Main Analysis

to establish a track record/reputation in the industry and will therefore be less focussed and put in less effort, which leads to lower returns.

Regressions 5 and 6 (without fixed effects) are included to assess whether the variation in performance comes from fixed unit characteristics. From these two columns a number of things can be concluded. Firstly, the adjusted R-squared is a lot lower compared to those of the first four models. Secondly, the conclusions regarding the variables of interest remain the same: size has a negative impact on performance and sequence does not have a significant influence. In addition, the magnitude of the coefficients differ relatively little from that of the variables in the other models. This all indicates that fixed unit characteristics do not account for much of the variation in the dependent variable and it strengthens the reliability of the results.

Table 6: The Effect of Fund Characteristics on Performance

The dependent variable in all six models is net IRR: the internal rate of return, net of fees. Fund size is the amount in millions of USD that is committed to the fund. Sequence represents the sequence number of the fund. Columns 1 and 2 include only year fixed effects. Columns 3 and 4 include both firm fixed effects and year fixed effects. Columns 5 and 6 do not include any type of fixed effects.

(1) (2) (3) (4) (5) (6)

Net IRR Net IRR Net IRR Net IRR Net IRR Net IRR

Log(Size) -1.259*** -7.690*** -4.344*** -11.688*** -1.556*** -7.810***

(0.274) (1.735) (0.684) (3.600) (0.299) (1.781)

Log(Size)² 0.517*** 0.571* 0.503***

(0.129) (0.288) (0.134)

Log(Sequence) 0.563 0.013 -2.989 -2.908 0.406 -0.601

(0.532) (0.887) (2.370) (2.547) (0.503) (0.833)

Log(Sequence)² 0.126 0.226 0.300

(0.238) (0.590) (0.237)

_cons 23.230*** 42.704*** 48.072*** 70.107*** 25.389*** 44.498***

(1.576) (5.549) (4.492) (10.410) (1.850) (5.786)

Firm fixed effects No No Yes Yes No No

Year fixed effects Yes Yes Yes Yes No No

R-squared 0.115 0.122 0.453 0.457 0.016 0.023

Adjusted R-squared 0.098 0.103 0.231 0.235 0.015 0.021

Obs. 1694 1694 1475 1475 1720 1720

Standard errors are in parenthesis and they are corrected for heteroskedasticity and clustered by year.

*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.

level. When the change in net IRR is used as a dependent variable, the coefficients on the number of deals increase. The result from regression 3 can be interpreted in the following way: an increase in the number of deals by 1 leads to an increase in the change of net IRR between subsequent funds of 0.113 percentage points. Also this result is significant at a 1%

level. These results are evidence against hypothesis 4 that the relation between the number of deals and fund performance is negative and instead support the idea that the number of deals of a buyout fund is positively associated with fund performance. Past research gives possible explanations for this result. First of all, Cumming and Dai (2011) showed with their research that the probability of a successful exit is higher in case of small investments rather than big ones. It appears that deals of smaller sizes are easier to integrate into an already existing portfolio. So by doing more small deals rather than a few big ones, the chances of a

successful exit increase which is beneficial for a fund’s performance. The positive effect of increasing the number of deals on performance may also be due to the benefits of

diversification that come along with it. According to Gejadze et al. (2017), by doing a lot of deals, funds can diversify and thereby reduce unsystematic risk. Humphery-Jenner (2012) also state that when funds diversify internationally, buyout funds can optimise their

investment portfolio by choosing to invest in the environment that is most favourable at that time. A broader universe of investment opportunities can in that way optimise the fund’s portfolio and as a consequence also their performance. Another explanation is that in case of a high number of deals, funds have repeated interactions with important parties like banks.

This leads to better relationships, less information asymmetry and more favourable loan terms, which all positively influences performance (Ivashina & Kovner, 2011). Lastly, Balboa and Martí (2007) argue that each additional investment leads to a better network and more experience and it improves the fund’s reputation by showing that it is able to

successfully negotiate and add new investments.

