1. © Cumming & Johan (2013) Fund Size Cumming and Johan (2013 Chapter 18) 1

2. © Cumming & Johan (2013) Fund Size A Growing Problem: the Problem of Growing! Introduction Hypotheses Data Empirical Tests Conclusions “We all had too much money. It was just too easy… The problem…was that the funds had grown so big that the 2 percent became just as important as the 20 percent… Success had less to do with performance or risk management…and more to do with bulking up.”— A Confession by a Private Equity Manager, The New York Times (September 22, 2009) 2

3. © Cumming & Johan (2013) Fund Size Chapter Objectives Introduction Hypotheses Data Empirical Tests Conclusions Consider whether fund size affects: Valuation Performance 3

4. © Cumming & Johan (2013) Fund Size Factors at Play with Respect to Fund Size and Valuation Introduction Hypotheses Data Empirical Tests Conclusions We project that the most reputable VCs are likely to pay a lower price, ceteris paribus. Larger VC funds are likely to pay a lower price as they have greater outside option. On the other hand, when funds become big, agency problem may kick in, which predicts a convex (U-shape) relationship between fund size and venture pre-money valuation. Further, if human capital does not keep up with the fund growth, the resulted diluted attention could reduce either funds’ outside option or continuation option, thus leads to a higher or lower pre-money valuation. 4

5. © Cumming & Johan (2013) Fund Size Hypotheses Introduction Hypotheses Data Empirical Tests Conclusions Hypothesis 18.1: The most reputable VCs offer lower pre-money valuation given the quality of ventures equal. Hypothesis 18.2: VC fund size is negatively correlated with venture valuation controlling for VC reputation and the quality of ventures. Hypothesis 18.3: There is a convex relation between fund size and venture valuation. Hypothesis 18.4a: Limited attention is negatively correlated with pre-money valuation if its impact on VCs’ continuation payoff is dominant. Hypothesis 18.4b: Limited attention is positively correlated with pre-money valuation if its impact on VCs’ outside option is dominant. 5

6. © Cumming & Johan (2013) Fund Size Factors at Play with Respect to Fund Size and Performance Introduction Hypotheses Data Empirical Tests Conclusions Diseconomies of scale in the VC industry due to agency problems and limited attention. Agency problems with larger funds – If some fund managers (“bad” managers) seek large fund commitments to pursue their pecuniary (for instance, greater fixed fee) and engage in inefficient investment behavior (for example, they may pursue larger but not necessarily highest NPV investments; they may pay more (offer higher valuation) to their portfolio companies; etc.), we expect a concave relation between fund size and portfolio companies’ exit performance. Limited attention with larger funds (see also Chapter 17) 6

7. © Cumming & Johan (2013) Fund Size Hypotheses Introduction Hypotheses Data Empirical Tests Conclusions Hypothesis 18.5: There is a concave relation between fund size and portfolio companies’ exit performance (e.g., probability of successful exits). Hypothesis 18.6: VCs’ limited attention has a negative impact on the portfolio companies’ ultimate performance (e.g., probability of successful exits). 7

8. © Cumming & Johan (2013) Fund Size Data Introduction Hypotheses Data Empirical Tests Conclusions VentureXpert database provided by Thomason Financial Corporation. 1991 – 2006 (Table 18.1) 27,754 rounds of VC investments. Among the 27,754 rounds of VC investments, 9,266 observations (34.4% of the sample) have the post-money valuation data Limited partnerships. – We do not include other types of VC investors, such as corporate VC, financial institution affiliated VC funds, pension funds and university foundations, etc. The purpose is to avoid potential impact of organizational forms of VC investors on valuations. 8

9. © Cumming & Johan (2013) Fund Size YearNumber of financing roundsRounds with valuation data Percentage with valuation data (%) 19911,136786.9% 19921,30226520.4% 19931,05823021.7% 19941,11131228.1% 19951,01434333.8% 19961,32842932.3% 19971,49753535.7% 19981,58175547.8% 19992,4571,29652.7% 20003,5711,96655.1% 20012,3141,13749.1% 20021,72661535.6% 20031,68449429.3% 20041,80035119.5% 20051,98725012.6% 20062,1882109.6% All Years27,7549,26633.4% Table 18.1 Number of Observations, by Year 9

10. © Cumming & Johan (2013) Fund Size Pre-Money Valuation Summary Statistics Introduction Hypotheses Data Empirical Tests Conclusions Table 18.2 provides pre-money valuations – Valuations increase when the ventures are at their later stages of development; ventures located in CA, MA, TX, and NY Pre pre-money valuation increases with the size of financing round. – The mean (median) pre-money valuation of the largest quartile of financing rounds is 103.8 (60.6) million, in 2006 dollars. In comparison, the mean (median) valuation of the smallest quartile of financing rounds is 18.8 (7.8) million, in 2006 dollars. Pre-money valuation of ventures increases with fund size. – The pre-money valuation of ventures invested by VC funds in the largest quartile has a mean (median) pre-money valuation of $74.1 (34.2) million, while the mean (median) pre-money valuation of ventures invested by VC funds in the smallest quartile is $30.1 (12.8) million. 10

