Master Thesis
Amsterdam | 1018 GD | +31 683699797 | kakshat@gmail.com
Identifying sub-categories within the Private
Equity taxonomy for buyout funds
Submitted by: Akshat Kshetrapal Student number: 11133295 Master in International Finance Supervisor: Dr. Jens Martin Submission Date: 31 August 2016
Acknowledgements
‘To end the work designed, a pair of hands needs but a thousand
minds.’1
Indeed, it is hard to pay tribute to all who have helped me. Foremost, I would like to thank Prof. Jens Martin, whose passion for Private Equity, trust in my abilities and fantastic support motivated me throughout this process.
Of my fellow students, I thank in particular Stanislav Butorin & Sander Tiemstra. Both of whom gave me the encouragement to stay the course.
I would also like to thank the administrative department for their immense patience with me and my chaotic work-study-life balance.
I am also indebted to the my superiors at PGGM Private Equity, who helped develop a more nuanced understanding of the field and gave me the motivation to use this thesis to answer some of the pertinent questions we face daily.
Finally, I dedicate this work to my wife, who could have written this thesis in one night, using only her left hand and 5% of her mental capacity but instead she use this opportunity to teach me the values of hard work, dedication and artistic formatting.
1 Johann Wolfgang von Goethe’s words, adapted to meet my purpose: “To end the greatest work designed, A
thousand hands need but one mind.” – Faust II, translated version published by Penguin Classics, London, 1959.
Abstract
This thesis investigates the taxonomy from Buyout funds with the objective of developing categories within buyout funds. This thesis analyses IRRs of 217 funds within 4 identified sub-categories. We conclude that the 4 sub-categories are valid as they are sufficiently different from each other. We further establish that the dispersion within the sub-categories is lower in comparison to the main category.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS 3
ABSTRACT 4
I. INTRODUCTION 1
II. LITERATURE REVIEW 4
A. REVIEW OF EXISTING RESEARCH 4
1. MACROECONOMIC IMPACT OF PRIVATE EQUITY 4
2. THE PRIVATE EQUITY FUND’S RELATIONSHIP WITH ITS INVESTEE COMPANIES 5
3. PERFORMANCE OF PRIVATE EQUITY 7
4. THE LP–GP RELATIONSHIP 7
B. IDENTIFYING THE GAP 9
C. INSTITUTIONAL BACKGROUND 12
III. DATA & METHODOLOGY 13
A. DATA 13
1. DATA SOURCE 13
2. DATA ATTRIBUTES 13
B. METHODOLOGY AND STRUCTURE OF RESEARCH 14
1. HYPOTHESIS BUILDING 14
2. CATEGORIZING FUNDS 16
3. NULL &ALTERNATIVE HYPOTHESIS 16
4. SELECTING THE SUITABLE VINTAGE 17
5. CONTROL VARIABLES 17
C. ANOVA 18
1. WHY ANOVA 19
2. SIGNIFICANCE OF THE FACTOR 19
3. A NOTE ON CAUSALITY 20
IV. VARIABLES & DESCRIPTIVE STATISTICS 21
A. DEPENDENT VARIABLE:IRR 21
B. INDEPENDENT VARIABLE:CATEGORIES 22
C. EYEBALLING THE DATA 24
1. TECHNOLOGY 24
2. HEALTHCARE 24
3. FINANCIAL SERVICES 25
4. DIVERSIFIED 25
V. ANOVA RESULTS & DISCUSSION 26
A. ANOVARESULTS 26
1. DESCRIPTIVES OF THE RELATIONSHIP 26
2. TEST OF BETWEEN-SUBJECT EFFECTS 28
3. PARAMETRIC ESTIMATES 29
B. DISCUSSION OF RESULTS 30
1. 4 SUB-CATEGORIES HAVE A LOWER DISPERSION VIS-À-VIS THE MOTHER CATEGORY. 30
2. CATEGORY (TARGET INDUSTRY) HAS A SIGNIFICANT IMPACT ON IRR. 30
3. 4 SUB-CATEGORIES ARE DISTINCT FROM EACH OTHER. 31
VI. TESTING THE UNDERLYING ASSUMPTIONS 32
VII. CONCLUSION, LIMITATIONS AND FUTURE DIRECTIONS 34
VIII. REFERENCES 37
I.
Introduction
Private Equity has emerged as an important and rapidly growing asset class in the last 3 decades. Conceptually, Private Equity is a form of financing to which companies may turn during the course of their entire lifecycle. In the start-up stage, venture capital serves as one of the most crucial sources of funding. As the companies achieve scale, they seek growth capital from private equity. In mature stages of a company, private equity serves as a source of financing to middle-market firms and listed corporations. For corporations that are undergoing difficulties, the private equity market serves as an important source for turnaround capital that facilitates turnarounds. For each investment stage different private equity funds are raised by General Partners (GP) who are specialized in specific private equity market segments, the sources of capital are Limited Partners (LP) who represent institutional interests (such as pension funds, endowments) or high net worth individuals. PE funds are set up as limited partnerships between the fund manager and the main equity contributors. Such partnerships have a fixed life of about 10 years, with the possibility of extending them for about 3 more years (Kaplan and Stromberg, 2009). Typically, the fund invests its resources within 5 years of fund subscription and holds the target company for at most 5 to 8 years.
This holding period and the resulting illiquidity truly differentiates private equity from other asset classes. PE funds usually acquire control, often the entire shareholdings, of companies. They target firms that are either already not-public i.e. their shares are not publicly traded, or firms that can be made not-public as the PE fund acquires such a significant shareholding to turn the firm private. Those activities require a long time horizon. Naturally, then, investments in those PE funds are therefore long-term and illiquid.
Private equity offers relatively high expected return and low correlation to other asset classes, as a result PE can play a significant part in the portfolio allocation of diversifying investors. Average
yearly returns, measured usually as the internal rate of return (IRR), are usually estimated to be at least 10% (Gompers and Lerner, 2001; Jones and Rhodes-Kropf, 2003; Ljungqvist and Richardson, 2003; Kaplan and Schoar, 2005), and empirical studies suggest that investors should allocate between 5% and 10% of their portfolio to PE (Braun and Harhoff, 2005). However, the variance of returns on the level of the individual PE fund is quite high – the investors in these funds either receive very high returns or face a complete loss of investment (Weidig and Mathonet, 2004).
