The impact of private equity fund size on performance

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The impact of private equity fund size on performance

An analysis of different ways to influence the size of buyout funds

University of Amsterdam Amsterdam Business School

MSc Finance – Quantitative Finance Master Thesis

Author: Dhani Saharso Student number: 11650532 Thesis supervisor: Arnoud Boot Finish date: June 2021

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ABSTRACT

This study provides empirical evidence on the relation between different fund size elements and fund performance. The main purpose is to examine the different ways to influence fund size and the impact it has on the fund’s performance. The sample consists of transactions of buyout funds between 1984 and 2019. The research is conducted using fixed-effects models that control for year fixed effects and private equity firm fixed effects. This thesis finds evidence in favour of performance persistence and a negative relation between fund size and performance. Furthermore, having subsequent funds does not have a significant impact on performance. Also, engaging in a high number of deals stimulates performance due to the benefits of diversification and reduced unsystematic risk. The influence of investment per deal on performance is positive but not significant.

Keywords: Private Equity; Buyout Fund; Size Effect; Fund Performance; Deal Size;

Number of Deals

Statement of originality

This document is written by student Dhani Saharso who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no

sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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CHAPTER 1 Introduction

1.1 Background

Over the last decades, the private equity industry has experienced tremendous growth. The inflow of capital has increased a lot and is likely to keep increasing over the coming years (Henry, Fumai, Taylor & Patel, 2020). Even during times with a lot of economic

uncertainties due to the Covid-19 pandemic, private equity firms engaged in a record number of deals in 2020 (PwC, n.d.). Ljungqvist, Richardson and Wolfenzon (2020) show that there exist over 9000 funds that raise more than $1.9 trillion from institutional and other wealthy investors. Even though buyout funds account for the majority (63%) of this amount, they have received less academic attention than other types of funds. Research by Bain and Company (2021) describes that nowadays one of the biggest challenges that buyout funds face are the extremely high asset prices. Besides that, deal multiples also reached high levels in both the U.S. and Europe. These two factors make it difficult for the funds to provide high returns and it puts pressure on them to sustain high performance. At the same time, more and more money is floating into the private equity sector, sometimes even faster than the general partners can invest it. So it is clear that private equity is growing in volume and importance.

What is not so clear however, is the effect of size on the performance of the private equity funds and the best way for them to influence it. There are a few researches that looked into the size effect of private equity firms already but the conclusion remains unclear. According to Lopez-de-Silanes, Phalippou and Gottschalg (2015) there are two main views about the nature of private equity returns. According to one view, fund returns are scalable and fund size should have a positive influence on performance. Private equity funds profit from for example tax advantages, the debt-equity cost-of-capital spread or buying value companies and reselling them at a profit. When these strategies are applied to larger private equity firms, so firms with more subsequent funds that do bigger and/or more deals, they can profit from economies of scale. For example, repeated interactions can lead to better relationships with banks and in turn less information asymmetry and more favourable loan terms (Ivashina &

Kovner, 2011). On the other hand, Lopez-de-Silanes et al. (2015) describe that there is an alternative view that states that the skills of private equity funds are difficult to scale. An increase in size may lead for example to more difficulties in communication and less time and attention spent on each portfolio company. So according to that view size can actually have a negative impact on performance. These conflicting findings demonstrate the need for

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further research. Is private equity fund size really associated with an increase in performance and if so, what is the most profitable way to influence size? The main elements of fund size are the number of deals per fund, the investment per deal and the number of subsequent funds that a private equity firm has. Funds can influence their size by changing each of these three elements. Knowing how each size element influences performance is not only valuable for private equity funds and investors themselves, but also for the economy as a whole. The rapid growth in private equity has led to concerns about its impact on the economy. Some

researchers state that private equity is an important driver of among other things production, employment and economic growth and growth in this sector can thus have a significant impact on the economy (Bernstein, Lerner, Sorensen & Strömberg, 2017; Frontier

Economics, 2013). So by analysing the effect of the different elements of fund size on private equity fund performance, investors can maximise performance which might lead to long-term improvements in firms, industries and even the economy as a whole.

1.2 Research Question

Because buyout funds represent the majority of private equity investments worldwide and the conclusion regarding their size effect remains unclear, this thesis focuses on buyout funds only. The main research question of this thesis is as follows:

“What is the impact of size as measured by the number of subsequent funds, number of deals per fund and investment per deal, on the performance of buyout funds?”

This thesis empirically examines several hypotheses regarding the private equity size effect on performance using a sample of 1720 buyout funds and 33052 buyout transactions using data provided by Preqin. The sample covers the years between 1984 and 2019. The

hypotheses will be tested using fixed effect regression models that control for year fixed effects and private equity firm fixed effects. In all regressions the standard errors are corrected for heteroskedasticity and clustered by vintage year to account for dependence in the residuals within a given year.

1.3 Main Findings and Contribution

Despite the growing number of private equity investments and potentially important

implications they have on the economy as a whole, we have only a limited understanding of

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the effect of fund size on performance. Although multiple researchers looked into the general effect of size (as measured by the amount of assets under management), to my knowledge no research has been done that looks at how the different elements of size might have a different influence on performance. This study therefore contributes to the existing literature by focussing on buyout funds, distinguishing between different elements of fund size and

enhancing the understanding of the relationship between size and performance. By looking at the different elements of size, it will become clear how private equity firms can best

influence fund size and stimulate their performance. This study finds evidence in favour of performance persistence between subsequent funds. This persistence is strongest between a private equity’s current and previous fund and reduces in relation to funds before that. The presence of performance persistence can be explained by the fact that general partners can differ a lot in their skills, experience and quality. Established funds have certain advantages to new funds, such as for example proprietary access to specific deals (Kaplan and Schoar, 2005). This can help good performing funds to also perform well in future funds.

