment roundtable included Kunna Chinniah (GIC), Maarten Ruijs (CVC), Gerald Leong (Point Hope), Kazu Mizukoshi (Daiwa), Ivica Turza (Riverside), Mathieu Perfetti (Credit Agricole), Khim Tan (Credit Suisse), Tay Wee Teck (Bank of Singapore), Bryan Goh (DBS Private Bank), Alex Schmitz (Capstone Partners), Diana Koh (KPMG), Dean Collins (O’Melveny and Myers), and Kelvin Chan (ex-Partners group). For more information regarding the Asia Private Equity Institute (APEI) at SMU and our upcoming activities, please contact Ms Karyn Tai, centre coordinator (Tel: +65-6828-0933, E-mail: [email protected]). We look forward to receiving your suggestions and comments.

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