Private Equity Fundraising, Fund Performance and Firm
Specialization
Maia Gejadze, Pierre Giot and Armin Schwienbacher1
ABSTRACT:
Using a firm-level perspective, this paper studies the effect of specialization on the fundraising activities of private
equity firms. In the empirical analysis, we consider two dimensions of specialization: asset class (venture capital
versus buy-out) and industry. Using a large sample of US private equity firms, we find that stage-specialized firms
raise new funds more quickly, while there is no meaningful difference for industry-specialized firms. Using a
competing risk framework to model jointly time until next fundraising and type of fund, we show that fundraising is
accelerated only for follow-up funds within their current area of expertise, but delayed for funds raised outside their
area. This suggests that expertise cannot be easily extended to other areas. Finally, we analyze the impact of duration
between two consecutive fundraising of the same PE firm on the performance of the newly launched fund. We show
that there exists a trade-off between quick fundraising and the performance of the newly raised fund, which fund
investors should mind before committing their capital to the new PE fund.
Version: August 28, 2012
JEL classifications: G2; G24; G3
Keywords: Private equity; specialization; fundraising; performance; competing risks model
1
Contact information: Maia Gejadze; Université catholique de Louvain, Louvain School of Management; Place des
Doyens 1; 1348 Louvain la Neuve (Belgium); Email: maia.gejadze@uclouvain.be. Pierre Giot; University of Namur,
CeReFiM and CORE; Rempart de la Vierge 8; B-5000 Namur (Belgium); E-mail: pierre.giot@fundp.ac.be. Armin
Schwienbacher; Univ. Lille Nord de France – SKEMA Business School; Université Lille 2; Rue de Mulhouse 2 - BP
381; 59020 Lille Cédex (France); Email: armin.schwienbacher@skema.edu. The authors thank Sabrina Chikh and Eric
de Bodt for insightful comments, as well as participants of the AFFI 2012 International conference, Belgian Financial
Research Forum, 3L Finance Research Workshop, and seminar participants at the Université catholique de Louvain
and Ghent University.
1
Fundraising is an essential activity of any independent private equity (PE) firm (Gompers and Lerner,
1998). PE management firms set clear investment objectives to each of their funds that in turn affect the
extent of specialization to be achieved and that are then communicated to potential investors (so-called
limited partners) through private placement memoranda. These fund characteristics are important for
limited partners to decide whether to commit capital, in line with their own investment objectives. While
some funds are highly specialized, others are generalist funds. Recent studies have examined possible
relationships between the degree of fund-level specialization and performance. Results however are
inconclusive. Most prominently, Ljungqvist and Richardson (2003) find no significant influence of
diversification across the number of portfolio companies and industries on the internal rate of return.
Next to specialization at the fund level, private equity firms may also be specialized in that they may
systematically raise funds for the same asset class (e.g., venture capital only or a narrow set of industries).
To our knowledge, the only studies taking a firm-level perspective of specialization in private equity are by
Gompers et al. (2009) and Knill (2009). The first study shows that firm-level specialization affects success
of individual investments, suggesting spill-over effects between funds of a same PE firm. It further shows
that generalists underperform specialists. Knill (2009) examines whether greater diversification at the firmlevel affects the growth of venture capital firms, measured by the increase in asset under management
(AUM) over time. The author finds a positive effect on growth of the firm’s AUM but at the same time
longer investment durations (time between investment and exit) in entrepreneurial firms.
Findings of the existing studies do not allow drawing conclusions on the link between firm specialization
and fundraising. This is critical as PE firms not only seek for successful capitalization from their portfolio
companies but also, due to the closed-end structure of most funds (Kaplan and Stromberg, 2008) need to
secure new fundraising to remain in the market. We believe that to better understand the costs and benefits
of firm level specialization, one should analyze not only its impact on performance but also its
consequences on the fundraising activity of a PE firm as the later is highly important for the sustainability
of the PE firm’s activity.
The purpose of this paper is to explain the link between firm specialization and fundraising. To better
understand this link, we first turn to the analysis of fundraising time. It allows gauging how PE fund
managers allocate their time and expertise between governing current investments and attracting new
capital commitments from limited partners. We believe that fund managers will decide upon setting up of
the follow-up fund according to their need and capacity to allocate managerial time and expertise to
fundraising activities. Gompers (1998) states that individual time constraints of VC fund managers lead to
a particular number of enterprises that each VC manager can select, monitor and support, while supply of
experienced venture capitalists is fixed in the short term. This further affects the number of portfolio
companies that fund managers can invest in (Cumming, 2006; Bernile et al., 2007; Bottazzi et al., 2008).
Finally, we argue that, this will also affect the time of launching a follow-up fund and we conjecture that
more quickly private equity firm will spend the capital available within current funds, more quickly it will
2
need to raise a new fund. We conjecture that this trade-off may be affected by the degree of specialization
of PE firms as it affects the speed at which new investments are made and the timing of exits (Knill, 2009).
In line with the above discussion, the first contribution of the paper is to ascertain if specialized PE firms
are more active in fundraising activities. We consider specialization across two dimensions: stage and
industry. Using a large sample of US private equity firms, we put forward three different approaches to
measure specialization. First, we measure firm level specialization according to the actual number of
investments made in each stage/industry. Second, following Gompers et al. (2009), we measure
specialization across each dimension according to a Herfindahl-Hirschman concentration index. Finally, to
measure firm-level diversification, we follow the approach adopted by Knill (2009) in that we calculate for
industry (stage) specialization the number of industries (stages) a PE firm has invested prior to raising a
new fund as a fraction of all possible industries (stages) in which the PE players (i.e., all players in the
market, not just the one considered) invested. Next, we estimate hazard and competing risks model which
allows us explore how specialization is linked with the time span between two consecutive fundraising of
the same PE firm as well as the trade-offs of raising specific types of funds. Across stage dimension, we
consider two main types of funds: buy-out (BO) and venture capital (VC) funds as they represent the
principal portion of US PE deals. Across industry dimension, we compare dynamics of fundraising with
investment focus in high-tech versus non-high-tech industries.
Next, we examine the impact of fundraising time on the performance of the fund. While the literature does
not provide any evidence on the subject, it provides important information for fund investors willing to
commit capital to the fund. Observing the time between two consecutive fundraising, fund investors can tell
how quickly PE firm will invest and return the capital committed. If the duration between two consecutive
fundraising is short, fund managers of the same private equity firm may be still in the process of portfolio
selection for the current fund. As the supply of fund managers is fixed in the short run, this should either
delay the portfolio selection period of the new fund or negatively affect the quality of deal selection due to
the limited available time for each new investment. Therefore we expect that shorter duration between
funds may be associated with relatively slow deal selection or lower performance of the newly raised funds,
while longer durations between two funds may be associated with improved screening of good investment
opportunities and higher quality of value-adding. We test the underline link between fundraising time and
performance by estimating a Tobit model for fund success rates. We follow Hochberg et al. (2007) and
measure fund success rate by the fraction of the fund’s portfolio companies that have been successfully
exited via IPO or M&A transactions.
Our analysis delivers the following interesting results. First, we find that specialized PE firms are faster at
raising new funds than more diversified firms. Then we show that this holds only if specialized firms are
raising new funds in their area of proficiency as fundraising is delayed for follow up funds outside their area
of expertise. This suggests that expertise cannot be easily extended to other areas and any drifts away come
3
at some costs. Third, we show that number of recent and past IPOs enhances fundraising speed implying
that past and recent investments success allows PE fund to attract new capital commitments. Next, we find
that ability to quickly select portfolio companies also speeds-up follow-up fundraising. Finally, we provide
the implications for fund managers and fund investors observing long durations between two consecutive
funds raised by the same PE firm. We show that, all else equal, longer duration translates into better
performance of the newly raised fund expressed by higher IPO exit rate, implying that there exists a tradeoff between quick fundraising and the performance of the newly raised fund which fund managers should
mind before launching a new find. Although quicker fundraising is preferred by an average PE firm as it
increases the assets under management, our findings indicate that time till raising a new fund enhances fund
performance via increase in time resources available to fund managers for proper screening of good
investment opportunities as well as for their value-adding activities. This further implies that fund investors
should also mind committing their capital into funds which has been raised shortly after a firm raised its
previous fund.
We make the following contributions to the literature on the topic. First, although the previous studies on
PE investment activities outline the importance of time in PE fundraising (e.g., Gompers, 1998), time
impact of specialization has not been yet investigated. Therefore, our study complements exiting studies on
the fund-level specialization in private equity (Ljungqvist and Richardson, 2003; Han, 2009). Second, we
also provide the first evidence on the link between fundraising time and performance. We show that there
exists a trade-off between quick fundraising and the performance of the newly raised fund that fund
investors should mind before committing their capital to the new PE fund. Finally, we provide evidence on
the impact of other firm-specific factors on fundraising time, such as firm experience, past and recent
performance and the speed at which the fund portfolio is selected.
The remainder of this paper is structured as follows. The next section presents our hypotheses. II describes
our empirical methodology. Section III describes data, the variables employed and the summary statistics.
Section IV tests our hypotheses. Section V concludes.
I. Hypotheses
In this section we discuss our hypotheses. Subsection A presents our hypotheses regarding fundraising
determinants. Subsection B discusses the potential link between fundraising time and fund performance.
4
A. Determinants of Fundraising
The literature on PE fundraising outlines several market-specific and firm-specific factors that determine
fundraising. For instance, Poterba (1989) shows that many of the changes in fundraising could arise from
changes in either the supply or the demand for venture capital. Decreases in the tax rate on capital gains
increases commitments to venture capital funds, while higher GDP growth and increases in R&D spending
lead to greater VC activity. Huson et al. (2006) study how general market conditions influence the level of
private equity activity. They argue that public market conditions influence the choice between public and
private sources of capital and the bargaining power of investee firms. They find that private placements are
more likely to occur following periods of relatively high stock market returns.