Furthermore the results indicate that performance decreases with size. Larger funds often experience more difficulties in communication, more agency problems and may devote less time and employees to the individual investments (Humphery-Jenner, 2012; Lopez-de- Silanes et al., 2015). Besides the general effect of fund size, this thesis looks at the effect of the different size elements. Firstly, this research investigates whether opening many

subsequent funds (known as sequencing) has a significant effect on performance. It appears that raising another fund by the same private equity firm has a negative effect on

performance, although this effect is not significant. Because the private equity market is maturing and becoming more and more competitive, it is becoming easier for new funds to compete with funds from already established funds. Secondly, this study reveals that a high number of deals per fund can lead to a better performance. This is especially true for less knowledge-intensive funds that are looking for more diversification. By doing a lot of deals funds can diversify and reduce their exposure to unsystematic risks (Gejadze et al., 2017).

Engaging in a lot of deals also leads to repeated interactions with other parties like banks, hence better relationships and more favourable credit terms (Ivashina & Kovner, 2011).

Furthermore every additional investment leads to more experience and possibly an

improvement in the fund’s reputation (Balboa and Martí, 2007). Thirdly, it can be concluded that a high investment per deal positively but not significantly influences performance.

Focussing on large concentrated investments can positively influence performance by providing an extensive network and a lot of industry-specific knowledge. Three additional

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robustness checks have been performed to check the validity of the results. The first one uses Tobit regressions to estimate the relationship between the flow of capital into subsequent funds and past performance. From those regressions it becomes clear that past performance does not significantly affect a fund’s ability to raise capital and the main driver of fund size is the size of the previous fund. A second robustness check is performed that uses different proxies for fund performance: the Kaplan-Schoar Public Market Equivalent and the net multiple. The results show that the previously mentioned findings are all robust to the choice of performance measure. In the third robustness check the sample is divided into two samples based on their geographical focus. It turns out that the positive significant impact of number of deals is only applicable to funds with a focus on the U.S and not to funds with a European focus.

1.4 Outline

The remainder of this thesis is organised as follows. Section 2 gives some background information on private equity along with the most relevant related literature, followed by the hypotheses. Section 3 elaborates on the methodology and research design used in this research and it gives a description of all variables used. Section 4 gives a description of the data and presents some descriptive statistics. Section 5 presents the results, followed by the results of some robustness checks in section 6. Lastly, section 7 focusses on the discussion and conclusion of the results. It also states some important implications and limitations of this research, together with a few suggestions for further research.

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CHAPTER 2 Literature Review

This chapter discusses the theoretical background and main relevant literature. First I will give some background information on the structure of private equity firms, the main determinants of private equity returns and important findings on the size effect and performance persistence. Besides that, this section elaborates on the different elements of private equity fund size and the possible implications they have on performance. Furthermore this part discusses the relation between private equity and the economy. Lastly, the

hypotheses follow from the previous literature.

2.1 Private Equity

Roggi, Giannozzi, Baglioni and Pagliai (2019) explain what private equity is and how it works. It is an investment class that consists of capital that is not listed on a public exchange.

In order to invest in a company, capital is raised from the limited partners (LPs). The LPs provide most of the capital and have limited liability. They are only liable up to the amount that they invested. The LPs are often wealthy individuals or large institutional investors like university endowments, insurance companies and banks. They invest substantial amounts of money for extended periods of time. The general partners (GPs), also called the fund

managers, are responsible for managing the fund and executing the investments. They have full liability, meaning that they are accountable for all liabilities of the private equity fund.

There are two main types of investment strategies: buyouts and venture capital (Kaplan &

Schoar, 2005). Metrick and Yasuda (2010) describe both methods in their paper. They state that buyout funds play an important role in global merger and acquisition (M&A) activity. In the beginning of the twenty-first-century, they accounted for almost one-quarter of all M&A activity. Buyout funds focus on obtaining a controlling interest in a company with the intention to increase the value of the portfolio company in order to sell it at a profit when they exit and distribute the proceeds to investors. Their focus lies on more mature businesses.

They can exit by selling to a third party buyer or another private equity firm or through an IPO. The holding period is relatively short. Private equity funds generally last for ten years and a new fund is started on average every three to five years. The second important type of private equity, venture capital, focuses more on start-ups and small businesses with high growth potential. The investment can be done at different stages but most of the time happens in an early stage when the firm needs extra financing and the fund sees potential. Venture capital funds made early investments in successful businesses like Google and Skype.

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Phalippou (2010) describes that these funds also typically have a fixed term of ten years.

During this time investors cannot change their capital allocation. A new fund is often started every two to five years, by the venture capital firm.

2.2 Determinants of Fund Returns

The return on investment of private equity funds is generated from a combination of things.

Boot, Ligterink and Martin (2017) mention the different sources of revenue. First of all, a significant source of value creation is operational improvement. Private equity firms will try to implement measures that will make the company more efficient. They do this by for example replacing the management team or by outsourcing specific activities. Secondly Boot et al. (2017) mention that private equity funds will revise and improve the strategic focus of the company. They have the ability to do this because of the financing, expertise and access to external networks that they provide. Besides that, their expertise can also be used to make optimal use of tax advantages. They will optimize the capital structure by increasing the amount of debt used without increasing the insolvency risk by too much. Lastly they also describe in their research that private equity funds earn money by buying an undervalued company to then resell it at the right time for a higher amount. Because of their access to timely and valuable information and a lot of knowledge about how markets will develop, they can find the right target companies and make a lot of money this way. Another important source of revenue is fees. Gelfer (2013) describes the most common fee structure: the 2 and 20 fee structure which refers to a management fee of 2% on the committed capital and a performance fee of 20% of net profits above a certain benchmark. There may also be other fees like advisory fees, transaction fees and termination fees. Another source of private equity fund returns is improvements in governance structure. Private equity involvement can be seen as mitigating the principal-agent problem. The principal-agent problem refers to the

misalignment of incentives between managers (the agent) and shareholders (the principal) as a consequence of the separation of ownership and control (Jensen & Mecklin, 1976). Boot et al. (2017) describe how private equity can make sure that the financial interests of the firm’s management and shareholders are better aligned. First of all there is more supervision and control. Public companies often have to deal with free-rider behaviour of shareholders. This means that shareholders do not want to put in effort to keep watch over the management themselves and hope that others will, so that they can profit from it. With private equity you reduce this issue because the private equity firm will control the management and

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shareholders have more direct access to information. That way there is less distance between the management and shareholder which improves coordination between the two. Moreover, private equity investment are often accompanied with more debt financing (Knauer,

Lahmann, Pflücke & Schwetzler, 2014). This leads to more monitoring and because of mandatory interest payments there is less cash available for wasteful investments (Triki &

Ureche-Rangau, 2012).