It is crucial to reiterate:
the high variance of returns from PE investments; and,
the illiquid nature of the investment over a long time horizon
These factors underscore the importance of carefully considered allocation to private equity by limited partners.
The LP’s task is increasingly complex given the proliferation of various kinds of private equity funds. Private equity funds explain their investment style, strategy, geography and fund size in an objective statement, which is distilled into a fund objective category such as Growth, LBO, MBO, venture and geography specific attributes. These attributes are used by investors to assess and compare relative risk/return profiles and benchmarking. Private equity benchmark providers such as Burgiss grade fund performance by comparing the return earned by a fund with the returns of other funds in the same stated objective group. Academic research also uses the stated objectives for categorizing funds for various tests. This categorization based on investment objectives and classification implicitly assumes that funds of the same stated objectives are alike in their attributes.
These fund attributes – often limited to fund size, strategy and geography are often defined broadly and loosely (“that there is no bright-line distinction between ‘‘buyout’’ funds and ‘‘venture funds.’’ Importantly, large ‘‘buyout funds’’ can and do invest in small venture investments. Thus, the characteristics of the fund are the important distinguishing factors. These include the desire to syndicate and the use of connections. “ – Mark Humphery Jenner, 2003). Although there is quite a bit of literature of fund performance, the literature does not really attempt to categorise funds by type. The objective of this study is to develop sub-categories to the existing taxonomies of primary areas of concentration, and consequently evaluate if there are substantive similarities in the risk/return profiles and other attributes within these sub-categories.
This thesis will attempt to develop new sub categories within the private equity asset class. We deem this a crucial contribution as it will facilitate better asset allocation within the private equity asset class. The LPs will be able to better organise their portfolios using the sub-category specific risk-return attributes and thus reap better risk adjusted returns.
We hypothesize that specific and narrower sub categories can be developed from within the broad taxonomy that is presently applied to private equity. The sub categories would have lower dispersion of performance and therefore higher predictability. This thesis will further evaluate whether there exists a statistically significant difference within these categories so as to qualify them as different from one another. The rest of this paper is structured as follows: Section 2 presents a number of relevant studies. Section 3 provides details on the data, its attributes and our methodology for identifying and developing sub categories. Section 4 contains the main empirical results of the research, while Section 5 concludes.
II. Literature Review
This section2 reviews and analyses the related academic literature on Private Equity with the
objective of better placing this research in the context prior work and hence underlining its motivation.
A. Review of existing research
In my review, I found 4 emergent themes in the academic literature pertaining to Private Equity. These were (1) Macroeconomic impact of PE; (2) interaction between a PE fund and its portfolio companies; (3) Interactions between LPs and PE funds; (4) Performance of PE funds.
1. Macroeconomic impact of Private Equity
Justifiably, the research in this field is split into research on either VC funds or buyout (PE) funds. This is because both kinds of funds have different macroeconomic impact. Venture Capital is nurtures start-up activity while buyouts target more mature organisations. An important publication on the economic impact of VC funds was presented by Kortum and Lerner (1998) and Kortum and Lerner (2000). This research concluded that there is a significant relationship between the number of patents filed by firms and the support they receive through invested VC firms. This is very relevant especially relevant because stylistically, VC funding is often targeted at technology companies and patents are a good proxy for a competitive advantage. In a similar research, Frommann and Dahmann (2003) focussed on the potential of VC-funded firms to create employment opportunities and support the growth of funded firms, while
Romain and van Pottelsberghe (2004) pointed to a positive impact of VC on the knowledge transfer between universities and business organisations. The research targeted at Private Equity(Buyout etc.) follows this trend closely. For instance, Lerner et al. (2008) found that increased patenting activities by PE-financed firms, and also a higher quality of patenting, measured in patenting originality and generality. However, Davis et al. (2008) find contradictory evidence and find only limited support for the notion that firms acquired by buyout firms also create more jobs than similar comparison firms. Job creation is generally lower for established, newly acquired firms and potential job growth in some segments is generally more than offset by larger job destruction through the shedding of business subsidiaries or through cost efficiency exercises.
2. The Private Equity fund’s relationship with its investee companies This is a broad field that can be further divided into 3 subgroups.
a) The selection of target-investee companies
Representative of many of these studies, Tyebjee and Bruno (1984), Riquelme and Rickards (1992), Fried and Hisrich (1994), Franke et al. (2006) are particularly important. Typically, these studies use direct survey-based methods to identify criteria used for making the investment decision. However, the research was typically limited to mostly restricted to VC investments in younger firms. Ljungqvist and Richardson (2003b) evaluated the investment behavior of non-VC funds, while Lossen (2006) took the important step to also analyze the value of diversification at the GP portfolio level. Basis of this analysis is that the investment decision, naturally, is not just driven by the prospects of the individual firm, but also the risk-profile of the GP’s portfolio. Lerner (1994) and Meuleman and Wright (2006)
complement this idea by analyzing how PE firms also co-operate by syndicating deals with each other, depending on the competitive landscape. Gompers (1996), in contrast, looked at the timing behavior of fund managers and argues that younger PE firms sell, for reasons of reputation, company shareholdings earlier than more established, older funds.
b) Legal and financial control as exercised by private equity
Timing of investments, as well as legal control rights are key tools to influence the management of portfolio companies. Milestone publications on this topic are Gompers (1995) and Neher (1999). Their arguments work on the agency-conflicts between the PE fund manager and the portfolio firm. This agency-problem also forms the basis for Kaplan and Stromberg (2001, and 2004). Kaplan and Stromberg (2003) look at the contracts between VC firms and the companies in light of financial contracting theory. Building on it, Lerner (1995) looks at the oversight of portfolio firms through representation in the board of those firms. VC firms more intensely control and monitor their portfolio companies at sensitive times of the firm, e.g. a change of the executive officer.
c) Role of advice and direct interface with the management team
MacMillan et al. (1989), Sapienza and Timmons (1989) and Sapienza (1992) explore the value added by private equity investors to their portfolios. The support by investors is, generally, valuable at all stages of the investment. However, it is discovered to be most effective if non-conflictive and therefore supportive. This is to say that the VC manager can aid through experience, contacts and financial expertise. Because of those findings, young firms themselves have an important incentive to prefer established firms with good reputations. In a more recent
research, Kaplan et al. (2007) look at which characteristics in a CEO matter most for the performance of the newly acquired firm. Interestingly, the results suggest that performance of the firms is highest if the investor recruits, post acquisition, an aggressive CEO that does not shy away from conflicts.
