Master in International Finance
Master Thesis
Which are the drivers that determine the IRR of American and
European private equity funds?
Author:
Alejandro Ariel Sciaini
Student Number:
10853162
Thesis Supervisor:
Dr. J.K. Martin
Abstract
This Master Thesis aims to analyse the significance of certain variables to determine IRR in Private Equity (PE) funds. OLS regression models are used to test the power of independent variables, which have been identified by previous researches on PE performance as determinants of IRR, such as Type of Fund, Vintage Year, Fund Size, Geographical Focus, Fund Sequence, Industry Focus, Market Index and Bond Yield to determine IRR in American and European PE funds.
The OLS regression models are constructed using Prequin, a database which provides fund-level data of 1,987 of both buyout and venture capital funds with Geographical focus in US and Europe and actively investing between 1980 and 2015. Additionally, other OLS regression models are developed to analyse separately the influence of the explanatory variables before and after 1999 and in the different type of funds: Buyout and Venture Capital.
The goal of this Master Thesis is to add further information to the existing literature regarding what factors influence the performance in PE.
Content
1. Introduction
1.1 What is Private Equity? 1
1.2 History of Private Equity 2
1.3 Structure 5
1.4 Venture Capital 5
1.5 Buyout Funds 6
1.6 Broad Research Question and Relevance 6
2. Literature Review
2.1 PE Performance 8
2.1.1 How to measure PE returns 8
2.2 Considerations 9 2.3 Drivers of performance 2.3.1 Type of Fund 9 2.3.2 Vintage Year 10 2.3.3 Fund Size 11 2.3.4 Fund Sequence 11 2.3.5 Industry Focus 12 3. Data 3.1 Data Bias 12 3.2 Prequin 14 3.3 Sample Selection 14
3.4 Variables and Descriptive Statistics 15
3.5.1 Dependent Variable 15
3.5.2 Independent Variables 17
4. Methodology
4.1 Model 24
4.2 Ordinary Least Squares 24
5. Results and Analysis
5.1 Regression Output 5.1.1 Base Model 25 5.1.1.1 Vintage Year 26 5.1.1.2 Geographical Focus 27 5.1.1.3 Fund Size 27 5.1.1.4 Fund Type 28 5.1.1.5 Fund Sequence 28 5.1.1.6 Industry Focus 29 5.1.1.7 Market Index 29 5.1.1.8 Bond Yield 30 6. Conclusion 31 References 33 Appendix 36
1. Introduction
1.1 What is Private Equity?
A private equity firm is managed by a General Partner (GP) who establishes a fund to which investors, called Limited Partners (LPs), commit an amount of capital to be invested in non-listed companies through the acquisition of stakes with the aim of exiting its investments within 2 to 5 years and distribute the proceeds from these exits to LPs immediately (Balboa and Marti, 2001). Since the target investment is a portfolio of non-listed companies, hence the name ‘private’ (Fraser-Sampson 2010). The most common types of funds are venture capital (VC) and leveraged buyout (LBO). VC funds invest in young or emerging companies from which high risk and growth is expected and are normally investments in the form of minority interests, as opposed to LBOs which normally are in the form of a majority interest and are financed with anywhere from 60 to 90 percent debt (Kaplan & Stromberg, 2008).
PE funds are commonly closed-end and have a lifetime of approximately 10 years (Nowak, Knigge and Schmidt, 2004, p10). As there is not yet a mature secondary market for stakes in private equity funds (Phalippou & Gottschalg, 2009), this makes PE investments a relatively illiquid asset. There are different strategies to execute the investment exits. One strategy is through an initial public offering (IPO), which is the sale of the portfolio company to the public. A second strategy is a trade sale which is the sale of the portfolio company to a strategic buyer. A third strategy to exit is through the secondary buy-out market and this occurs when the investment is sold to another private equity firm which might see an opportunity to increase the value of the company (Loos, 2005).
LPs generally consist of pension funds, institutional accounts and wealthy individuals who commit a certain capital to the PE fund. LPs have limited control of the investments but also limited liability. The management of the portfolio companies is carried out by the GP, who is
responsible for the operational performance of the fund as well as the process of exit which provides the return.
There are different ways to compensate the GP. One way is through management fees, which are commonly based on the capital committed and afterwards on the capital invested. Another way is the carried interest, that in most of the cases is around twenty percent and there are other cases where the GPs are compensated by deal fees and monitoring fees, but this is not a common practice (Kaplan & Stromberg, 2008).
1.2 History of Private Equity
The inception of PE dates back to early 20th century when investors had already been engaged in acquiring private companies. J. Pierpont Morgan provides an example when he bought Carnegie Steel Co. from Andrew Carnegie and Henry Phipps in 1901. Toward the beginning of the 1900s, J.P. Morgan’s company had engaged in financing of industial and railroads companies.
An important milestone happened in 1946, when the American Research and Development Corporation (ARDC) and J.H. Whitney & Co. were established. These companies are generally thought to be the first PE firms. But the real boost to PE capital came with the passage of two pieces of legislation in the United States. Section 1244 of the Internal Revenue Code allowed for a write off of capital losses against ordinary income for those individuals who invested at least $ 25,000 in small new businesses and the Small Business Investment Act of 1958 established Small Businesses Investment Companies (SBIC), which may be regarded as the event where the modern VC industry was born (Demaria, 2010). In five year these companies raised about fifty times the amount raised by ARDC in thirteen years.
As consequence of the boom of the stock market during the 1960s, the VC industry accelerated its growth but then had a decline when the Employee Retirement Income Security Act (ERISA) was enacted in the 70s restricting pension funds from taking excesive risk.
The concept of modern private equity itself came about in 1978 when KKR was founded. These were investments made by Jerome Kohlberg Jr., Henry Kravis and George Roberts, often specialized in family firms whose owners were reluctant to sell out to competitors. In 1988 KKR won the bidding war to take over RJR Nabisco for $25 billion, the largest leveraged buyout at the time.
During the 70s and early 80s, VC funds were larger than Buyout funds but in the late 80s, Buyout funds surpassed VC funds in terms of size.
Europe has tried to copy the PE model from US but has faced some challenges like the fragementation of the European market with different regulations and cultures where the risk aversion was predominant and there has been a more timid approach to entrepreunership and the idea of investing in PE funds (Demaria, 2010). Another challenge has been the imigration and education that has a key role in the US due to the ability of universities to attract worldwide talent that enhance the number and quality of startups, thus improving the market conditions for PE. Due to the proximity with the US in term of culture and language, the UK became an attractive center for PE activity and leads the market in Europe.