2.3 Evidence on Fund Size and Performance Persistence

There are multiples papers that looked into the performance of private equity funds but there is still no clear conclusion. Important research is done by Kaplan and Schoar (2005). They studied the performance and capital inflows of private equity partnerships. One of their main findings is that on average the private equity returns from both buyout funds and venture capital funds exceed those of the S&P500. Harris, Jenkinson and Kaplan (2014) studied the performance of nearly 1400 buyout and venture capital funds from the U.S. and came to a different conclusion. They found that buyout funds consistently outperformed public markets.

The average excess return was 20% to 27% over the lifetime of a fund. Venture capital funds however only outperformed in the 1990s. They underperformed public entities in the 2000s.

Kaplan and Schoar (2005) also found evidence in favour of performance persistence. Funds that perform well now are also more likely to attract more and bigger funds in the future and perform well in subsequent funds. According to them this can be explained by the fact that funds voluntarily restrict their size. After a fund performed well, investors want to invest more money in it such that the expected performance of that fund will be similar to that of other funds. When funds restrict the size of the fund, this will result in performance

persistence. However, the relationship is concave: the best performing partnerships grow at a slower rate than average performers. According to them, fund size and performance are positively correlated but once a fund becomes too large, performance actually starts to decrease. So they found that performance increases with fund size, but only to a certain extent. They also conclude that performance increases with the experience of the general partners. Korteweg and Sorensen (2017) also believe in the existence of performance persistence. According to their research, all types of private equity firms showed long-term persistence. Remarkable about their findings is that long-term persistence was more evident among small funds than large funds and persistence declined over their sample period but is never completely competed away. Braun, Jenkinson and Stoff (2017) also found evidence of

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performance persistence, although the evidence is weaker. They found that as a consequence of the private equity sector maturing and becoming more competitive, performance

persistence declined.

On the other hand, there are papers that contradict the previously mentioned findings regarding fund size and performance persistence. An important example is research done by Phalippou (2010), who states that Kaplan and Schoar (2005) overstate the persistence in fund performance. According to his research, persistence is the result of unsophisticated investors.

In the case of sophisticated investors, fund performance cannot be explained by past performance, fund size or sequence. Phalippou (2010) concludes that only the most skilled investors eliminate the predictability of performance because they are the only ones that use all available information to reallocate their capital in the most efficient way. Berk and Green (2004) and Harris et al. (2014) also agree that performance persistence was overstated by Kaplan and Schoar (2005). According to both of their researches, performance is not persistent at all since they found no significant relationship between performance and fund size for buyout funds.

There are also researches that found a negative relation between fund size and fund performance. Humphrey-Jenner (2012) concludes that large private equity funds generate lower returns than smaller funds. According to them, this may be due to the fact that funds that grow in size often reduce the number of staff per investment and thereby the attention given to them. This results in the portfolio companies being monitored and advised by the funds in a less efficient manner. They also explain that large funds may suffer more from agency conflicts. When large funds have a lot of money at their disposal, it can lead to more value destroying investments. Lopez-de-Silanes et al. (2015) also found that performance decreases when fund size increases. They conclude that private equity returns are not scalable.

2.4 The Elements of Fund Size

Despite the growing private equity market and the big impact private equity investments potentially have on the economy, we have only a limited understanding of the impact of size on performance. Limited partners are still searching for the best way to select funds for their investment portfolio. There are a few important ways in which private equity funds can influence their size. The first element of size is the number of funds per private equity firm, also known as sequencing. Private equity firms can decide to raise subsequent funds. These

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subsequent funds that are raised by the same private equity firm are referred to by the

sequence numbers of the funds (Roggi et al., 2019). Despite the fact that multiple researchers looked at the effect of sequence on performance, the conclusion remains unclear. Kaplan and Schoar (2005) found that in the cross-section, the sequence number of a fund is positively associated with fund performance. Their research shows that higher sequence number funds performed better than first-time funds. A possible explanation for this is that higher sequence number funds have developed more experience and skills which positively influences their performance. However, Braun et al. (2017) came to a different conclusion when they looked at the effect of sequence on performance using a more recent sample. They conclude that sequence has a negative effect on fund performance and that performance deteriorates over a general partner’s life. Furthermore, Phalippou (2010) concludes that the relation between sequence and performance is not significant when we assume that investors are sophisticated and skilled. These conflicting findings indicate that the effect of having subsequent funds on performance is still unclear.

Two other important elements of size are the number of deals per fund and the deal sizes. Metrick and Yasuda (2010) looked into the scalability of both buyout funds and venture capital funds and found that there are striking differences between the two. Both funds are labour-intensive and highly skill-based. However, their research shows that past success impacts future funds differently for both types of private equity. According to them there is one key difference between buyout funds and venture capital funds. In the case of buyout funds, managers possess skills that can be of value to firms of varying sizes. The same skills can be applied to not only small companies but also much larger ones. This is in contrast with venture capital funds where they mostly invest in small companies and their skills are not as applicable to larger and more mature firms where core management skills are already in place. This leads to buyout funds being more scalable than venture capital funds.

So Metrick and Yasuda (2010) conclude that when more money is floating into buyout funds, they increase size faster which leads to more revenue and better performance in future funds.

In the case of buyout funds, increasing the amount invested per deal and/or the number of deals can thus lead to an increase in performance. Whereas for venture capital funds it is more difficult to scale up after experiencing growth. When in that case the number of investments and/or investment per deal increases, it is likely that this is not accompanied by an increase in performance.