Regarding firm characteristics, Cumming et al. (2005) find that reputation in the form of firm size
positively affects the ability to raise new funds. Lerner et al. (2007) indicate that PE firms often obtain
capital commitments from limited partners who invested in previous funds for which performance
objectives were met. Gompers and Lerner (1998) find that VC firms that hold larger equity stakes in
companies that have recently gone public raise funds with greater probability and raise larger funds. Thus,
we conjecture that PE firms often capitalize on their reputation and previous success.
However, given the closed-end structure of most funds (Kaplan and Stromberg, 2008), capital is not raised
continuously and PE firms will launch a new fund according to their capacity and the need to do so. PE firm
that has recently raised a follow up fund will take longer to launch a subsequent fund than a firm that has
not done so for already several years. Therefore, to better understand the fundraising motivation and the
strategy adopted by a PE firm, next to fundraising likelihood, time of fundraising should also be analyzed. It
will shed light on whether a PE firm raises a new fund to take an advantage of the favorable PE market
conditions and their successful past performance, or needs to do so having exhausted a substantial portion
of a capital committed to current funds.
Thus, we hypothesize that the quicker a PE firm will spend the capital available, the quicker it will raise a
new fund. To test this, we examine the impact of firm specialization on fundraising as it is associated with
various potential costs and benefits for future investment activity. One benefit is the accumulation of
expertise with specialization (Michelacci and Suarez, 2004; Inderst and Mueller, 2004; Jovanovic and
Szentes, 2007; Sorensen, 2008).2 Firms that have made several investments in a given industry have
accumulated knowledge that helps improve future investment selection as well as quality and the speed of
value-adding in investees that are active in the same industry or stage of development. Indeed, Gompers et
al. (2009) shows that firm-level specialization affects success of individual investments, suggesting spill2
A further aspect is portfolio risk, which might increase due to a lack of sufficient diversification. Since PE firm
managers are generally active investors, specialization might also reduce risk as managers make investments in
companies they know better than managers who invest across a large number of industries, investment stages and
geographical locations with less information at hand. However, linking risk to future fundraising (our topic of
analysis) is unclear from this perspective. Therefore we do not elaborate costs and benefits of portfolio risk here.
5
over effects between funds of a same PE firm. On the contrary, Knill (2009) finds that greater
diversification at the firm-level increases investment durations (time between investment and exit) in VC
companies. Hence, we expect that specialization via enhancing value adding and exit speed can accelerate
the speed at which PE firms spend the capital available within current fund and accelerate the future
fundraising.
Next to timing, the current specialization of PE firms may also affect the characteristics of follow-up funds.
If expertise through knowledge accumulation matters, a PE firm specialized in buyout transactions will
find it more difficult to start a VC fund than a VC-specialized PE firm, as these two asset classes – although
both being private equity transactions – involve very different types of companies and expertise (Kaplan
and Stromberg, 2008). Similarly, a PE firm specialized in the internet industry may find it difficult to
suddenly shift to biotech ventures. Therefore, we expect a PE firm to raise more often follow-up funds of
the same “type” (i.e., similar to their area of specialization) if expertise cannot easily be transposed to other
areas.
Finally, Gompers (1998) states that individual time constraints of VC fund managers lead to a particular
number of enterprises that each VC manager can select monitor and support, while supply of experienced
venture capitalists is fixed in the short term. This further affects the number of portfolio companies that
fund managers can invest in (Cumming, 2006; Bernile et al., 2007; Bottazzi et al., 2008). We conjecture
that busyness of fund managers can determine their availability for the new investments. We expect that
the busier fund managers, less time they have to devote to exploring new investment opportunities or
attracting new capital commitments for them and thus, less frequently they raise a new fund.
B. Fund Performance
In this section we discuss the determinants of fund performance. We then build our key hypothesis on the
link between fundraising time and the performance of the newly raised fund.
The literature on the determinants of PE fund performance can be divided into two streams. The first one
assesses the link between public and private capital market performance. For example, Phalippou and Zollo
(2005) find that PE fund performance co-varies positively with both business cycles and stock-market
cycles. It increases significantly with the average GDP growth rate and decreases with the average level of
interest rates. Moreover, macroeconomic conditions are found particularly important at the time
investments are made. When either credit spreads or corporate bond yields are low at the time investments
are made, fund performance is higher. More recently Harris et al. (2012) compared buyout and venture
capital returns to the returns produced by public markets. They find that average U.S. buyout fund
performance exceeds that of public markets more than 3% per year and by 20% to 27% over the life of the
6
fund. Average U.S. venture capital funds, on the other hand, outperformed public equities in the 1990s, but
have underperformed public equities in the 2000s.
Another stream of literature focuses on PE firm and fund characteristics as drivers of fund performance. For
instance, Kaplan and Schoar (2005) find that the relation between fund performance and fund size is
increasing and concave. Moreover, they show that returns are persistent across a sequence of funds
managed by the same VC firm and argue that it may be a result of investment skill and experience.
Hochberg et al. (2007), in addition, study the performance consequences of the network centrality and find
that better-networked VC firms experience significantly better fund performance, as measured by the
proportion of investments that are successfully exited through an IPO or a sale to another company.
We hypothesize that among other characteristics, time of fundraising may also matter for fund future
performance as it may affect average investment and exit speed. Kaplan and Schoar (2005) indicate that
general partners (fund managers) have an agreed time period in which to invest the committed capital usually on the order of five years. They also have an agreed time period in which to return capital to their
limited partners (LP) - usually on the order of ten to twelve years in total. Thus, upon inception of the next
fund, general partners are obliged to start seeking for good investment opportunities not to delay investment
and exit process from their future portfolio companies and return capital to the LPs on time. We believe that
if the time elapsed between two consecutive fundraising is short, portfolio selection period for two
consecutive funds may coincide. As the general partners of the same PE firm co-manage their funds3 and
the supply of fund managers is fixed in the short run (Gompers, 1998), increased amount of task per partner
may influence his availability and the degree of his involvement in screening the good investment
opportunities. This will finally be reflected on the quality of deals selected. Moreover, limited availability
of general partner will also affect the quality of value adding to selected portfolio companies. Thus, we
conjecture that reduced available time for each investment selection and value adding process will
eventually affect the final performance of the fund. Therefore, we expect that shorter duration between
funds may be associated with lower performance of the newly raised fund. On the contrary, longer
durations between two consecutive fundraising may enhance the fund performance via increased
availability of general partners during portfolio selection and value adding process.
II Empirical Methodology
In this section we present our methodology. As our dependent variable is the duration between successive
funds (raised by the same PE firms), our empirical modeling is based on survival analysis. More
3
This implies not only participation into portfolio selection for each fund but also in monitoring and value-adding
activities.
7
specifically, we employ the hazard model to study duration till follow-up fundraising and we employ the
competing risks model to jointly model the time and the type of future fundraising. In this section we
describe each of them separately.
A. Hazard Model
The hazard model is a powerful model to study the duration (also called time spell) until the occurrence of
an event. It delivers a hazard function, which summarizes the concentration of spell length at each point of
time conditional on the time already elapsed since the beginning of the spell. In our setting, fundraising of
the next fund represents the event; the number of years elapsed from the current fund to the follow-up fund
is the duration to model.
In the framework of survival analysis, the duration between raising two successive funds can be understood
as a realization of a continuous random variable T. The continuous time hazard rate represents the
probability of observing a duration of t, conditional on the survival up to time t. Following the rules of
conditional probability, the hazard function can be represented as: λ /, where λ is the
hazard function, is the probability density function of t, and is the survivor function up to time t.
can be defined as Pr 1 , is the probability distribution function of t.
The advantage of the model lies not only in rigorous statistical modeling of time between fundraising
events, but also in the possibility to fully parameterize fundraising hazard by exogenous covariates
(explanatory variables). In our model, the main covariate for fundraising time is the PE firm specialization
(as hypothesized in the previous subsection).
Let us represent the link function of the observed time spell t between two successive funds and our firm
specialization measure in our model as:
where ln(t) is the natural logarithm of t, Specialization is the firm specialization variable, indicating that
prior to raising a new fund, all the firm investments are concentrated in one specific stage or industry,4 and
z is the error term with the distribution function F(z), which describes the dynamics of fundraising hazard
across time. For example, if z is exponentially distributed, we have constant hazard (exponential
distribution). From an economic point of view, the constant hazard implies that the follow-up fundraising
probability is as likely after one month as after one year. If z follows Weibull distribution, then the
fundraising hazard is monotonically increasing or decreasing, implying that as the time passes from recent
fundraising, the likelihood of launching a follow-up fund gets either larger or smaller over time.
4
The detailed description of our specialization measures is provided in Section III.
8
In the empirical application, we choose the gamma density distribution function as the density distribution
of the underlying error term since it is very flexible and encompasses many density distributions.5
B. Competing Risks Model
Up to now we have described the hazard model which allows us to explain the duration till the next
fundraising. In this section we describe the competing risks model which, in addition to studying time until
the next fundraising, also differentiates between many possible types of funds PE firms can raise. Such
distinction allows us to explore how the area-specific expertise is linked with the time and the trade-offs
between raising specific types of funds.
The competing risks model is the generalization of the hazard model, as it takes into account that the time
until the next fundraising can end by raising one of many possible types of funds. The model takes into
account the dynamics of each type of fundraising and allows covariates to influence corresponding
fundraising decisions separately. This implies that we can estimate a separate hazard function for each type
of fundraising. The dependent variable in each model will be the duration which ends by raising a specific
type of fund.6
Let us represent how the covariates enter the hazard function for the corresponding fund type. We
distinguish between different types of funds across asset class and industry dimension. Across asset classes,
we consider competing risks of raising either a VC or buy-out fund.7 Across industries, we analyze raising
the funds with alternative industry focuses (high-technologies versus non-high-technologies).