3. Performance of Private Equity
There is a growing number of publications on the overall performance of PE and VC funds. In principle, most studies find an outperformance of the PE asset class vis-`a-vis public equity markets. Gompers and Lerner (2001), Jones and Rhodes-Kropf (2003) and Kaplan and Schoar (2005) are notable in their contribution. There are also those who are critical, especially w.r.t. the issues of risk-adjustment and selection bias, this outperformance probably appears less significant: Kaserer and Diller (2004) for an adjustment of European PE returns to arrive at what is defined as ‘public-market-equivalent’ estimation and Cochrane (2005) for a correction of returns for survivorship bias. Further, there are some specific issues related to portfolio firm valuation and reporting standards, which prompt industry associations to try to establish standards, see EVCA (2006b) for valuation guidelines and EVCA (2006a) for reporting guidelines which also describe the intricacies of return measures such as internal rate of return (IRR). Diller, Kaserer (2008) also highlight the dependance of PE returns on the inflow of funds into the industry. Finally, Weidig and Mathonet (2004) and von Braun and Harhoff (2004) address the overall riskiness of the asset class for investors.
4. The LP – GP relationship
The academic contributions on this topic can be further divided into (a) research on the contracts between funds and their investors, (b) determinants of fundraising and on (c) the performance of LPs.
A crucial work on the contractual relations between funds and their investors was presented by Sahlman (1990), followed by Gompers and Lerner (1996) and Gompers and Lerner (1999). Those are primarily empirical works on a range of contracts that evaluate the economic relationship based on covenants, management fee and carry. A significant implication of all these studies is that the economic relation between LP and GP is fairly standardized. A separate group of publications is concerned with the determinants of successful fundraising. Gompers and Lerner (1998) focus on industry-specific and funds-industry-specific factors. Kaplan and Schoar (2005) established that there is a statistically significant link between the performance of one fund and its follow-on fund. This fact is likely used by investors as a signal of future performance. Hege et al. (2003) examine performance differences between the USA and Europe and come to the conclusion that parts of this can be explained by different use of contractual stipulations and covenants. In a more detailed empirical approach, Tausend (2005) investigates the actual ‘hard’ criteria used predominantly by fund-of-funds when screening and selecting funds. In this work, the composition and experience of the management team of VC funds are identified as the key criteria. The third group in this topic is concerned more directly with the investments by LPs in PE funds. The first is by Mayer et al. (2005) and deals with the sources of VC funds in different countries. More specifically, these authors look at Germany, Israel, Japan and the UK to establish if the investor composition, by investor type, differs for VC funds. They then aim to link those results to (historic) differences of the institutional investor landscape in the examined countries. The second important publication was published by Lerner, Schoar, and Wongsunwai (2007b), who analyzed 4618 investments of 352 US investors in 838 US PE funds. They document large heterogeneity in the performance of investor types. As had been hypothesized, endowments were particularly successful at picking funds and were also better at deciding whether to reinvest in a follow-on fund. The authors therefore
conclude that investor types differ systematically in their investment strategies, and in particular in their sophistication when assessing funds.
B. Identifying the gap
In reviewing the existing literature, the author’s attention was drawn to the classification of Private Equity funds through the unsettled state of conclusions on private equity performance. Lerner et al. (2007) find endowments earned 44 percent annually on investments in private equity funds raised between 1991 and 1998, on the other hand, in a survey of academic research, Phalippou (2008) concludes that “the average investor has obtained poor returns from investments in private equity funds” compared to those available in public markets. In contrast, Kaplan and Lerner (2010) conclude venture capital “returns net of fees have been competitive with the return from public markets” but there is a “great deal of variation over time in whether VC returns outperform or underperform public markets”. 3
We view the unsettled state of conclusions about private equity performance as directly linked to the inadequacy of data available for analysis and research. Through this thesis the author wishes to take but a small step towards better processing of that data since improving access to data is a matter that is beyond academic scope.
The author noted a glaring gap in the academic research in this field that pertains to the classification and/or taxonomy of private equity funds. Private equity funds explain their investment style, strategy, geography and fund size in an objective statement, which is distilled into a fund objective category such as Growth, LBO, MBO, venture and geography specific attributes. These attributes are used by investors to assess and compare relative risk/return profiles and benchmarking. Private equity benchmark providers such as Burgiss
grade fund performance by comparing the return earned by a fund with the returns of other funds in the same stated objective group. Academic research also uses the stated objectives for categorizing funds for various tests. This categorization based on investment objectives and classification implicitly assumes that funds of the same stated objectives are alike in their attributes.
These fund attributes – often limited to fund size, strategy and geography are often defined broadly and loosely. Academic researchers are thus forced to assign categories of their own device to the funds, and this self-categorization likely causes a large variance in the results as pointed out earlier. Although there is quite a bit of literature of fund performance, the literature does not really attempt to categorise funds by type.
The main analysis does not directly distinguish between ‘‘venture’’ and ‘‘buyout’’ funds. Instead, it distinguishes between ‘‘venture’’ and ‘‘buyout’’ funds by examining the ‘‘characteristics’’ of the fund, not a simplistic classification of a fund as ‘‘venture’’ or ‘‘buyout.’’ The rationale is that there is no bright-line distinction between ‘‘buyout’’ funds and ‘‘venture funds.’’ Importantly, large ‘‘buyout funds’’ can and do invest in small venture investments. Thus, the characteristics of the fund are the important distinguishing factors. These include the desire to syndicate and the use of connections. Nonetheless, I do examine whether the results hold for both buyout and VC investments. I code a fund as a ‘‘VC’’ fund if it Preqin statues that it makes venture or early-stage investments. I classify a fund as a buyout fund if Preqin explicitly states that it is a buyout fund or if Preqin classes it as making latestage investments. These groups are mutually exclusive. There are 632 VC funds and 590 buyout funds.