PE markets experienced a golden age up to the second quarter of 2007 (Shadab 2009). This rise of the PE market has been attributed to the superior governance model of PE relative to the publicly traded corporation (Jensen 1989) and regulatory costs of being a publicly traded company (Bushee and Leuz 2005) among other things.
Graph 1 shows the evolution of the number of funds and capital raised during the period from 2000 to 2016 (June YTD). The activity in PE grew since 1980 but during 2002 and 2003 experienced a decline because of the bursting of the Iternet bubble in 2001. In 2004 the PE industry recovered and growth was interrupted again in 2007 due to the collapse in credit markets and inability to effectively fund PE investments.
Graph 1: Number of Funds and Capital raised from 2000 to 2016 (June YTD) in the PE industry
Source: Prequin, June 2016
Nowadays the total the total capital managed by PE funds worldwide is around $ 2.4 trillion.
1.3 Structure
Generally, PE funds are organized as a limited partnership with a lifetime of around 10 years and managed by a management team appointed by the GP. Commonly, LPs are investors such as pension funds, financial institutions and wealthy families, that contributed the largest portion of the commited capital. Usually, GPs also commit to contribute a minotrity stake in the fund. (Brealey and Myers, 2003).
A PE fund usually has initial drawdowns og between 10% and 20% of the commited capital during the first year (Ljunqvist and Richardson, 2003). The capital is drawn by the GP only after the identification of the investment opportunity, thus causing a lag between the capital invested and commited which in most of the cases dissapears within 5 years after the inception of the fund when the total drawdowns are completed. (Phalippou, 2007).
Commonly, during their lifetime, PE funds tend to make between 15 to 25 deals with a limited size per investment. This boundaries are set by the LPs. The PE fund usually looks for the exit within the 5 years and the proceeds are immediately distributed to the LPs (Balboa and Marti, 2001).
1.4 Venture Capital
VCs are specialized in early-stage companies with sufficient growth potential but also they have higher risks. Due to the high risk of these investments, regular debt financiers are discouraged to support these businesses but venture capitalists are attracted by their high returns. Generally, VC firms obtain an equity shareholding giving the possibility to the investor to be involved in decision making and to monitor the company.
1.5 Buyout Funds
The strategy of Buyout funds is usually based on the acquisition of a majority interest in a target company’s equity with the aim of gaining control over the assets. Unlike VCs, Buyout funds commonly invest in later stage companies which generally have upward potential which is not being exploited by the current management. The most common way to finance an acquisition is through an LBO transaction that is the acquisition of a controlling interest of the company by private investors (Loos, 2005) and financed through debt. The debt used for the acquisition is collateralized with the company’s assets or its cash flows (Axelson, Stromberg and Weisbach, 2007). Once a buyout fund has identified a target company and completed the due diligence (Metrick, 2006), the fund must define the capital structure of the transaction. Buyout funds usually use equity, debt and mezzanine capital as sources of financing. Equity is the most expensive financing and secured debt is the cheapest one. The equity used in an acquisition depends on the capacity of the lender to access to cheaper financing as debt and mezzanine capital.
The buyout fund aims to increase the target company’s value to get a positive return out of the investment. It can be done by improving the financial leverage, improving its operation or an alternative strategy is the market timing (Nowak, E., Knigge and Schmidt, 2004) where the buyout fund buys in times of low market valuations and exits in times of high market valuations.
1.6 Broad Research Question and Relevance
Despite the growing relevance of PE in the financial world, there is little knowledge about aspects around PE such as returns, profits and risks (Mason, Harrison, 2002). There are controversies on the findings on PE performance as is pointed out by Harris, Jenkinson and Kaplan (2012). This Master Thesis is supported by the existing literature on PE funds’
performance, with the aim to identify variables that influence IRR in PE funds, and aims to bring further information to academics and practitioners that may help to better understand which variables determine IRR. More specifically, this Master Thesis looks at the explanatory value of Type of Fund, Vintage Year, Fund Size, Geographical Focus, Investment Size, Fund Sequence, Industry Focus, Market Index and Bond Yield on IRR.
This Master Thesis analyses the question: What factors determine the IRR of a PE fund?
In the second section of this Master Thesis discusses the existing literature on PE where several determinants of PE have been identified. In the third section there is an explanation on the characteristics of the data that is used to test the hypothesis of this Master Thesis, details on the sample selected, dummy variables and dependant variables. The fourth section goes through the methodology with the explanation of the regression models used for analysis. The fifth section presents the results from the test performed. The results are analysed in statistical and economical terms. The final section attempts to answer the hypotheses supported by the results from the tests and also provides recommendations for further research.
2. Literature Review
2.1 PE Performance
PE performance studies have focused on the return to investments at the portfolio company level, or return to investments at the PE fund level. For the purpose of this Master Thesis, the data analysed is based on performance at the PE fund level.
2.1.1 How to measure PE returns
The existing literature has commonly adopted the internal rate of return (IRR) as a measure of performance. As the time-weighted rates of return remove the impact of cash flows on the calculation of return, portfolio manager prefers this measure due to the fact that they do not have control over the timing and the amount of cash going in and out of their portfolios (Tierney and Bailey 1997). However, as PE fund managers have discretion as to the timing of drawdowns into and distributions of cash from the fund, this approach is not suitable for this type of investment.
In order to evaluate the performance of a PE fund, IRR is commonly used by institutional investors based on the calculation of cash flows in and out of a fund to determine the add value created by the fund’s manager (Douglas Cumming, 2010). Investors have been able to compare their own returns with those available from fund cash flow reported by PE firms to third-part data providers such as Prequin. These databases are sources of data for academic studies on PE firms. Investors are able to compare returns to aggregate industry performance through an index of fund IRRs (Woodward and Hall 2003). Index returns are also commonly used in the asset management industry when seeking an industry benchmark return.
Fund managers also commonly use the “market adjusted” PE returns which integrates an opportunity-cost benchmark to measure returns, considering that cash flows to private equity
are risky and need to be adjusted by an opportunity cost. A profitability index was created by Ljungqviest and Richardson (2003). This index considers risk equivalence for each cash flow in and out of the fund. The outflows are discounted at the risk free rate and the inflows at the market index rate (Kaplan and Schoar, 2005).
For the purpose of this Master Thesis, the IRR is set as the measure of PE performance.
2.2. Considerations
The IRRs analysed for the purpose of this Master Thesis may be subject to biases due to the private nature of the industry. There are previous researches that discussed the size of the potential bias in data providers’ dataset (Stucke, 2011) but the results show that the bias would not significantly change the previous estimate of average PE performance. Phalippou (2012) based on researches carried out by Robinson and Sensoy (2011), Harris, Jakinson and Kaplan (2012) and Higson and Stucke (2012), showed that PE return are virtually identical to those of listed and unveils the existence of biased dataset of PE funds track record.