Cumming and Dai (2011) and Lopez-de-Silanes et al. (2015) also looked into the scalability of private equity firms’ actions. Cumming and Dai (2011) conclude that an

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increase in fund size does not always lead to an increase in performance. They explain that this is due to the limits on the quality and quantity of human capital. They describe that doing more investments can lead to the funds reducing their attention allocated to each portfolio company. This is because of limitations to human’s information processing capacities and ability to perform multiple tasks at the same time. General partners only have limited time and energy that they can devote to portfolio companies. So unless human capital increases at the same rate as the fund grows in size, increasing the number of deals can actually lead to private equity funds performing worse. This implies that a better strategy would be to increase the size of the deals. However, Cumming and Dai (2011) also mention a disadvantage to this strategy. According to them, the probability of a successful exit

decreases when the size of the investment increases. Rossi (2019) found that it is harder for venture capital funds to scale up than for buyout funds. Furthermore he states that buyout funds often focus on making less but bigger investments, whereas venture capital funds make more and also bigger deals as a consequence of growth.

Lopez-de-Silanes et al. (2015) also found that private equity funds are not easily scalable. Unlike Metrick and Yasuda (2010), they conclude that buyout funds are not easily scalable either. According to their research, buyout funds that engage in a high number of investments often struggle to offer high returns. They show that investments underperformed when they were carried out at a time that the fund undertook many other investments. They also found that placing more concentrated bets, so doing less but bigger investments, should lead to an increase in performance. This may be due to the fact that larger investments are more efficiently priced (Lopez-de-Silanes et al., 2015). Given that the effect of the different elements of size is rather under-researched for buyout funds and findings contradict each other, the conclusion remains unclear.

2.5 The Implications of Influencing Fund Size

Funds that experience performance persistence are likely to grow in size because they will attract more and bigger funds in the future (Kaplan and Schoar, 2005). They can influence their size by raising subsequent funds, change the number of deals they do and/or change the size of the deals. But what are the implications of influencing size in these ways? There are a number of factors that need to be considered. First I will explain which types of funds are likely to benefit from engaging in investments with a high deal size. Doing big and concentrated investments implies taking more risk because you place larger bets on fewer

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companies. Funds that do this are therefore mostly funds that take on relatively high risk.

Ljungqvist, et al. (2020) describe in their paper that younger funds often take more risk than older and more mature funds. They classify younger funds as funds with a low sequence number, such as first-time funds. According to them, these funds want to improve their reputation quickly by impressing the market. In order to do this they need to perform better than a specific benchmark. The benefit they experience in case of a success is much larger than the discomfort in case of a big loss. This results in young funds sometimes passing up on investments with a high expected return to instead make investments with lower expected returns but higher volatility. Research by Balbao and Martí (2007) confirms the belief that large deals can improve the reputation of funds. They found that deal size positively impacts a fund’s reputation and thereby also the volume of funds that will be raised in the following years. Furthermore, past research by Ljungqvist et al. (2020) shows that funds with bad past performance take more risks and are thus likely to focus on large concentrated investments.

In order to get the fund’s performance up, worse-performing funds take more risky

investments. Le Nadant, Perdreau and Bruining (2018) add that this increase in risk is needed because it is otherwise too difficult for those funds to improve their performance in the short- term. Additionally, knowledge-intensive funds are also seen as funds that would benefit from engaging in investments with a high deal size. Those kind of funds possess a lot of firm- specific knowledge, resources and skills (Castellaneta & Gottschalg, 2016). When these firm- specific resources are valuable and hard to replace or imitate, they will be fundamental for value creation and lead to a competitive advantage over competitors. Castellaneta and Gottschalg (2016) also describe that these resources can take a long time to develop, will remain valuable for a long time and are not easily transferable. Norton and Tenebaum (1993) describe in their paper that such resources are stimulated when a private equity fund

specialises in certain areas/industries. According to them this leads to a better network and a better understanding of the market and specific industry trends. Therefore specialised private equity funds will have a competitive advantage over others, which can help them to perform better as well. Another important factor to consider is the access to external financing.

According to research by Bain and Company (2021), banks are currently more willing to provide financing for large deals than for smaller deals. They state that banks are more comfortable lending money to well-established private equity firms with a good reputation.

Therefore it is likely that such private equity firms will engage more in large deals rather than a lot of small ones. To summarise, funds that benefit from engaging in large deals are funds that take relatively high risk and focus on specialising rather than diversifying.

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Focussing on a few large investments can positively influence fund performance in many ways. First of all, specialised funds have access to better networks and possess a lot of specialised knowledge (Norton & Tenenbaum, 1993). Gejadze, Giot and Schwienbacher (2017) describe that this can lead to more profitable deals. Specialised funds possess more useful information about the market in general, the competitive environment and strengths and weaknesses of potential targets. This will lead to the funds selecting target companies with the best growth prospects. According to Cressy, Munari and Malipiero (2007), the valuable knowledge and network of the funds also makes it possible to hire the best skilled management team and more effectively monitor and advice their portfolio companies. They state that specialisation leads to a better understanding of industry dynamics and the average rate of success of companies in that industry. This leads to less information asymmetry between funds and investment targets and in turn better ability to control and monitor buyouts. The reduced information asymmetry also strengthens the bargaining power of the funds. Furthermore, Gompers, Kovner, Lerner and Scharfstein (2008) found that specialised funds respond to market signals faster compared to non-specialised funds. The fact that they act upon profitable opportunities faster can also positively affect their performance. On the other hand, if it is true that the probability of a successful exit declines in case of larger investments, as Cumming and Dai (2011) found, doing large investments can also decrease performance.

Next I will elaborate on the types of companies that would benefit from executing a lot of deals rather than a few big ones. This implies taking less risk by diversifying instead of specialising. According to Ljungqvist et al. (2020), older and more mature funds often take less risk and diversify more. They consider a fund old and mature when it is at least ten years old. Unlike young funds, they do not have the urge to establish a track record because they already have one. Besides that, based on the research by Bain and Company (2021) we can conclude that when the private equity firm’s reputation is not so good, banks may not be willing to lend out large amounts of money since they are afraid that the firm will not be able to pay it back. In that case the fund can only finance small deals and is therefore likely to focus on making small investments. Furthermore, Ljungqvist et al. (2020) conclude that funds with good past performance often become more conservative. This can result in them taking less risk and diversifying more and therefore engaging in a lot of deals rather than making a few large investments.