5
We estimate the model by maximizing the likelihood of the following gamma density function:
f (t , z , σ ) =
γγ
e( z
σt γ Γ (γ )
γ −u )
if ĸ ≠0, and
f (t , z,σ ) =
2
1
e ( − z / 2) if ĸ=0, where γ |ĸ| , σt 2π
"#ĸln µ/ σ, & ' | ĸ | ( with ĸ and σ parameters affecting the shape of the hazard function, and t is the
observed time spell to model. Finally, our covariates enter the equation in the following
form: ) .
The likelihood function to be maximized will be represented as ∏,
+ + , , -, where i indexes the observation
number, and N is the total number of observations in the data. As the dependent variable is time, a negative value for
any parameter estimate implies that an increase in the corresponding variable lens to a faster fundraising.
6
It can be described by a pair (t, y), where t is the duration between two consecutive funds and y a dummy variable
taking the value one if the type of the newly raised fund is i, and zero otherwise. The duration characterized by y=1
will contribute to the likelihood function via its density function. The duration characterized by y=0 will be right
censored and will contribute to the likelihood function via its survival function. See the previous footnote for the link
between hazard function and survival function.
7
Our model accounts for the fact that a PE firm also raises other than VC or buy-out fund. The duration that ends by
raising this other types of fund will be treated as right censored and will contribute to the analysis of both VC and buyout fundraising via the survival function. Therefore, it does not lead to any bias. However, analyzing this other type of
funds explicitly is beyond the focus of this study. Most importantly, our sample is too small to be able to provide
meaningful estimates for these other types.
9
For the asset-class dimension we thus have two different hazards (which depend on the outcome), each
hazard being shaped by its own parameters such as:
ln(t )VC FUND = β10 + β11 Specialized in VC + β12 Specialized in buy − out + z1
ln(t ) BUY −OUT FUND = β 20 + β 21Specialized in VC + β 22 Specialized in buy − out + z 2
where Specialized in VC and Specialized in buy-out is the covariate measuring the firm specialization in
each asset-class, respectively; z1 ( z 2 ) is the error term of the corresponding model.
As for the industry dimension we will have:
ln(t ) HIGH −TECH FUND = α10 + α11 Specialized in high − tech industry +
+ α12 Specialized in non − high − tech industry + e1
ln(t ) HIGH −TECH FUND = α 20 + α 21 Specialized in high − tech industry +
+ α 22 Specialized in non − high − tech industry + e2
where Specialized in high-tech industry and Specialized in non-high-tech industry are the covariates
measuring a firm’s specialization in each industry, respectively and e1 ( e2 ) is the error term of the
corresponding model. Detailed construction procedures of all the employed variables will be explained in
section III.
Additional information on survival analysis and/or competing risks models can be found in Lee and Wang
(2003). In VC studies, such models have been put forth by Gompers (1995), Cumming and MacIntosh
(2001) and Giot and Schwienbacher (2007).
III. Data and Variables
In this section we describe the data resources, the variables employed and the summary statistic. The data
used for this study is extracted from the Thomson Financial SDC database VentureXpert. It provides
detailed information on PE firms, the funds they raised and the portfolio companies in which they invested.
Although VentureXpert started collecting data in 1977, we choose to begin our sample from 1980 as the
venture capital as we know it today only took off since then. We limit our data set till 2005, as
VentureXpert does not provide information on sequence number of each fund anymore since 2005. This
10
information is critical for our analysis to ensure that we do not miss any fund.8 Our initial sample
encompasses information on 2295 PE firms, who raised 4403 funds and invested in 22,116 companies.
Since the initial sample contains significant number of missing values, we filter it as follows: first of all, we
remove observations for which we miss company level information on investment characteristics (industry,
stage, financial round date). We use company level information to identify the pattern of investments for
our specialization measures. Filtering leaves us with 14,204 company level observations; after construction
of our firm level specialization measures, this aggregates to 2453 fund-level observations from 1276 firms.
We next filter the data to construct the variable Duration. We define it, for a given PE firm, as the number
of years elapsed from previous fundraising till raising the next fund. To construct this variable we use the
information on fund sequence number and fundraising date. As both contain significant number of missing
values, we filter against them and maintain the sample of firms with only consecutive history of
fundraising. Further, as the construction of each observation of Duration requires the initial information on
two consecutive funds per firm, we also remove from the data the firms having raised only single fund. This
leaves us the sample of 1490 funds raised from 1982 to 2005, which translates for 763 observations of
Duration corresponding 386 firms.
Finally, we lose 15 observations per fund since some funds do not provide information on exit routes for
their portfolio companies. As a result, our final database reduces to 748 observations per fund raised from
1982 to 2005, corresponding 375 firms.
A. Variable Description
Table 1 provides an overview of all the employed variables. In this section, we provide detailed information
on their construction.
For ease of explanation let us consider a PE firm that has raised N funds. This firm will contribute to the
data with N-1 observations corresponding each fundraising starting from the second fund. The observation
corresponding to the first fundraising will be removed from the analysis since the time before raising the
first fund cannot be defined as the time between two consecutive fundraising events.
We define PE firm specialization across two distinct dimensions: stage and industry. Any of our
specialization measures captures the accumulated expertise a PE firm possesses at the time of
corresponding fundraising. It takes into consideration the number and the characteristics of the investments
8
However, limiting the data to 2005 also creates an advantage. Such an advantage is the ability to obtain
refined measures of fund success rate variables measured as a fraction of the fund’s portfolio companies
that have been successfully exited via an IPO and M&A transaction (Hochberg et al., 2009). Extending the
data till 2012 would result in downward biased measures of success as majority of the funds raised after
2005 will have principal portion of active (i.e. not yet exited) investments in their portfolio.
11
made by a PE firm before raising each fund. For the above firm, the number of investments made before
raising the Kth fund, where 2≤K≤N, counts the investments made by all the previous K-1 funds before the
fund number K was launched.9 Below we refer to this set of past investments as firm investments.
We construct specialization measures across each dimension using 3 different approaches. First, we
construct the Stage (Industry) specialization dummy employing the information on the actual number of
investments a PE firm has made so far in each stage (industry).10 According to the stage classification
variable there are six stages in our data: seed/start-up, early, later, expansion, buy-out/acquisition, other.11
According to the industry classification variable we consider 10 different industries: computer hardware,
computer software, communication/media, internet, semiconductors, biotech, medical/health, consumer,
energy and other products. The Stage specialization dummy takes the value one if more than 65% of firm
investments are concentrated in one of the five possible stages, and zero otherwise. Similarly, the Industry
specialization dummy takes the value one if more than 65% of firm investments are concentrated in one of
the ten possible industries, and zero otherwise. The 65% of threshold level accounts for the fact that PE
firms rarely invest all their capital in one industry or asset class in order to reduce the idiosyncratic risk
associated to their investments.12
Second, we follow Gompers et al. (2009) by constructing Herfindahl-Hirschman indexes (HHI) for stage
and industry dimensions. The Stage HHI is the sum of squares of the shares of firm investments at each
technological stage done so far. Similarly, the Industry HHI is the sum of squares of the shares of firm
investments in each industry. The higher the index, the higher is the specialization degree of a firm. In the
extreme case where a firm has made all investments so far in a single industry, the Industry HHI would
equal to 1.13
Third, we construct diversification indexes as in Knill (2009). Stage (Industry) diversification index
calculates the number of stages (industries) a PE firm has invested before raising corresponding fund, as a
fraction of all the possible stages (industries) in which PE players invest according to our data. For example,
if before raising the corresponding (new) fund, a PE firm has only invested in four different stages, the
Stage diversification index for the firm at a given point of time equals 4/6 (Total number of possible stages
9
While some of the previous funds can also make investments after a PE firm raises a follow-up fund, our
specialization measures take into account the investments made by all the previous funds before the follow-up fund
was launched. As robustness, we also estimated the model using window of investments made 1, 2, 3 years before
corresponding fundraising. We obtained qualitatively similar results as the ones reported in Section IV.
10
We use the information only on the first round investments a PE firm makes in its portfolio companies to exclude
the ambiguity caused by the multiple round investments in a company.
11
The stage classified as “other” in the VentureXpert database does not represent one specific stage. Hence we do not
consider it as a specialization category.
12
We choose a threshold level of concentration (here, 65%) so that a clear separation is made between specialized and
unspecialized firms. We also estimated the model using other threshold levels and we obtained qualitatively similar
results as the ones reported in Section IV.
13
Alternative example: if by the time of corresponding fundraising, a firm has done 30 investments out of which, 15
are early stage deals, 5 are expansion and 10, respectively, later stage deals, than the Stage specialization index for this
firm equals: 15/30 5/30 10/30 1/4 1/36 1/9 7/18.
12
according to our data equals six: seed/start-up, early, expansion, later, buy-out/acquisition, other). Thus, the
higher the measure, the greater is the extent of diversification.
Next we construct the variables identifying the type of the newly raised fund. With respect to asset classes,
we distinguish between two types of funds a PE firm can raise and construct two dummy variables: VC fund
and BO fund, taking the value one if the fund investment type is VC, respectively, buy-out, and zero
otherwise. As far as the industry is concerned, we also construct two dummy variables: High-tech fund and
Non-high-tech fund. High-tech fund takes the value one if the fund industry focus is in high-technology
companies, and zero otherwise. Similarly, Non-high-tech fund takes the value one if the fund industry focus
is in non-high-technology companies, and zero otherwise.14
We also construct area-specific specialization variables. According to the asset class dimension we
distinguish between two areas of specialization for which we create two dummies: Specialized in VC and
Specialized in buy-out. We refer to a PE firm being specialized in VC (buy-out) if 65% of firm investments
fall in VC (buy-out) asset class. Following the same procedure, we also construct two industry-specific
specialization dummies: Specialized in high-tech industry and Specialized in non-high-tech industry. The
corresponding dummy takes the value one if 65 % of firm investments are concentrated in high-tech (nonhigh-tech) industries, and zero otherwise.