2.3 Drivers of performance
2.3.1 Type of Fund
Harris, Jenkinson and Kaplan (2012) analysed the performance of nearly 1,400 U.S. buyout and VC funds. Their research shows that buyout funds have outperformed public markets, particularly the S&P 500, net of fees and carried interest, in the 1980s, 1990s, and 2000s and VC funds outperformed public markets substantially until the late 1990s, but have underperformed since.
Metrick and Yasuda (2009) analyse the differences between VC and LBO funds. Their research reveals that the ability and experience of managers is key to the performance and has a greater impact in LBO funds than in VC.
Chung, Sensoy, Stern and Weisbach (2012) stress that manager skillsare more scalable in LBO because managers may employ the same ability to acquire a larger firm and increase its value and results. But in the case of VC, as the investment is focused on start-ups which are smaller than the firms acquires in LBO, the skills of managers is less effective.
2.3.2 Vintage Year
Kaplan and Stromberg (2008) brought evidence that the PE activity is subject to boom and bust cycles suggesting that the year in which the fund raises capital influences the PE performance. Their research shows that in PE boom periods, PE funds raised during these years, most likely will experience disappointing return because firms are unlikely to be able to exit the deals at valuations as high as the PE firms paid in boom periods. Transactions undertaken during the boom period were perhaps driven by the availability of debt financing instead of the potential of investment, resulting in disappointing returns. When PE returns decline, commitments to PE also will decline. Lower returns to recent PE funds are likely to coincide with some failed transactions, including debt defaults and bankruptcies. The relative magnitude of defaults and failed transactions however, is likely to be lower than after the previous boom period.
2.3.3 Fund Size
Humphery-Jenner (2012) examined why large PE funds earn lower returns and explain that large funds take advantage of being well connected and better positioned to raise capital These advantages are suitable for large investments but not for small ones. On the other hand, a small PE funds take advantage of its smaller structure to efficiently manage small companies. That means that large PE firms perform better when they invest in large companies than when they invest in small ones. Existing literature demonstrates that large funds earn lower returns when they invest in small companies and earn higher returns if they invest in large companies. This suboptimal investment in small companies drives large PE firm to underperform.
2.3.4 Fund Sequence
Previous research suggests that the fund sequence may contain relevant information on the capacity of the managers to improve the performance of the funds due to a previous experience carrying out this activity.
Humphrey-Jenner (2012) adds the variable fund sequence to his regression in order to control for the number of funds that were raised by the PE firm prior to the current fund as a way to measure the effect of experience or skill of the manager.
Cumming, Fleming and Schwienbacher (2009) suggest that later PE funds tend to perform better than first time PE funds. They also add fund sequence to their model as a controlled variable to assess PE performance.
2.3.5 Industry Focus
Gompers, Kovner and Scharfstein (2006) found a positive relationship between the specialization by venture capitalists and its success. They also conclude that the deterioration in performance appears due to both an inefficient allocation of funding across industries and poor selection of investments within industries. Organizational characteristics, however, are not irrelevant: VC firms with more experience tend to outperform those with less experience.
3. Data
This section presents an explanation of some data issues by previous researches in PE. It is then followed by a description of Prequin, the database used for the analysis in this Master Thesis, followed by the sample selection. This section also looks at the variables that are expected to be relevant for the determination of PE performance and finally, the exposition of descriptive statistics on the data analysed.
3.1 Data Bias
There is a general idea that the sample data related to PE performance is not representative of the universe of private equity investments due to the private nature of the industry. Previous research studies point out survivorship bias and self-selection bias as common facts that skew the data used in research studies.
Survivorship bias is the tendency for PE funds with poor performance to be excluded from databases due to the fact that they no longer exist. Survivorship bias causes the results of some studies to skew higher because only companies which were successful enough to survive until the end of the period are included in the research.
Self-selection bias is caused when the data for statistical analysis is not chosen randomly affecting the statistical significance of the test.
Each company has a different approach to create its sample. As noted by Phalippou and Gottschalg (2009), data providers “obtains data mostly from fund investors as most fund managers refrain from giving out information. As Woodward and Hall (2003) remarks that reporting of data by companies is voluntary and the companies that do report are not a random sample of all companies, they are a biased sample. “Good news is reported more often than bad”. (Woodward and Hall, 2003, p.4).
Robinson and Sensoy (2010) explain that “the universe of PE funds is not available, and summary statistics from data sources, such as Preqin, differ from one another (Harris, Jenkinson and Stucke, 2010). It is not possible to know if the differences are a consequence of a function of sample selection, self-reporting or survivorship biases or due to the LP/GP matching process in PE (Lerner, Schoar, and Wongsunwai, 2007).
Available databases such as Prequin, usually used in academic research and as benchmarking by the industry, contain reporting and survivorship biases (Harris, Jenkinson and Stucke, 2010).
3.2 Prequin
The access to the dataset to test the influence of the independent variables on IRR in PE funds was via the Prequin database.
Prequin is a data source on private equity fundraising. This database is constantly updated and includes details for all funds of all types being raised worldwide, with key information on target sizes, interim closes, placement agents, lawyers and, investors among other information. It has an extensive source of PE fund performance, with full metrics for over 5,100 named vehicles. In terms of capital raised, Prequin contains data for over 70% of all funds raised historically.
3.3 Sample Selection
For the purpose of this Master Thesis, firstly all the realized PE funds were selected, both Buyout and VC, with geographical focus in US and Europe within the period from 1980 to 2015. Secondly all the funds that have no available data for at least one of the independent variables (for example, for some PE funds fund size was not available) and exit date, were excluded in order to leave only realized funds. Thirdly, outliers in terms of IRR were excluded. After these exclusions, the remaining dataset consists of information on 1,987 funds, all initiated over the period 1980-2013. From this dataset other datasets were extracted in order to analyse the influence of the independent variables on IRR for PE funds until 1999 (602 funds); from 2000 to 2013 (1.385 funds); only Buyout Funds (1.098 funds) and only VC funds (889 funds).
3.4 Variables and Descriptive Statistics
This section presents all the variables used in this Master Thesis to describe the relationship between the independent variables and the dependent variable defined as IRR.
3.5.1 Dependent Variable
For the purpose of this Master thesis, Internal Rate of Return (IRR) is used to measure PE performance.
IRR is the interest rate at which the net present value of all the cash flows from an investment equal zero. IRR is best-suited for analysing PE investments, which typically entail multiple cash investments over the life of the business, and a single cash outflow at the time of the exit.
IRR must hold the following equality:
NPV = Σ [(CFn)/(1+IRR)^n] = 0
The data related to the IRR used in this Master Thesis was collected from Prequin database.