The coefficients on “Investment per Deal” is positive in all four regressions but only significant in regression 4. This indicates that contrary to what I expected, increasing the investment per deal does not have a significant impact on fund performance. The signs of the coefficients are however in line with my expectation based on prior literature. Lopez-de-Silanes et al. (2015) for example argued that doing more concentrated investments should lead to an increase in performance. Possible reasons are that more concentrated bets lead to an increased network and specialised knowledge (Norton & Tenenbaum, 1993), more efficiently selecting high growth target companies (Gejadze et al., 2017), having access to more skilled managers and better monitoring of the target companies (Cressy et al., 2007)

and being able to respond to market signals easier and faster (Gompers et al., 2008). Lastly Lopez-de-Silanes (2015) concluded that the probability of a successful exit also increases with deal size. All of the above mentioned reasons have a positive influence on performance.

When looking at the control variable “Log(Size)” we can again conclude that fund size has a negative impact on fund performance. The coefficients on “Log(Sequence)” are not significant. These conclusions regarding fund size and sequence number were also apparent from Table 6. The coefficient on lagged performance (“Net IRRt-1”) is positive in regressions 1 and 2 but becomes negative in regressions 3 and 4 when the dependent variable is change in net IRR rather than net IRR. It is important to note that the negative coefficients do not necessarily mean that the effect of lagged performance on fund performance is negative. It implies that an increase in performance of the previous fund is associated with a decrease in change in net IRR but the change in net IRR might still be positive. The coefficients on the industry specialist dummy variable are positive across all four regressions but only

significant at a 10% level in regressions 2 and 3. The industry specialist coefficient in model 3 can be interpreted as follows: when the buyout fund was considered specialised in the industry of the buyout, it is associated with an increase in net IRR of 0.241 percentage points.

The specialised knowledge that the industry specialists have can enable them to pick the best investment opportunities and it can help them to more effectively advise and monitor their investments, which positively influences performance (Cressy et al., 2017). The same regressions from Table 7 are also performed by including dummy variables for each type of industry, to control for the industry focus of the fund. Including industry dummy variables did not change the results so the results of those regressions are not displayed here but can be found in Appendix A.

Table 7: The Effect of Number of Deals and Deal Size on Fund Performance

The dependent variable in regressions 1 and 2 is net IRR. The dependent variable in regressions 3 and 4 is the change in net IRR between subsequent funds. Number of deals is the average number of deals of a fund. Investment per deal is the average investment per deal of a fund. Size is the amount in millions of USD that is committed to the fund. Net IRRt-1 is the performance of the previous fund. Sequence represents the sequence number of the fund. Industry Specialist is a dummy variable equal to 1 when the fund is considered specialised in the industry of the buyout and 0 otherwise. Columns 1 and 3 include only year fixed effects. Columns 2 and 4 include both firm fixed effects and year fixed effects.

(1) (2) (3) (4)

Net IRR Net IRR  Net IRR  Net IRR

Number of Deals 0.072*** 0.050 0.113*** 0.094*

(0.024) (0.038) (0.033) (0.050)

Investment per Deal 0.005 0.011 0.006 0.018**

(0.006) (0.009) (0.006) (0.009)

Log(Size) -1.450** -3.778*** -1.748*** -4.733***

(0.560) (0.659) (0.627) (0.958)

Net IRRt-1 0.176*** 0.041 -0.651*** -0.763***

(0.032) (0.040) (0.075) (0.095)

Log(Sequence) 0.318 -1.503 -0.701 0.765

(0.633) (3.117) (0.795) (4.081)

Industry Specialist 0.197 0.241* 0.646* 0.365

(0.258) (0.141) (0.373) (0.219)

_cons 19.516*** 42.279*** 17.605*** 38.981***

(2.903) (6.701) (4.183) (8.652)

Firm fixed effects No Yes No Yes

Year fixed effects Yes Yes Yes Yes

R-squared 0.152 0.500 0.308 0.573

Adjusted R-squared 0.150 0.491 0.307 0.566

Obs. 26103 26098 24753 24750

Standard errors are in parenthesis and they are corrected for heteroskedasticity and clustered by year.

*** Significant at the 1 percent level.

** Significant at the 5 percent level.

* Significant at the 10 percent level.