To disentangle the impact of specialization on PE fundraising, we construct following control variables.
First of all, we expect that overall experience in current fundraising may facilitate future fundraising as
conducting activities repeatedly and learning by doing, also called experiential learning, is the roadmap of
future success (Yang et al., 2009). Second, there is strong evidence that past performance affects the level
of assets under management (see, e.g., Chevalier and Ellison, 1997, 1999, Sirri and Tufano, 1998, Elton et
al., 2001, and Knill, 2009). To account for the past performance of a firm, we construct Number of past
IPOs as the number of IPOs done by firm before launching a current fund.15 Further, we think that success
of active investments may also matter for fundraising as Gompers and Lerner (1998) find that VC firms that
hold larger equity stakes in companies that have recently gone public raise funds with greater probability
and raise larger funds. We proxy this variable by the number of IPOs done by a PE firm within the first six
months from current fundraising.16 Next, we construct Portfolio selection speed. Kaplan and Schoar (2005)
indicate that an agreed time period in which fund managers invest committed capital equals about four or
five years. We measure Portfolio selection speed as a fraction of fund investments done within the first two
14
As we do not have information on the fund industry focus in our data, we again rely on the portfolio company level
information for the identification. We refer a fund to have specific industry focus if more than 65% of fund’s
investments are concentrated in one specific industry.
15
We calculate this variable using the information on the status date per fund. However, our initial database contains
important number of missing values for the company status date. To avoid reducing our sample further, due to filtering
against missing values, we proxy status date by the average company exit time in each industry and stage per each
fund calculated using non-missing data.
16
While we do robustness check using 1, 2, 3 years from previous fundraising, we prefer to use shortest possible
threshold level to take into account the fact that duration between funds can be shorter than a year. However, our
results using longer than 6 months thresholds stay qualitatively similar.
13
years from its launching.17 Further, we also account for the busyness of the firm by the number of active
investments per executive at a time of current fundraising. Finally we account for the private and public
market conditions. To control for the supply of PE, we employ total amount of PE capital raised one year
prior to current fundraising. Lagging the PE capital supply by one year, we take into consideration that
market players base their future actions to their current expectations. We take into account stock market
conditions by controlling for the Yearly returns on S&P500 securities at a time of current fundraising.
Similar to the PE capital supply, we also lag this variable by one year.
B. Summary Statistics
Table 2 provides the summary statistics of the variables employed in the analysis. It evidences that on
average PE firms raise new funds once every three years as the number of years elapsed since raising the
previous fund equals 3.02. However, there is a substantial variation in the variable Duration with 15 years
as a maximum and 4 days (0.01 year) as a minimum. A very low value generally occurs for PE firms who
raise two funds almost simultaneously within the same fundraising campaign. This is, however, very rare in
our sample. Table 2 also indicates that on average fraction of IPOs per total number of fund investments
equals 15% for an average fund. While the same statistics for IPO and success full exits equals 58%. Mean
number of IPO exits for an average fund equals about 3 and for Successful exit rate, respectively, 11.
Regarding other control variables, Table 2 shows that PE firm experience (measured by fund sequence
number) ranges from 2 to 21. Firm busyness expressed by the number of active investment per executive
varies between 0 and 54. Minimum at 0 occurs for the firms who have raised only one fund so far as the
number of investments when duration starts equals zero for such firms. The same is true for Number of past
IPOs and for Number of recent IPOs.
[Table 2 about here]
Table 3 compares the average firm specialization to the average fund specialization and summarizes their
dynamics across time. As Table 3 evidences, PE funds are on average more specialized than PE firms. This
holds regardless of the dimension and the measure of specialization considered. According to the Stage
specialization dummy, the percentage of specialized funds equals 50% versus 40% of such firms.
Respectively, the proportion of funds specialized across industries equals 33% versus 22% of such firms.
Regarding the dynamics of specialization across time, Table 3 indicates that the degree of specialization
increases over time both on fund- and firm-level (from the first (1982-1988) to the last (2000-2005) subperiod). However, fund specialization exhibits more significant dynamics than firm specialization.
17
Again, decreasing or increasing the threshold period delivers qualitatively similar results. However, the magnitude
of impact is bigger for shorter than 2 year thresholds and smaller otherwise.
14
Interestingly, during the first two sub-periods, firms are more specialized than funds across stage
dimension, while for the last two sub-periods, the opposite is true.
[Table 3 about here]
Panel A of Table 4 compares subsamples based on our three measures of specialization/diversification
variables. It shows that specialized PE firms across stage dimension exhibit shorter durations between
consecutive funds. This holds for both measures of specialization. The differences in means are statistically
significant at the 2% of significance level for both the Stage specialization dummy and the Stage HHI. As
for the industry dimension, p-value for the same statistics equal 10% for Industry specialization dummy and
is insignificant for Industry HHI. Table 4 also indicates that PE firms that are diversified across industries
exhibit longer durations between successive funds.
Panel B of Table 4 compares subsample of longer than mean duration (3 years) to that of shorter than mean
duration. It shows that longer duration between funds are not associated with increased fund performance as
differences in means are not significant for any of the two exit rate variables.
[Table 4 about here]
Table 5 displays the correlation matrix for the main variables in the model. It indicates that PE firms
diversifying across stages also diversify across industries as the correlation between the Stage
diversification index and the Industry diversification index is significant and equals 0.76. Similarly, private
equity firms specialized across stages are likely to be specialized across industries, as the correlation
between Stage specialization dummy and Industry specialization dummy equals 0.33. The same magnitude
according to the HHI indexes equals 0.61. Finally, we notice that the stage (industry) HHI is strongly
correlated with the stage (industry) specialization dummy. Thus, we conclude that both measures appear to
capture the same effect.
[Table 5 about here]
IV. Empirical Analysis of Fundraising Dynamics
In this section, we analyze fundraising dynamics and describe our estimation results. Subsection A and B
explores the fundraising determinants and the link between fundraising time and specialization. Subsection
C discusses other factors potentially affecting fundraising activities and explores the robustness of our
results. Subsection D examines the link between time till raising a follow-up fund and the performance of
that fund
15
A. Does Firm Specialization Enhance the Fundraising Activities?
To assess the impact of firm specialization on the duration between raising two consecutive funds, we
estimate the hazard model described in Section II. Table 6 describes our estimation results. As we define
specialization using different approaches (see Section III.1), we estimate separate sets of hazard models for
each of them. Due to high correlation between stage and industry specialization variables (see Table 5), we
control for each dimension of specialization separately.
Panel A of the Table 6 describes our results while controlling for specialization dummy variables. Panel B
presents our results using specialization index variables and finally, Panel C describes our estimation results
for diversification indices. Columns 1-3 in each table present results for stage dimension, columns 4-6,
respectively, for industry dimension. Since our dependent variable is time spell, a negative value for any
parameter estimate implies that an increase in the corresponding variable leads to faster fundraising.
[Table 6 about here]
According to our estimation results, firm specialization across any dimension accelerates new fundraising
as indicated by the negative coefficient of specialization variables in Panel A and B of the Tables 6. We run
the model according to increasing degree of complexity. The coefficient of specialization varies across
distinct specifications.
According to our simulation results, the predicted median time for the follow-up fundraising for an average
firm, having already raised 4 funds, equals about 3 years (950 days). Column 1 and 3 of Table 6 Panel A
indicate that specialization across stage decreases the median survival time by about 19%. In column 2 we
replace Firm experience by Number of past IPOs. Since the later proxy of fund performance is highly
correlated with Firm experience (0.67), we therefore include each of them separately in the model.
Excluding Firm experience from the model delivers lower coefficient for Stage specialization dummy
(0.17). However, in all specifications, Stage specialization dummy stays significant at 1% of significance
level.
We observe the same picture for the industry dimension. As shown by the coefficient of Industry
specialization dummy in columns 4 and 6 of the Table 6 Panel A, specialization across industry decreases
the median survival time by about 16%. The same impact according to the column 5 equals 11%.
To sum up, Table 6 shows that whatever the specialization/diversification measure used, our results confirm
that specialized PE firms are more productive in fundraising compared to non-specialists. Thus, in line with
our prediction developed in Section I, we conclude that asset-specific skills and expertise allow PE firms to
generate time benefits that can be channeled to follow-up fundraising activities. Hence, while the literature
indicates that generalist firms underperform specialists (Gompers et al., 2006), we find that the generalists
also are slower in raising follow-up funds.
16
Regarding other control variables, we find that the general experience in past fundraising allows PE firms to
raise new funds more quickly as indicated by a negative coefficient of the variable PE firm experience. This
result is in line with the experiential learning theory as discussed earlier (Yang et al., 2009). Furthermore, in
line with Poterba (1998) and Gompers (1989) our results show that higher the past performance quicker a
PE firm raised a new fund. However, we notice that the coefficient of Number of past IPOs becomes
insignificant while adding into the model PE firm experience (see columns 2 and 4). This is expected taking
into account the high correlation between the two. Table 6 further shows that the bigger the Number of
recent IPOs, the quicker the follow-up fundraising. Thus, in line with Gompers and Lerner (1998) our
results confirm that recent successful capitalization assists PE firms in rapidly attracting new capital
commitments. Another interesting finding is that that the quicker a PE firm selects the portfolio for the
current fund, quicker it raises a new fund as shown by the significant and negative coefficient estimate of
Portfolio selection speed.18 Regarding Firm busyness expressed by the number of active investments per
executive and PE capital supply, our estimation results indicate that they do not significantly alter the
fundraising speed19. Finally, we find that Yearly returns on S&P500, facilitate quick fundraising as we
would expect from existing literature (Poterba, 1989; Black and Gilson, 1999), however the significance of
the variable varies among different specifications.