Table I.A presents a summary of the descriptive statistics of IRR and Table I.B displays the frequency of the samples.
Table I.A: Summary Statistics of IRR
This table shows the summary statistics (mean, median, standard deviation, minimum and maximum) of the dependant variable IRR.
Statistics of IRR Funds from 1980 to2013 Funds from 1980 to1999 Funds from 2000 to2013 Buyout Funds from 1980 to2013 VC Funds from 1980 to2013 Mean 10,50 11,45 10,09 13,16 7,23 Median 10,20 11,25 9,90 12,55 6,90 Standard Deviation 10,75 11,55 10,35 9,78 10,98 Minimum -14,70 -14,40 -14,70 -14,10 -14,70 Maximum 34,80 34,80 34,80 34,80 34,60
Table I.B: Frequency Distribution of IRR
This table shows the frequency distribution (in numbers and cumulative percentages) of the dependant variable IRR. Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013 Buyout VC
Bin Freq. Cum. % Freq. Cum. % Freq. Cum. % Freq. Cum. % Freq. Cum. %
-15 - 0,00% - 0,00% - 0,00% - 0,00% - 0,00% -13,5 18 0,91% 8 0,72% 10 0,72% 3 0,27% 15 1,69% -12 12 1,51% 3 1,37% 9 1,37% 4 0,64% 8 2,59% -10,5 27 2,87% 11 2,53% 16 2,53% 7 1,28% 20 4,84% -9 21 3,93% 5 3,68% 16 3,68% 2 1,46% 19 6,97% -7,5 27 5,28% 11 4,84% 16 4,84% 10 2,37% 17 8,89% -6 36 7,10% 9 6,79% 27 6,79% 11 3,37% 25 11,70% -4,5 36 8,91% 10 8,66% 26 8,66% 9 4,19% 27 14,74% -3 43 11,07% 15 10,69% 28 10,69% 7 4,83% 36 18,79% -1,5 66 14,39% 20 14,01% 46 14,01% 18 6,47% 48 24,18% 0 60 17,41% 15 17,26% 45 17,26% 24 8,65% 36 28,23% 1,5 64 20,63% 17 20,65% 47 20,65% 26 11,02% 38 32,51% 3 66 23,96% 20 23,97% 46 23,97% 30 13,75% 36 36,56% 4,5 75 27,73% 15 28,30% 60 28,30% 27 16,21% 48 41,96% 6 95 32,51% 23 33,50% 72 33,50% 51 20,86% 44 46,91% 7,5 106 37,85% 30 38,99% 76 38,99% 56 25,96% 50 52,53% 9 144 45,09% 36 46,79% 108 46,79% 90 34,15% 54 58,61% 10,5 116 50,93% 35 52,64% 81 52,64% 68 40,35% 48 64,00% 12 136 57,78% 38 59,71% 98 59,71% 83 47,91% 53 69,97% 13,5 105 63,06% 31 65,05% 74 65,05% 69 54,19% 36 74,02% 15 113 68,75% 32 70,90% 81 70,90% 81 61,57% 32 77,62% 16,5 84 72,97% 22 75,38% 62 75,38% 57 66,76% 27 80,65% 18 64 76,20% 21 78,48% 43 78,48% 44 70,77% 20 82,90% 19,5 71 79,77% 21 82,09% 50 82,09% 48 75,14% 23 85,49% 21 61 82,84% 22 84,91% 39 84,91% 43 79,05% 18 87,51% 22,5 55 85,61% 19 87,51% 36 87,51% 34 82,15% 21 89,88% 24 61 88,68% 19 90,54% 42 90,54% 43 86,07% 18 91,90% 25,5 37 90,54% 20 91,77% 17 91,77% 23 88,16% 14 93,48% 27 34 92,25% 9 93,57% 25 93,57% 23 90,26% 11 94,71% 28,5 29 93,71% 13 94,73% 16 94,73% 18 91,89% 11 95,95% 30 32 95,32% 13 96,10% 19 96,10% 26 94,26% 6 96,63% 31,5 26 96,63% 9 97,33% 17 97,33% 19 95,99% 7 97,41% 33 39 98,59% 16 98,99% 23 98,99% 24 98,18% 15 99,10% 34,5 20 99,60% 12 99,57% 8 99,57% 13 99,36% 7 99,89% 36 8 100,00% 2 100,00% 6 100,00% 7 100,00% 1 100,00% Total 1.987 100,00% 602 100,00% 1.385 100,00% 1.098 889
3.5.2 Independent Variables
The following are the variables included in the regression models to partially explain the effect in the dependent variable defined as IRR.
VINTAGE: The vintage is the year in which the fund made its first investment. The samples arise from the period from 1980 to 2013. Table II shows the frequency of the vintage year of the samples selected for this Master Thesis. It can be observed that the number of funds increases over the years experiencing a reduction during the period from 2002 to 2004 as consequence of the DotCom bubble effect and PE fund activity is reactivated from 2005 until 2008, when the growth slowed down due to the financial crisis which began in 2007.
As the samples include only PE funds that were realized until 2015 it is also logical to see a smaller number of funds during the last years (2009-2013) because a great number of PE funds created during that period were not realized before 2015.
This increasing activity in PE since 1980 reaffirms the evidence on the increased popularity of PE presented by Mason and Harrison (2002).
For the purpose of this Master Thesis, the regression model uses dummy variables to represent the vintage year. Five dummy variables are created to represent the periods 1980-1985; 1986-1990; 1991-1995; 1996-2002 and 2003-2008. The period from 2009 to 2013 was eliminated in order to avoid the dummy variable trap and the creation of multicollinearity. The last two dummy variables represent in a better way the DotCom bubble and the financial crisis periods respectively.