(pg 811) Private Equity Fund Size, Investment Size, and Value Creation* MARK HUMPHERY-JENNER
This thesis borrows from the academic literature about the mutual fund industry. While it is difficult to replicate the methodology, classification principles and statistically sophisticated analysis applied to the data-rich mutual fund industry. A study of ‘Style Investing’ (Barberis and Shleifer, 2000) helped the author develop a new framework useful in evaluating private equity funds.
“One of the clearest elements of human thought is classication: the grouping of objects into categories. We group countries into democracies and dictatorships based on features of political systems within each group. Such categorization simplies our thinking, and enables us to process vast amounts of information reasonably efficiently. Mullainathan (2000) provides an innovative analysis of the implications of categorization for decision making. Classication of large numbers of objects into categories is pervasive in financial markets. Investors classify assets as liquid securities such as stocks and bonds or illiquid ones, such as real estate and venture capital. They classify stocks as domestic or international, small or large, growth or value, old economy or new economy, cyclical or non-cyclical. Such groups of securities are often called asset classes or styles. Portfolio allocation based on selection among styles rather than among individual securities is known as style investing. When classifying securities into styles, investors group together assets that appear to be similar, in the sense that they are perceived to have a common characteristic. “ (Barberis and Shleifer, 2000).
The objective of this study is to develop sub-categories to the existing taxonomies of primary areas of concentration, and consequently evaluate if there are substantive similarities in the risk/return profiles and other attributes within these sub-categories. The author deems this a simple yet crucial first step in contributing to the taxonomy of private equity funds.
C. Institutional background
The author is in the employ of PGGM’s Private Equity team. The investment team comprising 15 investment professionals deploys EUR 2.0 Bn of capital in private equity each year through fund-of-fund investments, Secondaries and co-investments with private equity funds globally. The absence of a clear taxonomy to categorize funds is a tangible issue facing the institution. A well-defined taxonomy is relevant to institutional investors as it simplifies decisions pertaining to portfolio allocation as well as to measure performance of managers w.r.t. a benchmark.
III. Data & Methodology
A. DataPrivate equity works as a private enterprise between the private equity manager (the General Partner) and investors (the Limited Partners) and is usually structured as a partnership or a limited liability company (Kaplan and Stromberg, 2009). Consequently, PE houses are not obligated to publish financials or other detailed reports. A key difficulty in arriving at any set of conclusions about private equity funds is obtaining a sample representative of the universe of private equity investments.
1. Data Source
We use the data as provided by Prequin. A noted by Harris et al, 2010, Preqin (originally “Private Equity Intelligence”) obtains its data from various sources including public filings and reports, general partners (GPs) and by requesting information from public institutional investors. Access to its data is available to the author courtesy my supervisor and the University of Amsterdam. It is crucial to note that Preqin data have been used only rarely in academic research as it is a more recent entrant to the market and consistent time series data on cash flows is only available for a small number of funds prior to 2002.
2. Data Attributes
We received from Prequin the data for 7,948 private equity funds spanning 27 fund categories (Buyout, Early stage, Mezzanine etc). In line with the motivation of this thesis, we found a major chunk of the classification to be ambiguous and/or misguided. Count of funds as per type are detailed in Appendix A.
The data was encapsulated across 27 attributes. The given attributes were Fund ID, Firm ID, Fund Name, Firm Name, Vintage, Status, Fund Value (mn), Fund Value (mn USD), Fund Value (mn EUR), Fund Target Value (mn), Fund Target Value (mn USD), Fund Target Value (mn EUR), Type, Region Focus, GP Location, Called (%), Distr. (%), DPI, Value (%), RVPI, Net Multiple (X), Net IRR (%), Benchmark Specifics, Bench Net IRR (%), Net IRR Diff. (% pts.), Quartile, Date Reported, Industry Focus and Location Focus.
We found some of the data missing, there were some overlaps (pertaining to sector concentration etc) that had to be manually resolved by individually querying the fund’s website, news articles etc.
B. Methodology and Structure of Research
The motivation of this thesis is to identify statistically valid sub-categories within the broad, loosely defined fund categories within private equity.
1. Hypothesis building
The cross sectional dispersion of fund returns in the private equity industry is very high (Kaplan and Schoar, 2005). We observed the a similar trend in our data set with a standard deviation of 25.95 in our data set of 8,000 funds (only value for 6,349 funds were available).
N Range Min Max Mean Std.
Deviation Variance
IRR 6349 1115.7 -100.00 1015.70 13.587 25.94932 673.37
This high dispersion is often attributed to the difference in manager skill, market timing, sector idiosyncrasies. However, this high dispersion reduces the reliability of Private
Equity as an asset class as it is difficult to define a range for the risk-return attributes. The histogram below displays a frequency distribution of IRRs.
We hypothesize that if sub-categories are astutely identified within fund categories (such a Management-led-Buyout identified as a sub-category within the broader category of Buyouts), the dispersion would be drastically reduced. We argue that this can be attributed to sector-trends impacting investment performance and cross pollination of sector experts within private equity teams (thus transfer of skills).
Further, we expect that the mean returns within these sub-categories will be sufficiently different from each other so as to justify the existence of the sub-category (i.e the IRRs for funds within the sub category of Buyouts-Healthcare will be materially different from Buyouts-Manufacturing).
2. Categorizing Funds
In line with our hypothesis building, we are interested in seeing whether type of industry has a significant impact on fund performance. The industry type has many possible levels (Healthcare, Technology etc). While the interest of this analysis is in all possible levels, only a random sample of levels is included in the data (since it is not possible to have an exhaustive list of every type of industry). We chose the 4 industry types based on the following criteria:
Large enough sample size within our data
A balance of old economy stable business models and disruptive new models in the sector so as to justify private equity interest.
Well defined industry metrics and ratios
For our sample, funds, which invested in any one of the following industries were included (funds that invested in two or more of these industries were not included since they cannot be categorized based on the industry type):
Financial Services
Healthcare
Technology
Diversified
3. Null & Alternative Hypothesis
The hypothesis to study whether IRR is equal across all Categories (different types of funds based on whether they invested in certain industries):
No difference in the means of IRR across various categories of funds
HA: μi≠ μj for at least one i and j……….….Alternate hypothesis There is a difference in means for at least one type 4. Selecting the suitable vintage
It was decided to focus on the funds within the vintage range of 2000 to 2016. As with any sample selection decision, we wanted the data to be relevant to the present and the future without being so dated so as to be not comparable. The time period also allowed us to observe the impact of two major financial crises and control for them.