Next we control jointly for industry and stage specialization variables to compare their relative impacts on
fundraising speed. Columns 1-3 of Table 7 present results while measuring specialization using each
approach. We notice that in all specification coefficients of stage and industry specialization variables
decline. However, stage specialization-specific variable stays significant, while the coefficients of industry
specification variables become either insignificant (in columns 1 and 3) or its significance reduces to 10%
(column 2 of Table 7). We conclude that high correlation between industry and stage specialization (see
table 5) makes it difficult to judge their relative impacts as industry specialization is significant in our
previous models.
[Table 7 about here]
B. Is the Effect Different Outside the Firm’s Area of Specialization?
In the previous subsection, we concluded that specialized PE firms raise new funds faster. In this section we
differentiate between different possible types of funds a PE firm can raise and test the specialization impact
on fundraising speed when the investment preferences of the newly raised fund coincide (or do not
coincide) with those of the current specialization area of a PE firm.
18
As an alternative measure of Portfolio selection speed, we also tested the impact of the dummy variables taking the
value one if a fund made its first investment within 0.5 years since its launching and zero otherwise. We obtained
qualitatively similar results.
19
As an alternative measure of Firm busyness, we also tested the impact of the number of firm’s active investments
without scaling it by the number of firm executives. We obtained qualitatively similar results.
17
To examine the impact of specialization on the specific type of fundraising, we estimate competing risks
models as described in Section II. We look at the different types of funds within asset class (VC versus buyout) and industry dimensions. We run separate sets of models for each dimension. The dependent variable
in each model is the duration which ends by raising a specific type of fund.
Table 8 (Model I) presents estimation results of the competing risks models for VC and buy-out
fundraising. As shown by Columns 1 and 2, PE firms specialized in VC investments raise new VC funds
quicker than generalist firms. Similarly, raising a buy-out fund is accelerated for a PE firm specialized in
buy-out investments. According to our simulation results for an average firm the predicted median time for
VC fundraising equals about 3.3 years. Specialization in VC investments shortens this time by 32% (about
one year) as shown by the coefficient of Specialized in VC. In contrast, given that a firm is specialized in
buy-out investments, VC fundraising is delayed by about 3 years (100%). This implies that specialization
accelerates fundraising only if the investment preferences of the newly launched fund coincide with the
expertise area of a PE firm. However, fundraising in other areas is significantly delayed. This suggests that
PE firms specialized in a particular area are likely to stay specialized in that area also in the future and thus
capitalize on their accumulated expertise.
[Table 8 about here]
Columns 3 and 4 of the Table 8 (Model II) present estimation results of the competing risks models for
fundraising with alternative industry focus (high-technological versus non-high-technological industry).
Estimation results confirm that launching a fund with the specific industry focus is accelerated only if a PE
firm is specialized in the same industry. Similarly to asset-specific expertise, we conclude that expertise
benefits are relatively difficult to spread in other industries, suggesting that much of the expertise is
industry- and asset-specific rather than of general nature for any PE transaction. Interestingly, according to
our simulation results, for an average firm in our data median predicted time to raise the next fund is
shortest if the new fund type is VC and equals 3.3 years, followed by high-tech fund (3.7 years) and nonhigh-tech fund (7.6 year). The median time is longest for buy-out fundraising and equals about 9 years.
Regarding other control variables, results confirm our previous findings on firm-specific characteristics.
Concerning private market conditions, estimations of the competing risks models indicate that PE capital
supply enhances launching of all type of funds except VC fund. As for the public market conditions, strong
stock market returns accelerate launching a VC fund and the fund with high-tech industry focus.
Interestingly, our estimation results suggest that IPOs of recent investments do not matter for buy-out
fundraising while the later is significantly delayed for busy firms. One possible reason why the buyout
industry may be less sensitive to stock market conditions is that buyout deals involve significant amount of
debt financing and thus might be more sensitive to debt market conditions.
18
C. Additional Tests and Robustness Checks
First of all, we test the impact of PE firm reputation and performance-specific variables on fundraising time.
There is strong evidence that fund performance affects the level of assets under management (see, e.g.,
Chevalier and Ellison, 1997, 1999, Sirri and Tufano, 1998, Elton et al., 2001, and Knill, 2009). Gompers
and Lerner (1998) find that VC funds that hold larger equity stakes in firms that have recently gone public
raise funds with greater probability and raise larger funds. Moreover, Cumming et al. (2005) find that
reputation in the form of firm size positively affects the ability to raise new funds. Thus, we expect that the
larger the previous fund, the more successfully PE firms raise new funds. Similarly, past success in
fundraising should facilitate new fundraising. We examine the impact of reputation by controlling for the
size of the previously raised fund (measured in millions of dollars). To control for the previous success in
fundraising, we use a dummy variable taking the value one if the previous fund launched by PE firm
achieved its target size and zero otherwise. Doing so, our sample reduces to 449 observations due to the
large number of missing values for the fund target size. While controlling for these additional covariates,
we found that PE firms that achieved their target size in the previous fund indeed raise new funds quicker.
Previous success in fundraising therefore enables PE firms to capitalize from their success also in future
fundraising, which may in fact induce them to accelerate the raising of their next fund. As for the previous
fund target size, it has no significant impact on new fundraising.
Second, we estimate competing risks models for fundraising with narrower definitions of stage (industry)
focus. For example, instead of considering fundraising of two different asset classes (VC versus buy-out),
we examine three different asset specific hazards (early stage VC fund, later stage VC fund, buy-out
fund)20. Similarly, instead of investigating hazards of fundraising with two competing industry focuses
(high technologies versus non-high technologies), we examine three different industry focuses (information
technologies, medical/health, non-high technologies)21. Results are qualitatively similar to those reported in
the paper and confirm our finding that the expertise benefits are difficult to spread in other areas.
In the next step, we also study the impact of other market conditions on PE fundraising speed, such as IPO
market conditions (expressed by total annual number of IPOs) and bond market returns (expressed by US 3month Treasury bill yields). As these variables are highly correlated, we run separate regressions for each
market variable22. In each regression we also control for our specialization measures, PE firm experience
and PE capital supply. Our results indicate that both more favorable IPO markets and increased returns on
US 3-month Treasury bills facilitate new fundraising. Finally, we also control for year dummies to account
20
Similar to competing risks model for VC versus buy-out fundraising, this model also accounts for the fact that PE
firms can raise other than early VC, later VC or buy-out fund. Duration that ends by raising other type of fund will be
treated as right censored and will contribute to the analysis via its survival function. Therefore, it does not lead to any
bias. However, analyzing this other type of funds explicitly is beyond the focus of this study. Most importantly, our
sample is too small to be able to provide meaningful estimates for these other types.
21
All the industries in the data can be grouped into these three categories.
22
We lag each market variable by one fundraising following the procedure adopted for other market variables in our
study (see Section III).
19
for the effects of any general economic event or trend. Controlling for year dummies in the hazard model,
we obtain higher coefficients for our specialization variables and our main findings stay robust. We are
unable to control for year dummies in competing risks models due to the limited number of observations for
each fund type that turns the convergence of the maximum likelihood function very sensitive to the
inclusion of so many additional control variables in the model.
D. Fund Performance
The previous section studied the impact of PE firm specialization on its fundraising speed. In this section,
we examine the link between time till raising a follow-up fund (our variable Duration) and the performance
of that fund. We expect the time till raising a follow-up fund can affect performance via affecting the
investment speed at which a PE firm spends the capital available within current fund and/or needs to ensure
new funding. Investment speed of a PE firm may affect the quality of deal selection and value adding that
determines the final profitability of the firm. Thus, the underlined research question is interesting not only
for fund investors but also for fund managers willing to successfully capitalize from their investments.
To estimate the impact of Duration on fund performance, we employ Tobit models as the dependent
variable is the exit rate based on realized IPOs and trade sales; this variable varies between zero and one
and thus is censored from both sides. As additional control variables, we include Stage specialization,
Industry specialization, Firm experience, Fund size, fund type (BO fund) and Firm busyness. Table 9
presents our estimation results. The first two columns present estimation results for IPO and successful exit
rate respectively.
[Table 9 about here]
We find that duration between two successive fundraising events does not affect performance of the follow
up fund. This result may be driven by the endogeneity due to potential reverse causality from performance
to fundraising time. Indeed, our estimation results of hazard and competing risks model indicate that past
performance expressed by Number of past IPOs is an important determinant of fundraising time. Therefore,
to disentangle our estimates from endogeneity biases we turn to an instrumental variable (IV) approach. We
instrument Duration by the variables Number of recent IPOs and Portfolio selection speed, since our
previous estimation results indicate that these variables matter for follow-up fundraising. Columns 3-4
present results for instrumental variable Tobit models. Results indicate that duration between two
successive fundraising enhances fund performance by 22-28%, depending on the measure of performance
used (i.e., whether we only consider IPOs or also trade sales in our measure). Coefficients are statistically
significant now. Wald tests confirm the endogeneity of the variable Duration at 1% of significance level for
IPO exit rate and, respectively, at 10% for Successful exit rate. Therefore, we trust results using the IV
20
approach and conclude that while the impact of Duration on IPO exit rate is well established, the link is
less strong for the measure Successful exit rate.23
We also estimate the model for absolute number of exits per fund in lieu of scaling them by the number of
investments. Table 10 presents our estimation results. Results are qualitatively similar, however both
magnitude and the significance of the coefficients are much stronger than for exit rates discussed earlier.24
[Table 10 about here]
Taking into account the results obtained in this section, we conclude that time till raising a new fund
positively affects the performance of the fund. However, impact if more established for IPO exit rate and
less strong for Successful exit rate. This is expected, taking into account the trade sale is the universal
channel of exit and hence, does not require as much attention and devotion from PE firm managers as the
IPO exit option.
Regarding other control variables, Table 10 confirms that the bigger the fund size, better the performance of
the fund. This finding is in line with Kaplan and Schoar (2005). One interesting result is that the busier the
firm, better their funds performance.