Table II: Frequency Distribution of VINTAGE
This table shows the frequency distribution (in numbers and cumulative percentages) of the dependant variable VINTAGE. Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013 Buyout VC
Vintage Freq. Cum. % Freq. Cum. % Freq. Cum. % Freq. Cum. % Freq. Cum. %
1980 5 0,25% 5 0,83% 0 0,00% 2 0,18% 3 0,34% 1981 3 0,40% 3 1,33% 0 0,00% 0 0,18% 3 0,67% 1982 3 0,55% 3 1,83% 0 0,00% 0 0,18% 3 1,01% 1983 8 0,96% 8 3,16% 0 0,00% 0 0,18% 8 1,91% 1984 15 1,71% 15 5,65% 0 0,00% 3 0,46% 12 3,26% 1985 12 2,32% 12 7,64% 0 0,00% 2 0,64% 10 4,39% 1986 17 3,17% 17 10,47% 0 0,00% 5 1,09% 12 5,74% 1987 21 4,23% 21 13,95% 0 0,00% 8 1,82% 13 7,20% 1988 20 5,23% 20 17,28% 0 0,00% 11 2,82% 9 8,21% 1989 25 6,49% 25 21,43% 0 0,00% 11 3,83% 14 9,79% 1990 29 7,95% 29 26,25% 0 0,00% 14 5,10% 15 11,47% 1991 13 8,61% 13 28,41% 0 0,00% 6 5,65% 7 12,26% 1992 24 9,81% 24 32,39% 0 0,00% 12 6,74% 12 13,61% 1993 24 11,02% 24 36,38% 0 0,00% 13 7,92% 11 14,85% 1994 37 12,88% 37 42,52% 0 0,00% 23 10,02% 14 16,42% 1995 39 14,85% 39 49,00% 0 0,00% 21 11,93% 18 18,45% 1996 40 16,86% 40 55,65% 0 0,00% 24 14,12% 16 20,25% 1997 69 20,33% 69 67,11% 0 0,00% 44 18,12% 25 23,06% 1998 91 24,91% 91 82,23% 0 0,00% 57 23,32% 34 26,88% 1999 107 30,30% 107 100,00% 0 0,00% 54 28,23% 53 32,85% 2000 171 38,90% 0 100,00% 171 12,35% 80 35,52% 91 43,08% 2001 94 43,63% 0 100,00% 94 19,13% 36 38,80% 58 49,61% 2002 70 47,16% 0 100,00% 70 24,19% 35 41,99% 35 53,54% 2003 68 50,58% 0 100,00% 68 29,10% 36 45,26% 32 57,14% 2004 73 54,25% 0 100,00% 73 34,37% 34 48,36% 39 61,53% 2005 118 60,19% 0 100,00% 118 42,89% 78 55,46% 40 66,03% 2006 163 68,39% 0 100,00% 163 54,66% 103 64,85% 60 72,78% 2007 155 76,20% 0 100,00% 155 65,85% 98 73,77% 57 79,19% 2008 134 82,94% 0 100,00% 134 75,52% 76 80,69% 58 85,71% 2009 59 85,91% 0 100,00% 59 79,78% 36 83,97% 23 88,30% 2010 60 88,93% 0 100,00% 60 84,12% 38 87,43% 22 90,78% 2011 78 92,85% 0 100,00% 78 89,75% 46 91,62% 32 94,38% 2012 77 96,73% 0 100,00% 77 95,31% 50 96,17% 27 97,41% 2013 65 100,00% 0 100,00% 65 100,00% 42 100,00% 23 100,00% Total 1.987 100,00% 602 100,00% 1.385 100,00% 1.098 100,00% 889 100,00%
GEOGRAPHY: The variable GEOGRAPHY represents the regions where the PE fund invests. For the purpose of this Master Thesis, only the PE funds with regional focus on US and Europe were selected resulting in a dummy variable to represent whether the PE fund is focused on the US or not.
Table III shows the frequency distribution of the samples. Of the 1,987 PE funds from Dataset 1; 76% are focused on investments in US and the rest in Europe. The samples selected show a supremacy of the US as a destination for PE funds regardless of the type.
Table III: Frequency Distribution of GEOGRAPHY
This table shows the frequency distribution (in numbers and percentages) of the dependant variable GEOGRAPHY. Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013 Buyout VC
Geography Freq. % Freq. % Freq. % Freq. % Freq. %
US 1.516 76% 504 84% 1.012 73% 747 68% 769 87%
Europe 471 24% 98 16% 373 27% 351 32% 120 13%
Total 1.987 100% 602 100% 1.385 100% 1.098 100% 889 100%
FUNDSIZE: The variable FUNDSIZE represents the total amount raised by the PE funds. This variable is represented in the regression models by the total amount of the capital raised. Since this data is very dispersed across funds, the natural logarithms of the total amount of the capital raised is used with the aim of presenting this data following a normal distribution,. By using this methodology, the variable shows a normal distribution around the mean which reduces the outliers (Stock and Watson, 2007, p267). Graphs 3 and 4 represent the shapes of the frequencies before and after applying the natural algorithms respectively for the PE funds created between 1980 and 2013.
Graph 3: Frequency Distribution of total amount raised by PE for Dataset 1
TYPE: This variable represents the type of fund that for the purpose of this Master Thesis is classified either Buyout or VC.
A Dummy variable was created to represent whether the PE fund is Buyout one or not. In case of being a Buyout the dummy takes on the value 0 otherwise the value 1.
As Table IV shows; Buyout funds represent between 55% and 57% of the total PE funds included in the samples.
Table IV: Frequency Distribution of TYPE
This table shows the frequency distribution (in numbers and percentages) of the dependant variable TYPE.
Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013
Type Freq. % Freq. % Freq. %
Buyout 1.098 55% 310 51% 788 57%
Venture 889 45% 292 49% 597 43%
Total 1.987 100% 602 100% 1.385 100%
SEQUENCE: This variable identifies whether the PE fund is the first one created by the GP or not. In order to capture this information a dummy variable was created which takes on the value 1 in case of being a First Time PE fund and the value 0 if it is Subsequent or Follow On PE Fund.
In Table V shows the distribution frequency of the variable sequence.
Table V: Frequency Distribution of SEQUENCE
This table shows the frequency distribution (in numbers and percentages) of the dependant variable SEQUENCE. Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013 Buyout VC
Sequence Freq. % Freq. % Freq. % Freq. % Freq. %
First Fund 394 20% 167 28% 227 16% 217 20% 177 20%
Subsequent 1593 80% 435 72% 1158 84% 881 80% 712 80%
INDUSTRY: The variable INDUSTRY captures the specific industry were the portfolio companies within the PE fund are active. Prequin has an extensive nomenclature for the industry where the PE fund invests, but for the purpose of this Master Thesis and with the aim to analyse the impact that PE firms with industry focus have in the performance of the fund, a dummy variable is created to take on the value 1 in case the fund is focused on a specific industry and 0 in case the fund is diversified.
Table VI shows that a larger quantity of PE funds are diversified.
Table VI: Frequency Distribution of INDUSTRY
This table shows the frequency distribution (in numbers and percentages) of the dependant variable INDUSTRY. Funds from 1980 to 2013 Funds from 1980 to 1999 Funds from 2000 to 2013 Buyout VC
Industry Freq. % Freq. % Freq. % Freq. % Freq. %
Focused 212 11% 61 10% 151 11% 78 7% 134 15%
Diversified 1775 100% 541 100% 1234 100% 1020 100% 755 100%
Total 1.987 100% 602 100% 1.385 100% 1.098 100% 889 100%
MARKET_INDEX: This variable captures the return of investing in the market index in the same period than the fund lifetime. The data comes from the calculation of the accumulated return of the S&P500 and MSCI Europe index for the PE funds investing in US and Europe respectively from the vintage year until the exit year.