5. Control Variables
Control Variables: the following variables were kept at constant levels for the purpose of this analysis. It is known that these variables strongly influence fund performance. Since they are not of primary interest for this analysis, they were therefore held constant while testing the relative relationship of the dependent variable (IRR) and independent variable (Type of industries).
a) Fund-type
The data comprised multiple fund types across the spectrum ranging from early stage-seed capital (targeted at concept-stage start-ups, with money multiples in the range of 10-30x but with a very low hit-rate)to Mezzanine financing (targeted at mature firms with established credit ratings, with money multiples in the range of 0.5-3x). For the purpose of this thesis, we decided to focus on buyout funds. Our motivation for selection of Buyout funds was the following:
The category represents the largest data set with over 1000 funds.
Buyout funds tend to cover a wider cross section of sectors as compared to other categories (such a Mezzanine that focus only on specific
industry).
As one of the oldest category of funds within private equity, these are often the most sophisticated in terms of investing.
Higher sophistication also implies better reporting standards and therefore a higher reliability of the data.
b) Region Focus
For the purpose of this analysis, we decided to focus on only the US centric funds. Our motivation for this selection of US funds was the following:
The US has the largest sample of funds.
Given its oldest legacy with private equity, the US market is the most developed and sophisticated.
The US economy is diversified and does not represent any sectoral
concentration (in contrast, the UK economy has a strong Financial Services bias, the Russian economy has a strong Oil & Gas bias.
Our sample therefore consisted of 217 of the highest performing buyout funds with a vintage from 2000 – 2016 and a region focus in US, that invested in any one of the above 4 listed categories and whose data was the most complete and accessible.
C. ANOVA
ANOVA (analysis of variance) is a statistical tool used to analyze the relationship between a categorical independent variable and a numerical dependent variable.
1. Why ANOVA
This research is intended to test if there a difference in the performance (Y variable, IRR) for funds with different focus industries (X variable, Category).
While we are interested in studying if there is an effect of Category (fund’s choice of target industries) on IRR, the research selected a random sample of Category from the large number of target industries that various funds invest in. "Category" is therefore a random factor. Since IRR (expressed in percentage) is a numerical variable and Category is a categorical variable, ANOVA is the tool used to study the relationship between these variables.
ANOVA will test the null hypothesis and help us answer these questions: Are the group means (of IRR) identical for funds that invest in different target industries? Are the (differences between) different target industries a source of variation in IRR?
2. Significance of the factor
As discussed previously, the null hypothesis is that target industry does not affect IRR or, in other words, that the means of IRR of funds with different target industries are equal. The significance, or p-value, of this factor (Category)will be our tool for decision making about this hypothesis.
A low p-value for the factor will allow us to reject this null hypothesis and conclude that the effect of this factor “category” on our response/ dependent variable, IRR is significant. For the purpose of our analysis, α-risk selected before the analysis at 0.05, that is, we are willing to tolerate a 5% probability of error that the null hypothesis is erroneously rejected (the effect of ‘category’ on IRR is erroneously considered significant).
3. A note on causality
Generally observational data only demonstrates correlation. It is generally not possible to infer causal structure from data alone.
However, using this additional knowledge (i.e. time-logic), we can rule out the interpretation that IRR affects the target industries. Time-logic implies that since the choice of target industries came before the return on investments, and therefore before our response variable IRR, the latter could not have influenced the former. It is by way of this additional knowledge that we can interpret causality in the right direction.
IV.
Variables & Descriptive Statistics
A. Dependent Variable: IRROur Dependent Variable is IRR or Internal Rate of Return, in percentage. Measuring private equity performance is not a quantitative exercise alone. The Partnership claims are not traded, neither are the underlying stakes/assets in the fund. The market pricing information for the assets is usually lagged, as a long duration may have elapsed since the acquisition of the company. This problem is even more complicated if one attempts to aggregate performance across funds since the IRRs cannot be averaged. However, given that private equity does not offer many data points, we decided to use IRR as our variable of choice. It is a quantitatively efficient measure of performance. It is also the most prevalent measure of performance in the industry. We also deem is superior measure of performance as compared to ratios such as RVPI (Remaining value to paid-in capital) and TVPI (total value to paid in capital) as IRR relies on the cash inflows and outflows to the private equity fund.
Descriptives
Statistic Std. Error
IRR Mean 19.3240 .82926
95% Confidence Interval for Mean
Lower Bound 17.6895 Upper Bound 20.9584 5% Trimmed Mean 18.3505 Median 16.1000 Variance 149.226 Std. Deviation 12.21579 Minimum -10.80 Maximum 94.00 Range 104.80 Interquartile Range 12.80 Skewness 1.867 .165
Kurtosis 7.068 .329
While IRR has a big range (104.8 %), 95% of the funds in our sample had IRR below 41.62% (recall that our sample came from high-performing buy-out funds). Percentiles for IRR of our sample are in Appendix C. To answer the above research question, IRR will be our dependent (Y) variable, because we intend to understand if IRR is affected by some other variable (categories).
B. Independent Variable: Categories
The type of industry (Financial Services, Healthcare, Technology and Diversified) in our analysis is the categorical variable. As discussed previously, these are four types of funds based on industry exposure that are considered. The highest frequency is that of Diversified funds. These are funds that are opportunistic in their pursuits of investment targets and do not have a sector focus approach. Because of the large size of our sample, we do, however,
have substantial representation of funds that are not ‘diversified’ but have invested in the following industries. (Note, some of these funds have invested in more than one industry, but are not categorized as diversified since they are not sector agnostic. However, it is important to note that these funds are not considered Diversified as they do not have a single sector specialization, they are often spread across 2-4 sectors and do not pursue opportunities outside of these sectors).
The number of various categories (based on choice of industry) can be noted from the following frequency table.
40% percent of the observations were of diversified funds. 15% and 16% of observations were in Financial Services and Technology respectively and 28% of cases were of Healthcare. A pie-chart of the same is included in Appendix D.
Frequency Percentage Diversified 87 40.1 Financial Services 33 15.2 Healthcare 61 28.1 Technology 36 16.6 Total 217 100.0
C. Eyeballing the data
As a starting point, we took a high-level view of our sample data to refine our hypothesis and better understand the data. In our review we found clear demarcation based on the 4
identified
sub-categories. Following
are some of our
observations on the four sub-categories.