Engaging in a lot of different deals also has possible implications for the fund performance. By doing a lot of different deals, funds control their risk exposure and reduce

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their unsystematic firm-specific risks (Gejadze et al., 2017). Norton and Tenenbaum (1993) describe that firms are exposed to a lot of unsystematic risk in all of their portfolio

companies. This risk arises from factors that are specific to certain firms or industries. By choosing to diversify across many different companies and industries, funds can diversify the unsystematic risk away so that they are only left with systematic risk. Gejadze et al. (2017) also describe another way in which diversification can have a positive effect on fund

performance. They state that by not limiting investments to certain firms or industries, funds do not lose any available targets and are left with a much broader universe of investment opportunities. This way funds can make the most of profitable investment opportunities in all firms and industries. When a certain industry appears less profitable during a specific period, they can focus more on other industries with better growth prospects. On the other hand, a high number of deals also has a disadvantage, as mentioned in section 2.4. It could lead to funds paying less attention to their portfolio companies (Cumming & Dai, 2011). When that happens the fund’s reputation is harmed and the probability of raising funds and the amount of capital raised decreases, which can lower future returns (Balboa & Martí, 2007). When as a consequence managers are struggling to raise sufficient capital, they will have to put in relatively more of their own capital.

Lastly private equity firms can choose to open many funds in a row. They can do this while also increasing the amount of deals and/or investment per deal. On the other hand they can also choose to do this as an alternative to doing more and/or bigger investments. So instead of increasing the size of the current fund, they open subsequent funds. Kaplan and Schoar (2005) mention two reasons why private equity firms might choose not to scale up investments in the current fund. First of all, firms may provide a lot of inputs that are not easily scalable, such as expertise and time. In that case general partners can be reluctant to put more money in deals or to invest in more firms. Secondly they conclude that general partners may choose not to scale up when the current investment opportunities are limited. In that case they will not invest more now but instead save the funds for more profitable

investments in the future. Raising subsequent funds can thus be beneficial when the prospects regarding investment opportunities in the future are good and when private equity firms provide a lot of inputs that are difficult to scale. The implications of opening a lot of funds in a row on fund performance are unclear, as mentioned in section 2.4. On the one hand higher sequence funds could perform better because they have more experience and skills (Kaplan &

Schoar, 2005). Furthermore, Balboa and Martí (2007) explain that reputation can be linked to a fund’s track record. Young firms without many subsequent funds do not have a track record

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yet. It can therefore be more difficult for them to attract capital and as a consequence the managers have to put in relatively more of their private capital. However, throughout subsequent funds you can gain more experience, establish a track record and in turn build your reputation. This will lead to more capital inflows and potentially higher future returns (Balboa & Martí, 2007). On the other hand, research by Braun et al. (2017) showed that performance can also decline over a general partner’s life. They concluded that performance persistence disappeared as markets became more mature and therefore success is no longer guaranteed when you stick with funds from the same top-performing private equity fund.

Table 1 presents a summary of the different ways in which private equity funds can influence their size and which type of funds are likely to benefit from it. It states the types of funds that are likely to benefit from a few big investments (high investment per deal), a lot of small investments (high number of deals), both (a lot of big investments) and opening a lot of subsequent funds.

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Table 1: Summary Implications of the Different Elements of Fund Size Size Element What type of funds benefit from it?

High

investment per deal

▪ Young funds (funds with a low sequence number) that want to improve their reputation quickly (Ljungqvist et al., 2020). Deal size has a positive impact on reputation (Balboa & Martí, 2007).

▪ Funds that performed worse in the past and need to take more risk to get the fund’s performance up (Ljungqvist et al., 2020; Le Nadant et al., 2018).

▪ Highly knowledge-intensive and specialised funds. It will give them a competitive advantage over competitors (Castellaneta & Gottschalg, 2016;

Norton & Tenebaum, 1993).

▪ Well-established private equity firms with a good reputation. Banks are more willing to lend large amounts to such firms (Bain & Company, 2021).

High number of deals

▪ More mature funds (funds with a high sequence number, in general at least 10 years old) that have already established a track record and reputation

(Ljungqvist et al., 2020).

▪ Funds with good past performance. They become more conservative and want to diversify more (Ljungqvist et al., 2020).

▪ Less knowledge-intensive and more diversified funds (Castellaneta &

Gottschalg, 2016). They invest in many different industries/countries.

▪ Private equity firms with a reputation that is not so good. Banks are not willing to finance large deals when they think the private equity firm will not be able to pay back the money (Bain & Company, 2021).

High

investment per deal and high number of deals

▪ Funds that do not have a good and reliable reputation (yet) and want to improve their reputation quickly (Bain & Company, 2021; Ljungqvist et al., 2020).

▪ Older and more mature funds that have easy access to large amounts of external financing (due to a long-standing reputation) and have no need to impress the market in the short-term (Bain & Company, 2021; Ljungqvist et al., 2020).

▪ Funds that want to invest during times where there are not a lot of profitable investment opportunities across many different industries/countries.

A lot of subsequent funds per private equity firm

▪ Funds that provide a lot of inputs that are difficult to scale (Kaplan & Schoar, 2005).

▪ Funds that want to invest during times where current investment opportunities and limited but the future prospects are better (Kaplan & Schoar, 2005).

▪ Funds that want to build a reputation. They can establish a track record throughout subsequent funds (Balboa & Martí, 2007).

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2.6 Private Equity and the Economy

The fact that private equity has been growing over the last couple of years has raised

concerns about the impact on the economy. As a consequence there is a lot of literature that focuses on whether or not private equity contributes to economic welfare. After the financial crisis in 2007/2008 governments even started to regulate the private equity industry more (Payne, 2011). Payne (2011) describes that after the financial crisis there was a need for more transparency within the industry in the form of more disclosure requirements. However, whether the impact on the economy is positive or negative is still open for debate. There are researches, among for example that of Jensen, Light and Baker (1989) that state that private equity can improve operations in the firms they are invested in. Furthermore, Bernstein et al.

(2017) looked at the impact of private equity on industry performance and found that

industries where private equity firms have invested in have experienced more growth and are less exposed to aggregate shocks. In addition, Davis et al. (2014) show that companies that are backed by private equity firms were not more sensitive to the financial crisis than other companies. In fact, during the crisis they had higher investment levels, more asset growth and capital inflow and a higher increase in market share compared to peer companies. They also conclude that private equity involvement can lead to productivity gains, as it often leads to getting rid of less productive establishments and replacing them by more productive ones.