Table VII presents a summary of the descriptive statistics of the returns if PE funds had been invested in the market index of the respective region and during the lifetime of the fund.
Table VII: Summary Statistics of MARKET INDEX
This table shows the summary statistics (mean, median, standard deviation, minimum and maximum) of the dependant variable MARKET INDEX.
Statistics of MARKET_INDEX Funds from 1980 to2013 Funds from 1980 to1999 Funds from 2000 to2013 Buyout Funds from 1980 to2013 VC Funds from 1980 to2013 Mean 5,11 5,82 4,80 4,94 5,32 Median 5,47 6,03 5,47 5,47 5,99 Standard Deviation 4,09 2,57 4,56 3,84 4,37 Minimum -42,86 -3,44 -42,86 -20,16 -42,86 Maximum 27,69 27,69 18,72 12,37 27,69
BOND_YIELD: The variable Bond Yield measure the yield of investing in government bonds in the same period than the fund lifetime. For the PE funds with geographical focus on the US the yield on US 10-year Treasury Notes is considered and for the PE funds focused on Europe the reference is the German 10-year Bond.
Table VIII presents a summary of the descriptive statistics of the returns in case the PE funds had invested in Bonds.
Table VIII: Summary Statistics of BOND YIELD
This table shows the summary statistics (mean, median, standard deviation, minimum and maximum) of the dependant variable BOND YIELD.
Statistics of BOND_YIELD Funds from 1980 to2013 Funds from 1980 to1999 Funds from 2000 to2013 Buyout Funds from 1980 to2013 VC Funds from 1980 to2013 Mean 4,96 6,95 4,09 4,70 5,28 Median 4,63 6,44 4,27 4,61 4,79 Standard Deviation 1,91 1,86 1,11 1,68 2,11 Minimum 1,51 4,55 1,51 1,51 1,51 Maximum 13,91 13,91 6,03 12,44 13,91
4. Methodology
This section explains the methodology used in order to generate information that brings together elements to explain, at least partially, which are the drivers that determine IRR in PE funds with geographical focus on US and Europe.
4.1 Model
The model is based on determinants of IRR which arose from previous research studies. Given the literature mentioned in Section 2 and the data available, the following base model arises:
IRR = αi + β1VINTAGEi + β2 GEOGRAPHYi + β3GPLOCATIONi + β4FUNDSIZEi +
β5TYPEi + β6SEQUENCEi + β7INDUSTRYi + β8MARKET_INDEXi + β9BOND_YIELDi +
β10DUMMYi +
ε
i4.2 Ordinary Least Square
Ordinary Least Squares (OLS) linear regressions are performed to test the significance of the independent variables, described in Section 3, to know how much of the IRR of a PE Fund can be explained by these variables.
5. Results and Analysis
This section presents and discusses the results of the regression analysis on the model with IRR as dependant variables.
5.1 Regression Output
5.1.1. Base Model
The Adjusted R² of the base models are between 0.043 and 0.164 indicating the fitness of the model. That means that according to Analysis ToolPak of Microsoft Excel, between 4.3% and 16.4% of the variation in IRR can be explained by the independent variables described in these regression models.
Table IX: OLS Regression Analysis
This table shows the estimates of OLS regressions. Funds are the unit of observation. The dependent variable is the IRR of funds realized between 1980 and 2015. Dependent variables are the vintage year, type of fund, geography location of the fund, the sequence of the fund, industry focus, market index and yield on government bonds. Data on IRR is from Preqin.
Model 1 Model 2 Model 3 Model 4 Model 5
Intercept 11.97a 2.63 12.37a 12.35a 6.08b VINTAGE_1980-1985 0.29 10.12c -9.70 VINTAGE_1986-1990 4.67c 9.20b -1.40 VINTAGE_1991-1995 2.09 4.56c -1.11 VINTAGE_1996-2002 -3.01b 2.82 -11,09a VINTAGE_2003-2008 -2.57b 0.24 -7.09a FUNDSIZE -0.12 -0.70c -0.07 -0.21 0.27 GEOGRAPHY 1.66b 6.33a 0.86 1.87c 0.60 INDUSTRY_FOCUS -0.39 -2.02 -0.29 -0.23 -0.12 SEQUENCE 2.02a 1.61 2.20a 1.88b 2.29b TYPE -6.18a -5.01a -6.98a MARKET_INDEX 0.25a 1.04a 0.29a 0.29c 0.12 BOND_YIELD 0.29 1.07a -0.20 -0.42 1.04c Adjusted R-square 14.97 16.24 12.97 4.29 16.40 Number of observations 1.987 602 1.385 1.098 889 a
significant at 1%; b significant at 5%; c significant at 10%. .
5.1.1.1 Vintage Year
The vintage year indicates the year when the first investment was made by the PE fund. As the sample contains a broad range of vintage years, a breakdown by bin was made. The regression model 1 includes the bins 1980-1985; 1986-1990; 1991-1995; 1996-2002; 2003-2008 and the period from 2009 to 2013 was dropped in order to avoid the dummy variable trap. For the regression models 2 and 3, this variable was excluded.
The regression models show that most of the dummy variables are statistically significant at 10% significance.
These results allow one to make some observations. Firstly, all the dummy variables until 1995, for the analysis including both types of funds, are positive, pointing out a better performance than the period from 2009 to 2013. In the separate analysis, only Buyout funds hold this characteristic. In principle this could be caused by the fact that this period, not represented by a dummy variable, has fewer examples of realized funds that could give a biased result and affect the comparability. On the other hand, it can also be an indication that during the last years the PE performance was negatively affected by the slowdown in the post financial crisis economy with a stronger effect in Buyout funds.
Secondly, the negative coefficients for the dummies 1996-2002 and 2003-2008, indicate that these periods perform much worse than the years before 1996 and after 2008. An argument to explain lower performance during 1996 and 2001 could be that assets acquired during that period were overvalued due to the well-known DotCom bubble. PE Funds could have acquired overvalued assets resulting in disappointing returns. A similar process could have happened during 2002-2008 where US economy had a process of recovery that turned out to be the genesis of the financial crisis. It can also be observed that the coefficient for the period 1996-2002 is lower than the one for the period 2002-2008 indicating that the DotCom bubble
has a greater negative impact in the PE industry than the financial crisis, particularly in VC funds.
The fact that most of the dummy variables are statistically significant, indicates that Vintage year exerts an influence on IRR.