V. Concluding Remarks
This paper looks at the impact of PE firm specialization on its fundraising activities and analyzes its linkage
with fund performance. More precisely, first, we identify specialization patterns focusing on two main
dimensions of expertise: stage and industry. Our definitions capture the basic idea that specialized PE firms
have accumulated extensive expertise in the specific stage or industry. Using hazard models, we explain the
time between two consecutive funds by degree of firm specialization across each dimension. Using
competing risks models, we also study the time between raising two consecutive PE funds together with the
types of the newly launched funds. On the next step, we estimate Tobit models to examine the impact of
duration on the performance of the newly raised fund and provide implications for fund investors.
23
To check for the goodness and the validity of our instruments, we also run standard two-stage OLS regressions, as
IV Tobit estimations do not report corresponding test statistics. The Cragg-Donald Wald statistic rejects
underidentification (i.e., confirms the relevance of instruments) at 1% of significance level, while the Sargan statistics
of overidentified restrictions confirm the validity of our instruments (P-value = 0.76).
24
We also run the robustness checks using redefined IPO (Successful exit) rate defined as the number of IPOs
(successful exits) as fraction of the number of fund’s exited companies in lieu of number of investments. However,
since the majority of funds in our sample have exited all their investments, these alternative measures deliver similar
results.
21
Our analysis delivers interesting results that can be summarized as follows. First, we find that specialized
PE firms raise new funds more quickly. Second, we show that this effect is driven by the funds being raised
within the same area of expertise. Specialized PE firms are able to raise follow-up funds faster than
generalists only within their area of proficiency, implying that expertise benefits are difficult to spread in
other areas. Finally we provide the evidence that, quick fundraising via reducing the time available to fund
managers for each additional investment can affect the quality of deal selection and of value-adding
process.
As a limitation of the study, one can consider the fact that we do not explicitly explore whether a
specialized PE firm takes active or passive role in governing the investment projects. Although the
definition of venture capitalist imply that VC is actively involved in monitoring and governing its portfolio
companies, it does not hold for other type of PE firm, for example, buy-out firm. Besides, while investing in
a company, venture capitalist can also choose between holding a lead or a non-lead investor’s status which
afterwards will determine his actual involvement in the portfolio company. Further research may
additionally account for the PE firm’s actual involvement in the company. Further research may also
investigate whether style drifting in PE fundraising is contingent upon certain firm, time or market
characteristics. Similarly, the decision to style-drift in PE fundraising may depend on limited partner’s
willingness to provide the capital observing the specialization of the PE firm and its style-drifting behavior
in new fundraising. Future research is needed to provide better understanding of the issue.
Despite its limitations, this study has important implications for fund investors committing their capital to
PE funds. We provide the evidence that observing longer durations between two successive fundraising
may be translated as good news as it increases the successful exit rate of newly raised funds. Concerning the
implications for PE firms, we show that specialization via accumulating asset-specific skills and expertise
enhances quicker future fundraising. Thus, we show that there exists a trade-off between quick fundraising
and the performance of the newly raised fund, which fund investors should mind before committing their
capital to the new PE fund.
22
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Working
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25
Table1.
Variable Definitions.
Data source: Thomson Financial SDC database VentureXpert unless specified
differently.
Variable
Definition
Dependent variables:
Duration
Number of years between launching current fund and the next fund by the same
private equity firm.
Number of IPOs made by fund as a fraction of total number of its investments.
IPO exit rate
Number of IPOs and trade sales made by fund as a fraction of total number of
Successful exit rate
its investments.
Selection variables:
VC fund
Dummy variable equal to one when the investment type of the PE fund is VC
and zero otherwise.
BO fund
Dummy variable equal to one when investment type of the PE fund is BO and
zero otherwise.
High-tech fund
Dummy variable equal to one if more than 65% of fund’s investments are
concentrated in high-technological industry (medical/health/life-science and
informational technologies) and zero otherwise.
Non-high-tech fund
Dummy variable equal to one if more than 65% of fund’s investments are
concentrated in non-high technology industry and zero otherwise.
Specialization specific
variables
for the hazard models:
Stage specialization dummy
Dummy variable equal to one when before raising a new fund the 65% of all the
investments made by a PE firm are concentrated in one specific stage (startup/seed, early, expansion, later, buy-out/acquisition) and zero otherwise.
Industry specialization dummy
Dummy variable equal to one when before raising a new fund the 65% of all the
investments made by a PE firm are concentrated in one specific industry
(computer, internet, life-science, health/medical, semiconductors, biotech,
communication/media, energy) and zero otherwise.
26
Stage Hehfindahl-Hirshman
The sum of squares of the share of firm investments in each stage measured by
Index (HHI)
the time of a new fundraising (Gompers et al., 2009).
Industry Hehfindahl-Hirshman
The sum of squares of the share of firm investments in each industry measured
Index (HHI)
by the time of a new fundraising (Gompers et al., 2009).
Stage diversification index
The number of stages a PE firm has invested (by the time of a new fundraising)
as a fraction of total market number of stages where PE players invest (Knill,
2009).
Industry diversification index
The number of industries a PE firm has invested (by the time of a new
fundraising) as a fraction of total market number of industries where PE players
invest (Knill, 2009).
Specialization specific variables for
the competing risks models:
Specialized in VC
Dummy variable equal to one when the 65% of firm investments by the time of
new fundraising fall in VC asset class.
Specialized in buy-out
Dummy variable equal to one when the 65% of firm investments by the time of
new fundraising fall in buy-out asset class.
Specialized in high-tech industry
Dummy variable equal to one when the 65% of firm investments by the time of
new
fundraising
are
concentrated
in
high-technological
industry
(medical/health/life-science and informational technologies) and zero otherwise.
Specialized in non-high-tech
Dummy variable equal to one when the 65% of firm investments by the time of
industry
new fundraising are concentrated in non-high technology industry and zero
otherwise.
Other control variables:
PE firm experience
The number of funds raised by PE firm, i.e., whether the given fund is the first,
the second, etc. for a PE firm.
Number of past IPOs
The number of IPOs done by PE firm till current fundraising.
Number of recent IPOs
The number of firm IPOs done in a year since current fundraising.
27
Portfolio selection speed
The fraction of investments done by the previous fund within the first year from
its fundraising.
Fund investment speed
Fraction of investments made by a fund within the first three years from
fundraising relative to the total number of investments.
Firm busyness
The number of firm's active investments at a time of current fundraising divided
by the total number of firm executives.
Fund size
Total amount of capital raised by a fund (in million USD)
PE capital supply
The total amount of PE capital raised by the US PE industry 1 year prior to
current fundraising, measured in million dollars (in logarithms).
Yearly returns on S&P500
Average annual returns of the monthly S&P500 index at the year prior to
current fundraising (source: Global Financial Database).
28
Table 2. Summary statistics for the employed variables. Variables are described in Table 1.
Variable
Obs. Mean Std. Dev.
Min
Max
Dependent variables
Duration
748
3.02
2.01
0.01
15.42
IPO exit rate
748
0.15
0.15
0
1
Successful exit rate
748
0.58
0.20
0
1
VC fund
748
0.79
0.41
0
1
BO fund
748
0.17
0.37
0
1
High-tech fund
748
0.57
0.49
0
1
Non-high-tech fund
748
0.18
0.38
0
1
Stage specialization dummy
748
0.42
0.49
0
1
Industry specialization dummy
748
0.22
0.41
0
1
Stage HHI
748
0.54
0.25
0.20
1
Industry HHI
748
0.40
0.26
0.12
1
Stage diversification index
748
0.50
0.22
0.17
0.83
Industry diversification index
748
0.48
0.27
0.10
1
Selection variables for the competing risks models
Specialization/diversification type variables for the hazard models
Specialization/Diversification type variables for the competing risks models
Specialized in VC
748
0.29
0.45
0
1
Specialized in buy-out
748
0.13
0.34
0
1
Specialized in high-tech industry
748
0.14
0.35
0
1
Specialized in non-high-tech industry
748
0.18
0.38
0
1
PE firm experience
748
3.94
2.66
2
21
Number of past IPOs
748
1.51
3.89
0
36
Number of recent IPOs
748
0.13
0.47
0
4
Portfolio selection speed
748
0.57
0.32
0
1
Firm busyness
748
2.41
5.60
0
54
PE capital supply
748
11.26
1.15
8.12
12.51
Yearly returns on S&P500
748
19.17
12.67
-15.48
41.43
Other control variables
29
Table 3. The dynamics of PE firm and fund specialization across time. Firm specialization variables are
constructed using the history of firm investments since its launching till the time of corresponding fundraising,
as defined in Table 1. Fund specialization measures are constructed based on the investments history of the
fund. Specialization on both levels is measured using three different approaches described in Section III. Panel
A presents the mean statistics for the specialization on stage dimension. Panel B provides the mean statistics for
the industry dimension.
Panel A: Mean statistics for the stage specialization (diversification) variables
stage
Stage
specialization dummy Stage HHI
Fundraising
diversification index
Firm
Fund
Firm
Fund
Firm
Fund
period
Observations
level
level
Level
level
level
level
1982-1988
119
0.42
0.34
0.52
0.48
0.51
0.51
1989-1994
107
0.41
0.37
0.54
0.50
0.50
0.50
1995-1999
263
0.38
0.49
0.53
0.57
0.50
0.50
2000-2005
259
0.47
0.64
0.56
0.68
0.49
0.49
Total sample
748
0.42
0.50
0.54
0.59
0.50
0.50
Panel B: Mean statistics for the industry specialization (diversification) variables
Fundraising
Industry
Industry
specialization dummy Industry HHI
diversification index
Firm
Fund
Firm
Fund
Firm
Fund
period
Observations
level
level
level
level
level
level
1982-1988
119
0.12
0.15
0.35
3.03
0.50
0.45
1989-1994
107
0.24
0.28
0.42
2.90
0.46
0.37
1995-1999
263
0.23
0.33
0.41
1.56
0.49
0.32
2000-2005
259
0.24
0.42
0.40
1.42
0.47
0.26
Total sample
748
0.22
0.33
0.40
1.94
0.48
0.33
30
Table 4. Test statistics for the differences in means across two different samples of PE firms.