1. Technology The data in this
sub-category exhibited
characteristics as
expected, there are more outliers in thie category, possibly alluding to the unicorns such as Google and AirBnB that have given market leading returns to their investors. There is a fund in our sample that achieved an IRR of 94%. This significantly improves the sample’s mean.
2. Healthcare
Healthcare has a higher spread out in terms of IRR. This points to one of the weaknesses in our model. While funds categorize healthcare as one unified sector, the risk return profiles of sub segments within healthcare vary immensely. To illustrate, Pharma companies mimic the performance of technology companies with large investments in R&D followed by bumper returns with an extremely low hit rate of success (drug
discovery). Hospitals mimic the cash profiles of hotel chains, except with much higher margins. Health technology companies too exhibit a wide range of risk return profiles. At an aggregate level, there is wider dispersion in healthcare than any.
3. Financial Services
Returns in this category are clustered around a low IRR. This is reflective of the life-cycle stage of the industry, wherein hereto stable and profitable incumbents are facing the threat of disruption at the hands of new entrants. This also represent muted returns owing to the financial crisis of 2008, the worst in history, which was rooted in the Financial services industry and left an indelible mark on the private equity portfolios holding assets in this sub-category across the globe.
4. Diversified
This sub-category seems to have benefitted from the freedom to pick assets without sectoral restrictions. The above average portfolio also represents the advantages that accrued to those holding well-diversified portfolio through 2 financial crises in the last 2 decades.
However, eyeballing is not sufficient to declare that there is a statistically significant difference in means. Therefore, to better understand the relationship between a categorical X (Buyout sub-category) variable and a numerical Y variable (Gross IRR), we have proceed with ANOVA.
V. ANOVA Results & Discussion
A. ANOVA Results1. Descriptives of the relationship
The estimates of mean IRRs differ for the 4 identified categories of funds. In line with our expectations, the standard deviation of diversified fund’s IRR is the lowest, representing the stability lent to the portfolio through a well balanced portfolio. Healthcare and Technology both have higher IRRs, but also greater standard deviations alluding to the embedded high risk (variance) in these private equity portfolios.
Category IRR Statistic Std. Error
Diversif Mean 18.0759 .95843
95% Confidence Interval for Mean
Lower Bound 16.1706 Upper Bound 19.9812 5% Trimmed Mean 17.8671 Median 16.3000 Variance 79.917 Std. Deviation 8.93962 Minimum -5.20 Maximum 41.60 Range 46.80 Interquartile Range 11.70 Skewness .446 .258 Kurtosis .424 .511 Financia Mean 15.6697 1.65684 95% Confidence Interval for Mean
Lower Bound 12.2948
Upper Bound 19.0446
5% Trimmed Mean 15.8051
Median 13.8000
Std. Deviation 9.51780 Minimum -10.80 Maximum 34.70 Range 45.50 Interquartile Range 9.90 Skewness -.011 .409 Kurtosis 1.081 .798 Healthca Mean 20.6377 1.73816 95% Confidence Interval for Mean
Lower Bound 17.1609 Upper Bound 24.1145 5% Trimmed Mean 19.3628 Median 16.5000 Variance 184.292 Std. Deviation 13.57543 Minimum 5.30 Maximum 64.00 Range 58.70 Interquartile Range 16.30 Skewness 1.380 .306 Kurtosis 1.563 .604 Technolo Mean 23.4639 2.83194 95% Confidence Interval for Mean
Lower Bound 17.7147 Upper Bound 29.2130 5% Trimmed Mean 21.3278 Median 16.4000 Variance 288.716 Std. Deviation 16.99163 Minimum 6.80 Maximum 94.00 Range 87.20 Interquartile Range 15.95 Skewness 2.398 .393 Kurtosis 7.713 .768
2. Test of Between-Subject Effects
To evaluate whether there is enough evidence to suggest that these differences are significant, we reviewed Tests of Between Subject Effects (using SPSS).
Tests of Between-Subjects Effects
Dependent Variable: IRR Source
Type III Sum
of Squares df Mean Square F Sig. Intercept Hypothesis 70493.017 1 70493.017 173.654 .001 Error 1303.540 3.211 405.940a Category Hypothesis 1298.480 3 432.827 2.980 .032 Error 30934.255 213 145.231b a. .907 MS(Category) + .093 MS(Error) b. MS(Error)
Significance of ‘Category’ is 0.032. This indicates that there is only a 3.2% chance that such differences in means occur due to random fluctuations. We had previously established the α-risk at 5%, so that means that with a p-value (significance) that is lower than 5%, would require us to reject the null hypothesis, implying that the alternate hypothesis must be true.
Null Hypothesis
REJECTED μ1= μ2 = μ3 = μ4
No difference in the means of IRR across various categories
Alternate
Hypothesis IMPLIED μi≠ μj for at least
one i and j
There is a difference in means for at
least one type
In other words, we can say with 95% confidence that the mean-IRR of categories is not equal or for our analysis ‘category’ has an impact on IRR.
3. Parametric Estimates
The test also produced the following parametric estimates: Dependent Variable: IRR
Parameter B Std. Error t Sig.
95% Confidence Interval Lower
Bound Upper Bound
Intercept 23.464 2.009 11.682 .000 19.505 27.423 [Category=Diver sified] -5.388 2.388 -2.256 .025 -10.096 -.680 [Category=Finan cial Services] -7.794 2.904 -2.684 .008 -13.519 -2.069 [Category=Healt hcare] -2.826 2.533 -1.116 .266 -7.819 2.166 [Category=Techn ology] 0 a . . . . .
a. This parameter is set to zero because it is redundant.
Parametric estimates suggest the degree and direction of the impact of ‘category’ on IRR. The sign of this estimate indicates the relative direction of impact on IRR. The parameter estimates have used Technology as the base case. Since the estimates of
Diversified and Financial Services are significant, we can say with 95% confidence that the average IRR for them is different from this base level of 23.46
Finally, it is important to note that not all parametric estimates are significant. Significance implies whether we can trust the estimate or not. So in this case, since Healtcare has a high significance of 0.266, it means that there is a 27% chance that Healthcare’s impact on IRR lies outside the range of confidence.