5.1.1.2 Geographical Focus
This variable indicates the regions where the PE funds invest. For the purpose of this Master Thesis, only the Funds with geographical focus on US and Europe were selected. A dummy variable is created to take on the value 0 in case the PE fund invests in US, otherwise it takes on the value 1.
The regression models 1 and 2 show that the dummy variable is statistically significant at 5% level and at 10% level in model 4. The coefficient is positive indicating that a PE investing in Europe will outperform another investing in US. The regression model 3 shows that the variable does not have a statistical significance and the coefficient is lower suggesting that the influence of the geographical focus is higher for the PE funds created before 1999, specially for Buyout funds. There is a strong indication that Geographical Focus has significant influence on the IRR but further research is recommended.
5.1.1.3 Fund Size
The Fund Size represents the total amount raised by the Fund. Due to the dispersion of this information this variable takes the natural algorithms of the total Fund Size for the regression models.
There is statistical evidence at 10% significance level that smaller PE funds created before 1999 outperform large ones. The rest of the regression models shown are not statistically significant at 5% level but from the analysis of the coefficient some conclusions can be
reached. The coefficients are negative, that means the greater the fund, the lower the IRR. For the funds created since 2000, when the PE industry reached its maturity, the negative impact is lower than the Funds created in previous periods suggesting that GPs managing greater funds improved their operational capacity. In the case of VC funds, the coefficient is positive giving an indication that large funds may outperform small VC funds. In general, the indications from the regressions models are in line with the existing theory that suggests that large funds earn lower returns (Humphery-Jenner, 2012). One reason, is that large funds may have many dispersed investments that diminish the capacity of the GP to manage efficiently each investment.
5.1.1.4 Fund Type
The fund type is split into two categories: Buyouts and VC. A dummy variable is created, which takes on the value 0 in case of being a Buyout otherwise takes on the value 1.
This dummy variable is statistically significant at 1% level for all the models. The coefficient of this variable is negative indicating that VC funds tend to perform worse than Buyout funds, which may due to the ability and experience of managers having a greater impact in
Buyout funds than in VC funds (Metrick and Yasuda, 2009). It is also in line with the paper of Chung, Sensoy, Stern and Weisbach (2012) which stresses the skills from managers as more scalable in LBO funds than in VC funds.
5.1.1.5 Fund Sequence
This variable is included in the model to capture the effect of previous experience from the PE firms in managing funds.
A dummy variable is created, which takes on the value 1 when the Funds was created by the first time and the value 0 when it is a subsequent or follow-on fund.
The regression models show that this variable is statistically significant at 1% level in models 1 and 3 and at 5% level in models 4 and 5. The positive coefficient indicates that First time funds tend to outperform subsequent funds. This indication is not in line with the existing theory (Cumming, Fleming and Schwienbacher ,2009), maybe because the dummy variables are set into two categories instead of adding more dummies to represent the quantity of previously raised funds. Due to the availability of the data, this breakdown is not possible.
On the other hand, this result may indicate that first-time funds tend to outperform subsequent funds due to the focus and effort from the GP to gain a reputation in the industry. Due to the significance of the variable and its contradiction with existing literature, further research is needed to bring more insights on this topic.
5.1.1.6 Industry Focus
A dummy variable is created to represent whether the PE fund has specialized in a particular industry or not. In the case the PE fund has industry focus the dummy variable takes on the value 1 otherwise the value 0. The aim of the inclusion of these variables is to control the presence of industry effect.
As result of the analysis, the variable does not present a statistical significance making difficult to draw conclusions and further research is needed.
5.1.1.7 Market Index
This variable is added to the model in order estabish the relationship of the stock market with IRR. In other words, the aim is to analysis whether an improvement in the economy, that is reflected in a better performance of the listed companies, it is also reflected in PE fund performance.
S&P500 index was set as the proxy for the fund samples with geographical focus on US and MSCI Europe Index for the funds investing in Europe.
This variable shows a significant coefficient at 1% level and indicates a positive relationship between Market and PE performances.
5.1.1.8 Bond Yield
The variable Bond Yield is added with the aim to control the return that the PE fund would have obtained if it had invested the raised capital in Government bonds.
US 10-year Treasury Notes is used for PE funds with geographical focus on US and for the PE funds focused on Europe the proxy is the German 10-year Bonds.
The variable is statistically significant at 1% level only in model 2 and presents a positive coefficient for all the regression models except for the VC funds. The positive coefficient is in line with the existing research that suggests that when interest rates are low, and there is liquidity available to raise capital, performance decreases and when interest rate is high , PE performance increase (Kaplan and Stromberg, 2008).
The results from this analysis do not show statistical significance but there are some indications that there may exist a positive relationship between Bond yield and IRR but further research is recommended.
6. Conclusion
Despite of the increasing activity in PE (Mason and Harrison, 2012), there is little knowledge about what determines IRR in these kind of investments. The aim of this Master Thesis is to bring further information on the potential drivers that could be affecting the IRR and provide additional insight to stakeholders, such as investors, managers and shareholder among others, to help in the decision making process.
OLS regression models were created in order to assess the explanatory power of variables such as Type of Fund, Vintage Year, Fund Size, Geographical Focus, Fund Sequence, Industry Focus, Market Index and Bond Yield. From the outcome of the analysis some observations were made.
Firstly, there is statistical evidence that Vintage Year, Geographical Focus, Fund Type and Sequence are significant variables that may influence IRR.
Vintage Year seems to exert an important influence confirming the existing literature that in PE boom periods, PE funds raised during these years obtain disappointing return because the high acquisition cost. The results from this Master Thesis show that the IRR was strongly affected by the expansion due to the DotCom Bubble, especially in the case of VC funds. On the other hand the booming period before the crisis of 2008 had a lower effect on IRR. Other significant observation is that funds focused on Europe tend to outperform the funds investing in US but this influence seems to be stronger for Funds created before 1999 and particularly in the case of Buyout funds. This could be a reflection of the maturity reached by the PE industry in Europe in the XXI century. Although geographical focus seems significant, further research is recommended in order to go in depth into the causes on why European funds outperform the American ones. Other observation given by the models is that Buyout funds outperform VC funds, with a stronger effect in funds created after 1999,
confirming the existing literature which suggests that the ability and experience of managers has a greater impact in buyout funds than in VC. Another interesting aspect that this Master Thesis suggests is the statistical evidence that first-time PE funds outperform subsequent or follow-on PE funds contravening existing literature. This Master Thesis obtained some indication that this relationship is opposite than the one explained by previous research studies and First Funds have greater incentives to outperform with its first fund, in order to gain a good reputation for later fund raising. Despite of the significance of this variable, further research on this aspect is recommended.