Panel A: Means of Duration across specialized/non-specialized firms
Observations
Mean
Stage specialization dummy=1
314
2.81
Stage specialization dummy=0
434
3.16
Industry specialization dummy=1
162
2.77
Industry specialization dummy=0
586
3.09
Stage HHI>0.5
315
2.81
Stage HHI=<0.5
433
3.16
Industry HHI>0.5
324
2.90
Industry HHI=<0.5
424
3.11
Stage diversification index>0.5
278
3.12
Stage diversification index=<0.5
470
2.96
Industry diversification index>0.5
286
3.19
Industry diversification index=<0.5
462
2.91
P-value
0.02
0.08
0.02
0.16
0.30
0.06
Panel B: Means of exit rate variables across samples of long and short durations
Observation
Mean
P-value
IPO exit rate
Duration>3
304
0.147
Duration=<3
444
0.145
Duration>3
304
0.589
Duration=<3
444
0.567
0.88
Successful exit rate
0.15
31
Table 5. Correlation matrix for the employed variables. Variables are described in Table 1. Significance levels as follows: *p<0.10, ** p<0.05, **<0.01.
1
(1) Duration
(2) Stage specialization dummy
(3) Industry specialization dummy
(4) Stage HHI
(5) Industry HHI
(6) Stage diversification index
(7) Industry diversification index
(8) IPO exit rate
(9) Successful exit rate
(10) PE Firm experience
(11) Number of past IPOs
(12) Number of recent IPOS
(13) Investment selection speed
(14) Firm busyness
(15) Fund size
(16) PE capital supply
(21) Yearly returns on S&P500
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
1
-0.09**
1
-0.06**
0.33***
1
-0.08**
0.80***
0.46***
1
-0.05
0.42***
0.85***
0.62***
1
0.07**
-0.64*** -0.44***
-0.85***
-0.62***
1
0.04
-0.42*** -0.59***
-0.59***
-0.80***
0.76***
1
0.00
-0.03
-0.05
-0.03
-0.02
0.01
0.00
1
0.05
-0.01
-0.06
-0.04
-0.08**
0.05
0.04
0.48***
1
-0.24***
-0.23*** -0.23***
-0.28***
-0.34***
0.41***
0.53***
-0.02
-0.05
1
-0.16***
-0.24*** -0.17***
-0.25***
-0.26***
0.41***
0.50***
-0.04
-0.03
0.67***
1
-0.14***
-0.14*** -0.11***
-0.14***
-0.17***
0.24***
0.30***
-0.01
0.01
0.27***
0.39***
1
-0.34***
0.01
0.01
-0.03
0.02
0.03
-0.09***
-0.10***
0.07*
0.07**
0.06*
1
-0.04
-0.12*** -0.13***
-0.17***
-0.20***
0.24***
0.29***
0.11***
0.01
0.10***
0.08**
0.06
-0.04
1
-0.12***
0.07**
-0.03
0.07**
-0.05
0.01
0.06
0.04
0.05
0.15***
0.16***
0.12***
0.11***
-0.09***
1
-0.13***
-0.01
0.05
0.02
0.02
-0.01
0.00
-0.47***
-0.33***
0.21***
0.22***
0.08**
0.22***
-0.17***
0.26
1
-0.07***
0.01
0.01
0.04
0.02
-0.06
-0.06*
-0.04
0.07**
-0.11***
-0.04
-0.01
0.11***
0.01
0.03
0.12
0.04
32
1
Table 6. The effect of specialization on Duration
This table reports estimation results of the hazard models while controlling for specialization measures separately for each dimension. The dependent
variable is Duration measured as the number of years elapsed between launching current fund and the next fund by the same PE firm. First three columns
present results while controlling for the stage specialization and the last three columns, respectively, while controlling for the industry specialization. For
each dimension of specialization, we present three specifications. In the first two columns we control separately for PE firm experience and Number of past
IPOs (due to high correlation between the two), while in the third specification we jointly control for both of them. Panel A presents estimation results of the
hazard models while measuring specialization using Stage (Industry) specialization dummy constructed according to the actual number of investments in
each stage (industry). Panel B present results while employing Stage (Industry) HHI constructed according to Gompers et al. (2009). Panel C displays the
results employing diversification indexes constructed according to Knill (2009). The detailed description of all the employed variables is provided in Section
III and Table 1. Standard errors are in parenthesis. Significance levels are as follows: * p<0.10, ** p<0.05, *** p<0.01.
Panel A. Stage (Industry) specialization dummy
Dependent variable: Duration
(1)
(2)
(3)
Industry specialization dummy
-0.159***
(0.055)
-0.119**
(0.055)
-0.159***
(0.055)
Stage specialization dummy
-0.189***
(0.045)
-0.170***
(0.046)
-0.189***
(0.046)
PE firm experience
-0.057***
(0.009)
Number of recent IPOs
-0.140***
(0.045)
Number of past IPOs
-0.142***
(0.047)
(4)
-0.057***
(0.011)
-0.055***
(0.009)
-0.140***
(0.047)
-0.129***
(0.045)
-0.025***
(0.006)
-0.000
(0.008)
(5)
(6)
-0.057***
(0.011)
-0.135***
(0.048)
-0.132***
(0.047)
-0.023***
(0.006)
0.002
(0.008)
Portfolio selection speed
-0.679***
(0.070)
-0.691***
(0.072)
-0.679***
(0.070)
-0.662***
(0.071)
-0.684***
(0.072)
-0.663***
(0.071)
Firm busyness
-0.005
(0.004)
-0.007
(0.004)
-0.005
(0.004)
-0.003
(0.005)
-0.005
(0.004)
-0.003
(0.005)
PE capital supply
-0.020
(0.022)
-0.031
(0.022)
-0.020
(0.022)
-0.017
(0.022)
-0.030
(0.023)
-0.018
(0.022)
Yearly returns on S&P500
-0.003*
(0.002)
-0.002
(0.002)
-0.003*
(0.002)
-0.004**
(0.002)
-0.003
(0.002)
-0.004**
(0.002)
Constant
Observations
2.162***
(0.241)
748
2.098***
(0.248)
748
2.160***
(0.245)
748
2.076***
(0.243)
748
2.031***
(0.250)
748
2.086***
(0.247)
748
33
Table 6. (Continued) Panel B. Stage (Industry) HHI
Dependent variable: Duration
(1)
(2)
(3)
Industry specialization index
(4)
-0.327***
(0.087)
Stage specialization index
-0.368***
(0.093)
PE firm experience
-0.059***
(0.009)
Number of recent IPOs
-0.135***
(0.045)
Number of past IPOs
-0.290***
(0.092)
(5)
-0.223***
(0.086)
(6)
-0.327***
(0.087)
-0.368***
(0.093)
-0.060***
(0.011)
-0.060***
(0.009)
-0.138***
(0.047)
-0.136***
(0.047)
-0.129***
(0.045)
-0.025***
(0.006)
0.001
(0.008)
-0.062***
(0.011)
-0.135***
(0.047)
-0.132***
(0.047)
-0.024***
(0.006)
0.002
(0.008)
Portfolio selection speed
-0.666***
(0.070)
-0.683***
(0.072)
-0.666***
(0.070)
Firm busyness
-0.005
(0.004)
-0.007
(0.004)
-0.005
(0.004)
-0.005
(0.004)
-0.006
(0.004)
-0.005
(0.004)
PE capital supply
-0.018
(0.022)
-0.030
(0.022)
-0.018
(0.022)
-0.016
(0.022)
-0.030
(0.022)
-0.017
(0.022)
Yearly returns on S&P500
-0.003*
(0.002)
-0.002
(0.002)
-0.003*
(0.002)
-0.004**
(0.002)
-0.003
(0.002)
-0.004**
(0.002)
Constant
2.255***
(0.246)
2.162***
(0.252)
2.259***
(0.249)
2.183***
(0.244)
2.102***
(0.251)
2.195***
(0.248)
748
748
Observations
748
748
748
-0.664***
(0.070)
748
-0.687***
(0.072)
-0.665***
(0.070)
34
Table 6. (Continued) Panel C. Stage (Industry) Diversification
Dependent variable: Duration
(1)
(2)
(3)
Industry diversification index
Stage diversification index
(4)
0.588***
(0.098)
0.567***
(0.109)
PE firm experience
-0.067***
(0.009)
Number of recent IPOs
-0.154***
(0.045)
Number of past IPOs
0.456***
(0.108)
(5)
0.426***
(0.096)
(6)
0.599***
(0.099)
0.573***
(0.110)
-0.064***
(0.011)
-0.076***
(0.009)
-0.149***
(0.047)
-0.149***
(0.047)
-0.159***
(0.045)
-0.029***
(0.006)
-0.003
(0.008)
-0.072***
(0.011)
-0.148***
(0.047)
-0.151***
(0.046)
-0.033***
(0.007)
-0.006
(0.008)
Portfolio selection speed
-0.675***
(0.069)
-0.689***
(0.071)
-0.674***
(0.070)
-0.669***
(0.068)
-0.690***
(0.071)
-0.666***
(0.068)
Firm busyness
-0.007*
(0.004)
-0.009**
(0.004)
-0.007*
(0.004)
-0.011**
(0.004)
-0.011**
(0.004)
-0.011**
(0.004)
-0.013
(0.022)
-0.026
(0.022)
-0.012
(0.022)
-0.010
(0.022)
-0.025
(0.022)
-0.009
(0.022)
-0.003*
(0.002)
-0.002
(0.002)
-0.003*
(0.002)
-0.004**
(0.002)
-0.003
(0.002)
-0.004**
(0.002)
PE capital supply
Yearly returns on S&P500
Constant
Observations
1.764***
(0.246)
748
1.745***
(0.256)
748
1.743***
(0.251)
748
1.787***
(0.242)
748
1.776***
(0.253)
748
1.752***
(0.247)
748
35
Table 7. The effect of specialization on Duration
This table reports estimation results of hazard model while controlling jointly for specialization measures in both
dimension. The dependent variables is Duration measured as the number of years elapsed between launching
current and the next fund by the same PE firm. Column I presents estimation results while controlling for Stage
(Industry) specialization dummy constructed according the actual number of investments in each stage (industry).