B. Discussion of Results
The results were in-line with our expectation at the onset of this analysis. The 4 identified sub-categories without Buyouts offer a lower dispersion and greater predictability. It is also heartening to note that the 4 sub-categories have distinct means despite the commonality of Vintage and Geographic focus.
1. 4 sub-categories have a lower dispersion vis-à-vis the mother category.
The sub-categories that we had identified at the beginning of the analysis, namely, Healthcare, Technology, Diversified and Financial Services, all exhibit a significantly lower dispersion (standard deviation) compared to the mother category of Buyout. Implication: Private Equity funds can better communicate their value proposition to institutional LPs by declaring their sector focus. The institutional investors on the other hand would benefit from greater predictability of the performance of the funds, they would be able to therefore better construct their portfolios.
2. Category (target industry) has a significant impact on IRR.
We can say with a great degree of confidence that ‘Category’ or target industry has a significant impact on expected IRR of the fund. Implication: Category specific
benchmarks can be developed to more accurately measure the performance of managers.
3. 4 sub-categories are distinct from each other.
Each sub-category has a mean that is distinct from the other sub-categories despite the commonality of geographic focus and vintage years. This difference has also further been proved to be statistically significant. Implication: Large Private Equity players with multiple specialty funds under one house can better allocate resources to develop distinct teams with different sector expertise, while maintaining a few on the fund level diversification.
VI. Testing the underlying assumptions
This test for analysis of Variance (ANOVA) is based on the following parametric assumptions:
There are no irregularities in the data
Observations within groups are normal distributed
In order to verify these assumptions, we conducted a residual analysis. Residuals are calculated by subtracting from each observation the mean of the relevant Category and it is checked if the random scatter is normal and if there are outliers or irregularities.
The following QQ plot of residuals checks for normality in this way:
This indicates that the residuals are (close to) normally distributed. Note, while normality was satisfied, there was an outlier in the data (previously mentioned). This is marked in red on the
QQ-plot. To see if the outlier is an “influential observation” changing our results, we also evaluate the data without the outlier.
Clearly, even without the outlier, Category is still significant (p-value is less than 0.05). With the above results from this analysis, we can conclude from them that this is not an influential observation.
VII. Conclusion, limitations and future directions
At the outset of this paper, we noted the insufficiency of the taxonomy of private equity funds. Our view entering this project was that the major culprit was the lack of quality data and benchmarking. We had believed that it might be possible to classify funds more specifically using sub-categories within broad and loosely defined categories. Our analysis strengthens our view. We find that there is greater predictability within sectoral sub-categories. Our view is further bosletered as we discover that these sub-categories stand quite distinct from each other in terms of IRRs despite the sample homogeneity of Geographic focus, PE fund type, and most crucially, vintage of the funds.
We also take this opportunity to recognize the limitations of our analysis. These are explained below:
Limitation of sample selection: To provide a realistic measure of private equity performance, it is important that the data be representative of the underlying universe. The Literature on mutual fund and headge fund performance (a la Aggarwal and Jorion, 2010) show that practices often used to construct databases can introduce large biases. We are cognizant of the potential for survivorship bias and backfill bias in our dataset.
Limitation of category selection: For the purpose of this analysis, we focussed solely on buyout funds given their maturity profile and representativeness. We, however, recognize that there is the potential in the same data set to include other categories such as Venture Capital and Mezzanine.
Limitations of analysis: The author recognizes that there is potential to apply a more sophisticated statistical analysis to the dataset to mine for deeper insights. For example, it would be interesting to cross examine the sub-categories with the same sector focus but belonging to 2 different
categories. To illustrate, we could compare Buyout-Healthcare sub category with Venture Capital-Healthcare sub category to glean more insights. Both the scope of this thesis, and the resources available to the author as a student, did not allow for such a sophisticated analysis.
To date, many obstacles have blocked the development of a refined taxonomy for private equity including private equity data, legal issues, commercial incentives and limited partner coordination costs. In the end, however, a well developed taxonomy would benefit the ecosystem immensely. The question is how to move forward recognizing the complexities and the challenges.
We provide a skeletal elements of the way forward.
The first developmental step would be to expand the taxonomy to cover other private equity categories. Fund categories such as Venture Capital are distinct from Buyouts, there is also a larger set of data available for venture capital so as to facilitate a deeper statistical analysis. The second step would be explore how the various private equity sub-asset-classes interact with each other in a portfolio and what the impact of diversification is on the risk and return profiles of the portfolios.
A third step would be to evaluate how Private Equity Fund of Funds incorporate the new taxonomy into their decision making processes pertaining to portfolio planning and allocation. Such an undertaking is complex and we foresee significant hurdles. Accurate data would be needed for a large sample of funds to successfully execute the analysis in the suggested directions.
We deem that the time is right for such an undertaking and that it would have disruptive impact on the industry. The data studied here is a strong starting point. Once, concluded, a well developed taxonomy has the potential to vastly improve our understanding of the features, risks
and rewards of the many sub-categories of private equity. These benefits would then naturally help us tap into portfolio theory to fully benefit from this asset class.