Also, Schwanen, Kronick and Omran (2019) find in their study on the Canadian economy that an increase in the stock of private equity capital has a positive effect on the overall economy. More specifically it led to an increase in GDP, exports, productivity and jobs. On the other hand it is sometimes believed that the transactions have many negative

consequences like lay-offs and service disruptions (Bernstein et al.. 2017). Goergen,

O'Sullivan and Wood (2014) found that institutional buyouts are sometimes associated with negative employment consequences like lower wages and a decline in productivity.

2.7 Hypotheses

Before answering the main research question some preliminary analyses will be performed.

The following hypotheses are all based on prior research. First I will research the existence of performance persistence in the sample. This will be done using the following hypothesis:

H1: There is a strong persistence is fund returns across subsequent funds from the same private equity firm. This persistence is strongest for two subsequent funds and declines when

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you look at the performance persistence between a current fund and funds from further in the past.

The relation between fund characteristics and fund performance will be researched using the following two hypotheses:

H2: The relation between fund size and fund performance is positive and concave: larger funds have higher performance but their performance declines again when they become too large. When they become too large, communication becomes more difficult and this

outweighs the benefits of having more funds to invest.

H3: Sequence has a positive effect on fund performance when private equity firm fixed effects are not controlled for and the effect is negative when we do control for those fixed effects.

Furthermore, the relation between the number of deals and investment per deal of buyout funds and their performance will be tested. This will be done using the following hypotheses:

H4: The relation between the number of deals of buyout funds and performance is negative.

This is based on the rationale that more simultaneous deals lead to worse communication and less attention paid to each investment.

H5: The relation between the investment per deal of buyout funds and performance is

positive. This is based on the rationale that larger investments are priced more efficiently and that large concentrated investments lead to a better network and more specialised knowledge.

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CHAPTER 3 Methodology

This section describes the methodology used in this research. It shows the research design and gives a description of the different variables that are included in the models.

3.1 Preliminary Analyses

The methodology used in this research is mainly based on that of Kaplan and Schoar (2005).

The first four models (eq. 1-4) are used to test whether there is evidence in favour of performance persistence. The dependent variable is “Net IRRit”: the net internal rate of return. This is a common measure for fund performance and often seen as the most

appropriate performance benchmark (Korteweg & Sorensen, 2017). Other researches that use net IRR to measure performance include Braun et al. (2017), Harris et al. (2014), Humphrey- Jenner (2012), Kaplan and Schoar (2005) and Lopez-de-Silanes et al. (2015). Kaplan and Schoar (2005) explain that net IRR compares the present value of all fund contributions by investors to the present value of all fund distributions and the value of unrealised

investments. It excludes management fees and profit shares, also known as carried interest, paid to the GP. The variables of interest in the first four models (eq. 1-4) are “Net IRRit-1”,

“Net IRRit-2”and“Net IRRit-3”. They represent the lagged realised performance of the

previous fund, the fund before that and the one before that respectively. Based on hypothesis 1 the coefficients on the lagged values of net IRR are all expected to be positive but the second and third lag are expected to be smaller and less significant than the first lag. That would indicate that performance persistence is strongest for two subsequent funds and declines when you look at the funds from further in the past. “Fund_Sizeit” and “Sequenceit” have been added as control variables. Multiple studies have used these control variables and proved their significant impact on private equity fund performance. The control variable

“Fund_Sizeit” represents the logarithm of the total capital that is committed to the private equity fund. From past literature it is clear that fund size has an effect on fund performance and is therefore an important control variable. The direction of the effect however is unclear since multiple researchers came to a different conclusion, as discussed in chapter 2. To summarise, Humphery-Jenner (2012) and Lopez-de-Silanes et al. (2015) found a negative relation between size and performance, Kaplan and Schoar (2005), Korteweg and Sorensen (2017) and Braun et al. (2017) concluded that performance increases with fund size and Harris et al. (2014) and Berk and Green (2014) found a nonsignificant relation. The control variable “Sequenceit” represents the logarithm of the sequence number of the fund. Sequence

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numbers refer to the subsequent funds raised by the same private equity firm. It is often used in literature as a proxy for the experience and skills of the management (Roggi et al., 2019).

By controlling for sequence we can therefore isolate the effect of experience and skills on performance. It is also a good proxy for how well-established the fund and its reputation is, which can have an effect on its access to external financing (Bain & Company, 2021).

Multiple studies proved the importance of including sequence as a control variable, among for example Kaplan and Schoar (2005), Phalippou (2010) and Braun et al. (2017). According to Kaplan and Schoar (2005) the first or second private equity fund is likely to perform worse than follow-up funds that are more experienced. They found in their research that in the cross-section, higher sequence number funds also have significantly higher performance as measured by the KS PME. This variable is therefore expected to have a positive coefficient.

Another reason why it is important to include this variable is because the effectiveness of a high number of deals and/or a high investment per deal can depend on the sequence number of the fund. As explained in section 2, older and more mature funds (so funds with a higher sequence number) are more likely to diversify and benefit from a high number of deals, whereas younger funds (with a lower sequence number) are likely to specialise more and benefit more from doing less but bigger deals (Ljungqvis et al., 2020). Because the lagged values of net IRR are included as independent variables, these models do not control for private equity firm fixed effects. Otherwise the lagged variables would be correlated with the error term, which would bias the results. The models therefore only include year fixed effects. Year fixed effects are included to control for time-dependent factors that influence performance, such as credit conditions at the time of the buyouts (Lopez-de-Silanes et al., 2015). The standard errors are corrected for heteroscedasticity and clustered by vintage year to account for dependence in the residuals within a given year.