Column II presents results while controlling for Stage (Industry) HHI constructed according to Gompers et al.
(2009). Column III displays the results employing diversification indexes constructed according Knill (2009).
The detailed description of all the employed variables is provided in Section III and Table 1. Standard errors are
in parenthesis. Significance levels are as follows: * p<0.10, ** p<0.05, *** p<0.01
Dependent variable: Duration
(1)
Stage specialization dummy
-0.165***
(0.048)
Industry specialization dummy
-0.093
(0.058)
(2)
Stage specialization index
-0.252**
(0.116)
Industry specialization index
-0.185*
(0.109)
(3)
Stage diversification index
0.233
(0.146)
Industry diversification index
0.456***
(0.134)
PE firm experience
-0.059***
(0.011)
-0.063***
(0.011)
-0.072***
(0.011)
Number of recent IPOs
-0.141***
(0.047)
-0.137***
(0.047)
-0.155***
(0.046)
Number of past IPOs
-0.000
(0.008)
0.001
(0.008)
-0.006
(0.008)
Portfolio selection speed
-0.663***
(0.071)
-0.658***
(0.070)
-0.665***
(0.068)
Firm Busyness
-0.005
(0.004)
-0.006
(0.004)
-0.011**
(0.004)
PE capital supply
-0.018
(0.022)
-0.016
(0.022)
-0.008
(0.022)
Yearly returns on S&P500
-0.003*
(0.002)
-0.003**
(0.002)
-0.004**
(0.002)
Constant
Observations
2.149***
(0.245)
748
2.264***
(0.250)
748
1.696***
(0.249)
748
36
Table 8. The effect of area-specific specialization on Duration
This table presents estimation results of competing risks models for asset-specific and industry-specific fundraising. The
dependent variable is Duration which ends by raising a specific type of fund. Columns 1-2 present estimation results for
asset-specific (VC fund versus BO fund) fundraising. Columns 3-4 displays results for industry-specific (High-tech fund
versus Non-high-tech fund) fundraising. The detailed description of all the employed variables is provided in Section III
and Table 1. Standard errors are in parenthesis. Significance levels are as follows: * p<0.10, ** p<0.05, *** p<0.01
Asset-specific fundraising
VC fund
BO fund
(1)
(2)
Specialized in VC
-0.325***
(0.051)
0.263
(0.186)
Specialized in BO
1.219***
(0.194)
-1.012***
(0.124)
Industry-specific fundraising
High-tech
fund
(3)
Non-high-tech
fund
(4)
Specialized in high-tech
-0.355***
(0.083)
0.303
(0.200)
Specialized in non-high-tech
0.786***
(0.222)
-0.910***
(0.106)
PE firm experience
-0.051***
(0.012)
-0.048*
(0.026)
-0.032*
(0.017)
-0.111*
(0.022)
Number of recent IPOs
-0.131***
(0.050)
-0.142
(0.110)
-0.145**
(0.062)
-0.185*
(0.106)
Number of past IPOs
-0.003
(0.009)
-0.010
(0.018)
-0.013
(0.011)
0.098***
(0.036)
Portfolio selection speed
-0.792***
(0.079)
-0.389***
(0.148)
-0.843***
(0.102)
-0.332**
(0.132)
Firm Busyness
-0.005
(0.005)
0.050*
(0.027)
-0.007
(0.006)
0.018
(0.014)
PE capital supply
0.007
(0.024)
-0.124**
(0.054)
-0.113***
(0.034)
-0.095**
(0.046)
Yearly returns on S&P500
-0.003
(0.002)
-0.005
(0.004)
-0.003
(0.003)
-0.004
(0.004)
Constant
2.026***
(0.260)
4.201***
(0.611)
3.501***
(0.379)
3.816***
(0.525)
Observations
748
748
748
748
37
Table 9. The impact of Duration on IPO (Successful) exit rate
This table presents estimation results of Tobit regressions for IPO exit rate and Successful exit rate. IPO exit rate is
measured as the number of IPOs m ade by fund as a fraction of its total investments. Successful exit rate is measured as
the number of IPOs and M&A exits made by fund as a fraction of its total investments. Panel A reports estimation results
of Tobit and Instrumental variable Tobit model for IPO exit rate (columns 1-2) and Successful exit rate (columns 3-4).
Panel B reports first stage results of the corresponding IV Tobit models where Number of recent IPOs and Portfolio
selection speed are employed as instruments of Duration. The detailed description of selection and control variables are
provided in Section III and Table 1. Standard errors are in parenthesis. Significance levels are as follows: * p<0.10, **
p<0.05, *** p<0.01.
Tobit
IPO exit rate
IV Tobit
(1)
(2)
Tobit
Successful exit rate
IV Tobit
(3)
(4)
Duration
-0.001
(0.004)
0.029**
(0.012)
0.001
(0.004)
0.022*
(0.013)
Stage specialization dummy
-0.013
(0.016)
0.005
(0.018)
-0.019
(0.017)
-0.006
(0.019)
Industry specialization dummy
-0.025
(0.018)
-0.011
(0.020)
-0.032
(0.020)
-0.022
(0.021)
Fund size
0.00003**
(0.000)
0.00006***
(0.000)
-0.000
(0.000)
-0.000
(0.000)
PE Firm experience
-0.003
(0.003)
0.003
(0.004)
-0.004
(0.003)
0.000
(0.004)
BO fund
-0.027
(0.022)
-0.053**
(0.025)
0.148***
(0.024)
0.130***
(0.027)
Firm busyness
0.003***
(0.001)
0.004***
(0.001)
0.001
(0.001)
0.001
(0.001)
Constant
0.131***
(0.022)
0.005
(0.052)
0.585***
(0.024)
0.496***
(0.056)
Panel B: First stage estimation results for IV Tobit models
Stage specialization dummy
-0.584***
(0.147)
-0.584***
(0.147)
Industry specialization dummy
-0.390**
(0.172)
-0.390**
(0.172)
Fund size
-0.00004**
(0.000)
-0.00004**
(0.000)
PE Firm experience
-0.168***
(0.027)
-0.168***
(0.027)
BO fund
0.601***
(0.203)
0.601***
(0.203)
Firm busyness
-0.017
(0.012)
-0.017
(0.012)
Number of recent IPOs
-0.280**
(0.140)
-0.278*
(0.145)
Portfolio selection speed
-1.912***
(0.208)
-1.913***
(0.208)
Constant
5.178***
(0.187)
5.178***
(0.187)
Observations
Wald Chi2(1)
748
748
7.23***
748
748
3.07*
38
Table 10. The impact of Duration on Number of IPOs (Successful exits) done by a fund
This table presents estimation results of Tobit regressions for alternative measures of fund success. In the first two
columns, dependent variable is the realized number of IPOs by fund and in the last two columns, respectively, realized
number of successful exits. Panel A reports estimation results of Tobit and instrumental variable Tobit models. Panel B
reports first stage results of the corresponding IV Tobit models where Number of recent IPOs and Portfolio selection
speed are employed as instruments of Duration. The detailed description of selection and control variables are provided in
Section III and Table 1. Standard errors are in parenthesis. Significance levels are as follows: * p<0.10, ** p<0.05, ***
p<0.01.
Number of IPOs
Tobit
IV Tobit
(1)
Duration
-0.048
(0.098)
(2)
1.219***
(0.339)
Number of successful exits
Tobit
IV Tobit
(3)
(4)
-0.218
(0.165)
1.124**
(0.551)
Stage specialization dummy
-0.279
(0.414)
0.464
(0.496)
-1.078
(0.703)
-0.295
(0.794)
Industry specialization dummy
-1.169**
(0.491)
-0.581
(0.561)
-1.999**
(0.821)
-1.371
(0.891)
Fund size
0.002***
(0.000)
0.003***
(0.001)
0.004***
(0.001)
0.005***
(0.001)
PE Firm experience
-0.085
(0.076)
0.157
(0.104)
-0.147
(0.129)
0.109
(0.168)
BO fund
-2.668***
(0.591)
-3.738***
(0.707)
-6.292***
(0.971)
-7.439***
(1.107)
Firm busyness
0.038
(0.033)
0.055
(0.037)
0.025
(0.057)
0.043
(0.060)
Constant
2.993***
(0.587)
-2.291
(1.494)
13.184***
(0.996)
7.582***
(2.422)
Panel B: First stage estimation results for IV Tobit models
Stage specialization dummy
-0.575***
(0.147)
-0.577***
(0.147)
Industry specialization dummy
-0.386**
(0.172)
-0.386**
(0.172)
Fund size
-0.0004**
(0.000)
-0.0004**
(0.000)
PE Firm experience
-0.172***
(0.027)
-0.172***
(0.027)
BO fund
0.600***
(0.203)
0.600***
(0.203)
Firm busyness
-0.017
(0.012)
-0.017
(0.012)
Number of recent IPOs
-0.167
(0.134)
-0.185
(0.148)
Portfolio selection speed
-1.936***
(0.207)
-1.934***
(0.207)
Constant
5.192***
(0.187)
5.191***
(0.187)
Observations
Wald Chi2(1)
748
748
15.84***
748
748
6.65***
39