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IX. Appendix
A: Funds by Type (data attributes)
Fund Type Count
Balanced 121 Buyout 1778 Co-investment 73 Co-Investment Multi-Manager 65 Direct Secondaries 40 Distressed Debt 202 Early Stage 422
Early Stage: Seed 71
Early Stage: Start-up 65
Expansion / Late Stage 140
Fund of Funds 1007 Growth 380 Infrastructure 190 Infrastructure Fund of Funds 8 Infrastructure Secondaries 2 Mezzanine 287 Natural Resources 204 Real Estate 1393
Real Estate Co-Investment 7
Real Estate Fund of Funds 53
Real Estate Secondaries 19
Secondaries 195 Special Situations 95 Timber 45 Turnaround 35 Venture (General) 1016 Venture Debt 35
Grand Total 7948
B: List of funds used in ANOVA
Fund Name Fund Name Fund Name Fund Name
2000 Riverside Capital Appreciation Fund Cotton Creek Capital Partners II Lightyear Fund Sterling Capital Partners 2003 Riverside Capital Appreciation Fund Court Square Capital Partners Lightyear Fund II Sterling Capital Partners II 2008 Riverside Capital Appreciation Fund V Cressey & Co. Fund IV Lightyear Fund III Sterling Capital Partners III 2012 Riverside Capital Appreciation Fund VI Crestview Partners Lincolnshire Equity Fund III Stonehenge Opportunity Fund II
ABRY VII DFW Capital III Linden Capital Partners T3 Partners II
Accel-KKR Capital Partners III DFW Capital Partners Fund IV Linden Capital Partners II TA XI
Acon-Bastion Partners II DLJ Merchant Banking Partners III Lindsay Goldberg - Fund II Tailwind Capital Partners Alpine Investors IV DW Healthcare Partners 3 Linsalata Capital Partners IV TCW Shared Opportunities Fund IV Altaris Health Partners II Endeavour Capital Fund III Littlejohn Fund III Thoma Cressey Fund VII American Securities Partners III Enhanced Equity Fund Littlejohn Fund IV Thoma Cressey Fund VIII American Securities Partners V Eos Capital Partners III LLR Equity Partners II Thomas H Lee V American Securities Partners VI Excellere Partners I Lone Star CRA Fund Thomas H Lee VI
Apax US VII Excellere Partners II Lone Star New Markets Fund TowerBrook Investors
Apollo Investment Fund V Fortress Investment Fund Lone Star Opportunities Fund V TowerBrook Investors II Apollo Investment Fund VI Fox Paine Capital Fund II Lovell Minnick Equity Partners TPG Partners III Apollo Investment Fund VII Francisco Partners II Lovell Minnick Equity Partners II Trident Fund IV Aquiline Financial Services Fund Francisco Partners III Lovell Minnick Equity Partners III Trident Fund V Ares Corporate Opportunities Fund Genstar Capital Partners V Madison Dearborn Capital Partners IV Trilantic Capital Partners III Ares Corporate Opportunities Fund II Glencoe Capital Partners II Madison Dearborn Capital Partners V Trilantic Capital Partners IV Ares Corporate Opportunities Fund III Glencoe Capital Partners III Madison Dearborn Capital Partners VI Trilantic Capital Partners V North America Ares Corporate Opportunities Fund IV Goldner Hawn Marathon Fund V Mill Road Capital Trumpet Investors
Arsenal Capital Partners Gores Capital Partners Mill Road Capital II Vance Street Capital Fund Arsenal Capital Partners III Graham Partners II Overflow Monitor Clipper IA Vector Capital III Artemis Capital Partners Graham Partners Investments II MTS Health Investors II Vector Fund IV Aurora Equity Partners IV Graham Partners Investments III MTS Health Investors III Vista Equity Fund II Avista Capital Partners Green Equity Investors V New Mountain Partners II Vista Equity Fund III Avista Capital Partners II GS Capital Partners 2000 New Mountain Partners III Vista Equity Partners Fund IV Avista Capital Partners III GS Capital Partners V New Mountain Partners IV Vista Foundation Fund I Azalea Fund II Halifax Capital Partners North Castle Partners 2007 Vista Foundation Fund II Azalea Fund III Halifax Capital Partners II North Castle Partners V Wafra Private Equity Fund V Beecken Petty O'Keefe II Halifax Capital Partners III North Haven Capital Partners V Water Street Capital Partners Beecken Petty O'Keefe III Hancock Park Capital III Novacap Industries III Water Street Capital Partners II Behrman Capital III Harren Investors I Novacap Technologies Buyout III Waud Capital Partners III Berkshire Fund VI Hellman & Friedman IV Oak Hill Capital Partners II Wellspring Capital Partners V Birch Hill Equity Partners II Hellman & Friedman V Odyssey Investment Partners Fund III Welsh Carson Anderson & Stowe XI
Birch Hill Equity Partners III Hellman & Friedman VI ONCAP II Whitney V
Bison Capital Partners II HIG Capital Partners III ONCAP III Whitney VII
Blackstone Capital Partners V HIG Capital Partners IV Onex Partners Wind Point Partners V Blackstone Capital Partners VI HIG Capital Partners V Onex Partners II Wind Point Partners VI Blue Sage Capital Fund I High Road Capital Partners Performance Direct Investments I Wind Point Partners VII BLUM Strategic Partners II High Road Capital Partners II Persistence Capital Partners I
Brentwood Associates Private Equity IV HKW Capital Partners II Platinum Equity Capital Partners Fund I Brynwood Partners V HKW Capital Partners III Platinum Equity Capital Partners Fund III Caltius Equity Partners II Imperial Capital Acquisition Fund III - High Net Worth Progress-Lovell Minnick Ventures Capital Partners Private Equity Income Fund Industrial Growth Partners II Ridgemont Equity Partners Fund I Carlyle Global Financial Services Partners Industrial Growth Partners III Riverside Micro-Cap Fund I
Carlyle Partners IV Insight Equity I Riverside Micro-Cap Fund II
Carousel Capital Partners III Insight Equity II Rizvi Opportunistic Equity Fund Castle Harlan Australian Mezzanine Partners I USA Investcorp Technology Partners III RLH Investors I
Castle Harlan Australian Mezzanine Partners II Worldwide JLL Partners Fund IV Seidler Equity Partners I Castle Harlan Partners IV JLL Partners Fund V Seidler Equity Partners II CIVC Partners Fund IV JW Childs Equity Partners III Seidler Equity Partners III Clayton Dubilier & Rice IX KKR Fund 2006 Short Vincent Partners II Clayton Dubilier & Rice VII KKR Millennium Fund Short Vincent Partners III Clayton Dubilier & Rice VIII Kohlberg Investors IV Silver Lake Partners Commerce Street Financial Partners Kohlberg Investors VI Silver Lake Partners II Consonance Capital Partners Kohlberg Investors VII Silver Lake Partners III Cotton Creek Capital Partners II Levine Leichtman Capital Partners Private Capital Solutions Silver Lake Partners IV Court Square Capital Partners Levine Leichtman Capital Partners SBIC Fund Snow Phipps Fund I Cressey & Co. Fund IV Levine Leichtman Capital Partners V Snow Phipps Fund II
C: Descriptive Statistics - IRR
Percentiles Percentiles 5 10 25 50 75 90 95 Weighted Average(Definitio n 1) IRR 6.7800 8.0800 11.5500 16.1000 24.3500 35.1400 41.6200
Tukey's Hinges IRR 11.6000 16.1000 24.2000