Eq. (1) Net IRRit = t + 1 Net IRRit-1 + 2 Fund_Sizeit + 3 Sequenceit + it

Eq. (2) Net IRRit = t + 1 Net IRRit-1 + 2 Net IRRit-2 + 3 Fund_Sizeit + 4 Sequenceit + it

The fifth and sixth regression models (eq. 5 and eq. 6) are used to investigate the relation between fund size and performance and sequence and performance. The standard errors are again corrected for heteroskedasticity and clustered by vintage year. Model 6 (eq. 6) includes

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next to the linear terms also the squared terms of fund size and sequence. Those variables are included so that it can be analysed whether the relations with those variables and

performance are concave or not. Models 5 and 6 will be analysed in two different ways: by only controlling for year fixed effects and by controlling for both year fixed effects as well as firm fixed effects. It is important to also include firm fixed effects in these models because they capture variations in performance between different private equity firms. So by

including private equity firm fixed effects it can be tested whether private equity firms vary systematically in their performance. Based on hypothesis 2 the coefficients on fund size are expected to be positive in both models and higher in model 6 than in model 5. Also, the coefficient on the squared term of fund size is expected to be negative and significant. This would indicate a concave relation between size and performance. Based on hypothesis 3 the coefficients on sequence are expected to be positive when not controlling for firm fixed effects and negative when firm fixed effects are included. There is one potential issue that should be taken into account. By having subsequent funds, private equity firms can build/strengthen their reputation which could positively impact their performance. On the other hand, good performance can also lead to the fund increasing in size and having more funds available to open subsequent funds. So if it is the case that past performance

significantly affects the ability of a fund to raise capital for subsequent funds, the results might be biased due to reversed causality. According to Leszczensky and Wolbring (2019), fixed effects models offer protection against biases arising from among other things reverse causality problems. So by using panel data models with fixed effects, the probability of such a bias reduces. Even though fixed effects models are believed to reduce biases, it may not fully solve them. In chapter 6 I will therefore also perform a robustness check where I estimate the relationship between the flow of capital into subsequent funds and past

performance using Tobit regressions. When a first-time fund does not perform well and as a consequence does not have the ability to open a subsequent fund, it might drop out of the sample and bias the results. Tobit regressions deal with this potential bias by censoring the size variable at 0 such that the size of the next fund is set to 0 when a private equity firm does not raise a subsequent fund. This way it can be investigated whether past performance has a significant impact on the fund raising ability of a fund.

Eq. (5) Net IRRit = t + 1 Fund_Sizeit + 2 Sequenceit + it

Eq. (6) Net IRRit = t + 1 Fund_Sizeit + 2 Fund_Size²it + 3 Sequenceit + 4 Sequence²it + it

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3.2 Main Analysis

The main goal of this study is to research the effect of size as measured by the number of funds, number of deals per fund and investment per deal on the performance of buyout funds.

This is done by using regression models 7 and 8 (eq. 7 and eq. 8). The dependent variable in model 7 is “Net IRRit”. In regression model 8 the dependent variable is “Net IRRit”: the change in net IRR between subsequent funds from the same private equity firm. This dependent variable is used to measure the effect of the different size elements on the increase/decrease in performance between two subsequent funds rather than on the performance of one single fund. Despite the fact that net IRR is the main performance measure used in practice, there are researchers that argue that IRR is flawed in the context of private equity. Phalippou (2008) argues that IRR is distorted due to the endogeneity of cash flows. He states that using IRR can lead to unrealistic re-investment assumptions which in turn leads to an upward biased IRR that is unrealistic. To check whether the results are robust to the choice of performance measure, other proxies for performance are used in robustness checks. One robustness check is performed where the net multiple is used as a measure of performance. It measures how often investors have or are likely to get their money back and make a profit on their investments. The net multiple is after net IRR the most widely used performance metric (Harris et al., 2014). Harris et al. (2014) even preferred the net multiple over net IRR since it provided more explanatory power in their research. Another measure of performance is KS PME. The KS PME is the Kaplan-Schoar Public Market Equivalent and compares an investment in a private equity fund to an investment in the S&P500. It is calculated by discounting the cash outflows of the fund plus the current remaining value at the total return to the S&P500 and dividing that by the discounted value of the cash inflows of the fund (net of fees) (Kaplan & Schoar, 2005). A value greater than 1 indicates that the fund outperformed the market index. Given that you take more risk when you invest in a private equity fund compared to the market index, a successful fund should also perform better (Roggi et al., 2019). KS PME is more robust to variations in the timing and systematic risk of the underlying cash flows than net IRR (Sorensen & Jagannathan, 2015). Also, Braun et al. (2017) argue that the PME is a more suitable performance measure than net IRR or the net multiple because unlike the other metrics it adjusts to market risk exposure and thereby controls for certain important risks like leverage. The results of the robustness checks can be found in chapter 6. The variables of interest in the seventh and eighth model (eq. 7 and eq. 8) are “Number_of_Dealsit” and “Investment_per_Dealit”. The variable “Number_of_Dealsit”

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represents the number of deals the private equity fund was involved in and is obtained from the Preqin database. Based on hypothesis 4 the coefficient on the number of deals variable is expected to be negative in both model 7 and 8. This would indicate that an increase in the number of deals is associated with a decrease in performance. “Investment_per_Dealit” represents the average investment per deal that the fund is engaged in. This variable is

calculated by dividing the fund size (the total amount of capital committed to the fund) by the number of investments, following the same method as Rossi (2019). It also captures

potentially larger communication costs since Lopez-de-Silanes et al. (2015) argue that the larger the deal, the more employees are required. Based on hypothesis 5 the coefficient on the investment per deal variable is expected to be positive in both models 7 and 8. This would indicate that an increase in deal size is associated with a decrease in performance. Next to

“Fund_Sizeit” and “Sequenceit”, these models also include “Net IRRit” and

“Industry_Specialistit” as control variables. Net IRRit-1 represents the performance of the previous fund. Based on the findings of among others Kaplan and Schoar (2005) and Cressy et al. (2007) I expect to find a positive relationship between this control variable and

performance. As mentioned earlier, funds with good past performance often become more conservative and are likely to engage in a lot of small deals. At the same time funds that experienced worse past performance often take more risk and engage in less but bigger deals (Ljungqvist et al., 2020). Past performance is therefore an important control variable. The last control variable is “Industry_Specialisationit”. Cressy et al. (2007) have proven that industry specialisation of a buyout fund is associated with an increase in fund performance. This dummy variable is therefore expected to be positive. In constructing this variable, the methodology of Archibugi and Pianta (1994) has been used: the Index of Competitive

Advantage (ICA). Cressy et al. (2007) applied this method to the private equity context. This method looks at how specialised a private equity fund is across industries, relative to other funds. First of all the buyout funds have been divided into ten industry categories based on the industry classification that Preqin provides: Business Services, Consumer Discretionary, Energy & Utilities, Financial & Insurance Services, Healthcare, Industrials, Information Technology, Raw Materials & Natural Resources, Real Estate and Telecoms & Media. After that I calculated the ICA index using the following formula:

ICAij = (Cij / C.j) / (Ci. / C..) where a dot implies a summation over the relevant subscript and Cij = the total number of portfolio companies of buyout fund i in industry j.