Secondly, the result of this Master Thesis suggests some evidence that PE performance is in line with the cyclicality of the market. The outcome of the test shows that market index holds a positive relationship with IRR suggesting that when the economy grows, PE performance also does.
Lastly, Fund Size and Industry Focus do not present statistical significance in this Master Thesis and conclusions cannot therefore be drawn. Further research on these aspects is recommended, especially the relationship with Fund Size, that according to previous research, shows that smaller funds outperform bigger funds and given that the results from the regression models presented in this Master Thesis show a negative coefficient suggesting a confirmation of the existing theory.
To conclude, although the performance of PE faces big challenges due to the difficulty of obtaining reliable and comparable data, there are theories being confirmed through subsequent research bringing more clarification on this topic. This Master Thesis contributes in this process of clarification by statistically confirming some drivers of IRR in PE that were identified by previous research studies.
References
Axelson U., Stromberg P. and Weisbach M., 2009, Why are buyouts levered?. The financial structure of private equity funds. Journal of Finance, 64, p.1549-1582.
Balboa, M. and Marti, J., 2001, The Determinants of Private Equity Fundraising in Western Europe, Working Paper, April.
Brealey R. and Myers S., 2003, Principles of Corporate Finance, International Edition, McGraw-Hill, 7 edition.
Bushee, B. and Leuz, C., 2005, Economic Consequences of SEC Disclosure Regulation: Evidence from the OTC Bulletin Board, Journal of Accounting & Economics, Vol. 39, No. 2.
Cumming D., 2010, Private Equity: Fund Types, Risks and Returns, and regulation, 1st Edition, Wiley.
Cumming, D., Fleming, G. and Schwienbacher (2009). Style drift in private equity. Journal of Business Finance & Accounting, 36, p. 645-678.Chung, J., Sensoy, B., Stern, L. and
Demaria C., 2010, Introduction to Private Equity. 1st Edition, Wiley Finance.
Fraser-Sampson, G., 2010, Private Equity as an asset class. 2nd Edition. Copyright John Wiley & Sons, Ltd. Chippenham, Wiltshire, Great Britain: Antony Rowe Ltd.
Gompers, P., Kovner, A., Lerner, J. and Scharfstein, D., 2006, Specialization and Success: Evidence from Venture Capital, Working Paper, February.
Harris, R., Jenkinson, T. and Kaplan, S., 2012, Private Equity Performance: What Do We Know?, Working Paper, Journal of Finance, July.
Harris, R., Jenkinson, T. and Stucke, R., 2010, A White Paper on Private Equity Data and Research, UAI FOUNDATION CONSORTIUM, December.
Higson, C. and Stucke, R., 2012, The Performance of Private Equity, March.
Humphery, M., Jenner, 2012, Private Equity Performance: What Do We Know?, Review of Finance, Vol. 16, No. 3, pp. 799-835, Journal of Finance.
Jensen, M., 1989, Eclipse of the Public Corporation, Harvard Business Review, September-October.
Kaplan S. and Schoar A., 2005, Private equity performance: returns, persistence, and capital flows, Journal of Finance.
Kaplan, S., per Stromberg, 2008, Leveraged Buyouts and Private Equity, Working Paper, National Bureau of Economic Research, July.
Lerner, J., Schoar, A. and Wongsunwai W., 2007, Smart Institutions, Foolish Choices: The Limited Partner Performance Puzzle, The Journal of finance.
Ljungqvist A. and Richardson M., 2003, The Cash Flow, Return and Risk Characteristics of Private Equity, NYU, Finance Working Paper No. 03-001, January.
Loos N., 2005, Value Creation in Leveraged Buyouts, Dissertation of the University of St. Gallen, Graduate School of Business Administration, Economics, Law and Social Sciences (HSG), April.
Mason, C. and Harrison R., 2002, Is it worth it?. The rates of return from informal venture capital investments, Journal of Business Venturing, 17, (3), p. 211-236.
Metrick A., 2006, Venture Capital and the Finance of Innovation, Hoboken, N.J.: Wiley.
Metrick A. and Yasuda A., 2010, The Economics of Private Equity Funds. Review of Financial Studies, Vol. 23, No.6, p.2303-2341.
Nowak, E., Knigge and Schmidt, 2004, On the performance of private equity investments: does market timing matter?. CEPRES Center of Private Equity Research.
Phalippou L., 2007, Investing in Private Equity Funds: A Survey, University of Amsterdam.
Phalippou L., 2012, A comment on recent evidence on private equity performance. Working paper. Said Business School, University of Oxford.
Phalippou L. and Gottschalg O., 2009, The Performance of Private Equity Funds, The Review of Financial Studies, Vol. 22, Issue 4, pp. 1747-1776, 2009, April.
Robinson, D. and Sensoy, B., 2010, Cyclicality, performance measurement, and cash flow liquidity in private equity. Dice Center WP 2010-021. Fisher College of Business WP 2011-03-021.
Shadab, H., 2009, Coming Together after the Crisis: Global Convergence of Private Equity and Hedge Funds, Northwestern Journal of International Law & Business, Volume 29, Issue 3, Summer.
Stock, J. and Watson, W., 2007, Introduction to econometrics, 2nd Edition, Pearson International or Pearson Educational, Inc.
Stucke, R., 2011, Updating history. Working paper. Said Business School, University of Oxford.
Tierney D. and Bailey J., 1997, Opportunistic Investing. Performance measurement,
benchmarking, and evaluation, The Journal of Portfolio Management, Vol. 23, No. 3: pp. 69-78, Spring.
Weisbach, M., 2012, The Review of Financial Studies, 00, (0), p.1-46.
Woodward, S. and Hall, R., 2003, Benchmarking the returns to venture. Hoover Institution and Department of Economics, Stanford University. National Bureau of Economic Research. Working paper, Kellogg School of Management.
Appendix A: Description of Variables
IRR: The interest rate at which the net present value of all the cash flows from an investment equal zero. IRR is best-suited for analysing PE investments, which typically entail multiple cash investments over the life of the business, and a single cash outflow at the time of the exit.
VINTAGE: The year in which the fund made its first investment.
TYPE: The type of PE fund grouped into two categories: Buyout and Venture Capital.
GEOGRAPHY: The regions where the PE fund invest.
FUND SIZE: The total amount raised by the PE fund. To normalize the data is applied the natural logarithm.
SEQUENCE: The classification of First or later PE Funds to identify the GP as novice or experienced in managing PE funds.
INDUSTRY FOCUS: The specific industry where the portfolio companies, within the PE fund, operate.
MARKET INDEX: The return of investing in stocks of listed companies during the lifetime of the PE fund..
BOND YIELD: The return from investing in government bonds during the lifetime of the PE fund.