C.j = the total number of companies invested in industry j by all buyout funds.

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Ci. = the total number of portfolio companies of buyout fund i.

C.. = the total number of companies invested in by all buyout funds together.

When the value of the index is greater than 1, it can be concluded that the fund is specialised in that industry. So the dummy variable “Industry_Specialistit” is equal to 1 when the buyout fund is considered specialised in the industry of the buyout and 0 otherwise. Models 7 and 8 again include time fixed effects to control for variations in performance due to the state of the economy. This way factors that impact the entire market, such as inflation, are controlled for.

By doing this, fund size is comparable throughout the years in the sample. I will also include specifications where the models include both year fixed effects as well as private equity firm fixed effects. The standard errors are corrected for heteroscedasticity and clustered by vintage year.

Eq. (7) Net IRRit = i + 1 Number_of_Dealsit + 2 Investment_per_Dealit + 3 Fund_Sizeit +

4 Net IRRit-1 + 5 Sequenceit + 6 Industry_Specialistit + it

Eq. (8) Net IRRit = i + 1 Number_of_Dealsit + 2 Investment_per_Dealit + 3 Fund_Sizeit

+ 4 Net IRRit-1 + 5 Sequenceit + 6 Industry_Specialistit + it

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CHAPTER 4 Data and Descriptive Statistics

This chapter elaborates on the data that has been used for this research. It gives an overview of how the data was collected and which adjustments were made to the sample. This is followed by some summary statistics.

4.1 Data Collection

In order to test the hypotheses, this study uses data provided by Preqin. Preqin is a database that is widely used in the industry and considered a reliable database (Diller & Jäckel, 2015).

Harris et al. (2014) compared several databases that contain private equity fund performance and found no evidence that data from Preqin was biased relative to that of other sources. Also Korteweg and Sorensen (2017) concluded that Preqin is one of the most reliable data sources concerning private equity performance. The database is used to retrieve data on many

different buyout funds and their investments. I use two different datasets. The first one contains information on fund characteristics and performance such as the size of the funds, the sequence number, the net IRR and net multiple. The second dataset contains data on the different deals each private equity fund engaged in: the number of investments of each buyout fund and the industry of all these different investments. The two datasets have been merged together.

4.2 Sample

The final sample consists of 1720 buyout funds, 33052 buyouts and covers the period of vintage years between 1984 and 2019. The observation period is much longer than that of most other studies. This way a more complete and robust picture is provided that is less affected by time-specific factors such as private equity industry cycles. Roggi et al. (2019) describe that private equity funds go through multiple stages: a period of growth, a period of crisis and a period of stable and mature growth. By extending the sample period, the effect of the specific stage the fund is in can be eliminated such that the true causal effects can be measured. A few changes have been made to the initial dataset. Following Kaplan and Schoar (2005), Braun et al. (2017) and Roggi et al. (2019), only largely liquidated funds are kept in the sample, so funds for which most of the cash flows have been realised. This is done to make sure that the reported performance measures are based on the true cash flows rather than subjective values that have been reported by the GPs themselves. In order to perform the analysis, all observations were removed that had missing values for fund size, net IRR or

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sequence. After removing these observations, the final sample consists of 1720 buyout funds.

A few variables were winsorized at the 1% level to minimise the impact of outliers, following Braun et al. (2017). For example, the dependent variable net IRR was winsorized based on the rationale that it can have extremely high positive values that result in a highly skewed distribution (Braun et al., 2017). Other variables that have been winsorized include: KS PME, fund size, net multiple, number of deals and investment per deal.

4.3 Descriptive Statistics

Table 2 presents summary statistics on the main variables included in the regression models.

The descriptive statistics are compared to those of a few other relevant papers to see whether the samples used are comparable. Generally it can be concluded that the sample is

comparable to the one used by Kaplan and Schoar (2005). The sample also has similar characteristics to the samples used by Braun et al. (2017) and Lopez-de-Silanes et al. (2015).

We can deduce a number of things from Table 2. First of all, buyout funds do on average 19 deals and the median number of deals is 13. This number is not surprising since it is close to the average number of investments of 18 that Lopez-de-Silanes et al. (2015) found.

Furthermore, the average investment per deal of buyout funds is 84.06 million USD and the median is 41.10 million USD. The median fund size is 510.00 million USD. This is higher than a median size of 415.79 million USD that Kaplan and Schoar (2005) found. The mean fund size of this sample is 1266.58 million USD. Braun et al. (2017) used a sample with a mean fund size of 1045 million USD. So both the mean and median fund size are slightly higher than that of past researches. This could be explained by the fact that this research uses a more recent sample that includes the years in which private equity increased substantially in size. The mean and median net IRR are 16.04% and 13.92% respectively. This is generally consistent with the median and mean values of 13% and 19% documented by Kaplan and Schoar (2005). Furthermore, the median and average values for the Kaplan-Schoar Public Market Equivalent (KS PME) are above 1 for this type of funds, suggesting that over this sample period private equity returns were on average higher than an investment in the S&P500. This is in line with what Kaplan and Schoar (2005) found in their sample. In their sample buyout funds had average PMs of 1.21, which is only slightly higher than the average of 1.16 in this sample. Also Lopez-de-Silanes et al. (2015) found a positive value (1.27) for the median PME. The values for the mean and median net multiple are 1.79 and 1.64 respectively. This is in line with, although slightly lower than the median multiple in the

The impact of private equity fund size on performance