11. © Cumming & Johan (2013) Fund Size Pre-money valuation ($M) MeanMedianN Round Size ($M) Highest Quartile103.860.62316 Second Quartile36.122.22317 Third Quartile23.913.12315 Lowest Quartile18.87.82318 Industry Computer Related42.218.74032 Communication68.425.91771 Medical/Health/Life Science30.818.21395 Biotechnology40.019.2790 Semiconductor46.920.5623 Non-Tech42.317.7655 Stage Startup/Seed11.26.01013 Early Stage20.210.72549 Expansion55.429.43843 Later Stage75.040.81502 Other102.838.5359 Location CA47.121.13649 MA47.720.21091 TX59.120.4500 NY57.224.1305 Elsewhere40.318.03421 VC Fund Size ($M) Highest Quartile74.134.22311 Second Quartile45.422.72293 Third Quartile33.117.12344 Lowest Quartile30.112.82318 Table 18.2 Pre-money Valuations of Financing Rounds 11

12. © Cumming & Johan (2013) Fund Size Measuring Limited Attention Introduction Hypotheses Data Empirical Tests Conclusions To measure the performance-adjusted amount of excess capital managed per partner, we first group venture capital firms into quartiles based on their previous IPO market share. Then we compare the amount of capital managed per partner of a specific venture capital firm to the median measures of their peers with similar past performance (same quartile based on their previous IPO market share). The differences are our final measures of limited attention 12

13. © Cumming & Johan (2013) Fund Size Regression of Pre-Money Valuation (1 of 3) Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance All specifications in Table 18.3 show that the relationship between venture valuation and VC past IPO market share is positive, albeit concave. – This finding suggests that more reputable VCs (of better past performance) are often matched with higher quality ventures which typically are of higher value, nevertheless, the most reputable ones, on the other side, offer lower price holding the quality of ventures constant, supporting Hypothesis 18.1. – This finding is consistent with the notion proposed in Hsu (2004) that entrepreneurs are willing to accept less favorable financial terms to be affiliated with more reputable VCs. 13

14. © Cumming & Johan (2013) Fund Size Regression of Pre-Money Valuation (2 of 3) Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance We find a convex relationship between private firm valuation and VC fund size. – The negative correlation between fund size and firm pre-money valuation indicates that larger VCs, in general, have greater negotiation power and thus pay lower price holding the quality of ventures constant, consistent with Hypothesis 18.2. – However, this relationship is reversed when the fund gets very large, as suggested by the positive correlation between fund size square term and valuation, consistent with Hypothesis 18.3 14

15. © Cumming & Johan (2013) Fund Size Regression of Pre-Money Valuation (3 of 3) Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance In models (2) and (3), we link limited attention to firm valuation. We find a significantly positive association between firm valuation and both measures of limited attention. For instance, a coefficient of 0.171 (p-value=0.003) on excess fund managed per partner, or Ln(Fund Size/N of Partners) adjusted by the median of the same measure of VCs with similar past performance, indicates that one standard deviation increase in excess capital managed per partner increases the valuation of the firm by 4.2% assuming other aspects similar. This set of findings are consistent with the prediction of Hypothesis 18.4b, implying that diluted attention when fund grows very large could reduce VCs’ outside option and thus decreases their bargaining power, which significantly increases the price of the investments. 15

16. © Cumming & Johan (2013) Fund Size DV: Ln (Pre-money Valuation) (1)(2)(3)(4)(5) Intercept2.391***2.076***2.033***2.398***2.296*** (0.000) Variables of Key Interest Ln(Fund Size)-0.122***-0.121**-0.096* (0.003)(0.035)(0.097) Ln(Fund Size) Square Term0.015***0.0090.007 (0.000)(0.121)(0.262) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)0.171***0.176** (0.003)(0.013) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted0.027***0.028*** (0.001)(0.004) VC IPO Share0.089***0.101*** 0.113*** (0.000) VC IPO Share Square Term-0.005**-0.006** -0.007*** (0.014)(0.010)(0.011)(0.004) Characteristics of Firms Stage of Firm Seed/Start-up Stage-1.432***-1.466***-1.473***-1.462***-1.469*** (0.000) Early Stage-1.093***-1.120***-1.114-1.115***-1.109*** (0.000) Expansion Stage-0.338***-0.383***-0.380***-0.384***-0.380*** (0.000) Industry of Firm DummiesYes Location of Firm DummiesYes Other Control VariablesYes Year Fixed EffectYes N92666572 Adjusted R 2 (%)47.5248.1548.3248.2248.38 Table 18.3 Regression Analysis: The Effect of Fund Size and Limited Attention on Valuation 16

17. © Cumming & Johan (2013) Fund Size Robustness Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance Alternative specifications Random effects Heckman selection 17

18. © Cumming & Johan (2013) Fund Size Panel A: Company Random Effect Model Variables of Key Interest Ln(Fund Size)-0.104***-0.102*-0.079 (0.008)(0.062)(0.160) Ln(Fund Size) Square Term0.013***0.0080.005 (0.001)(0.172)(0.345) Fund Size/N of Partners: Comparable VCs Median Adjusted0131**0.135** (0.013)(0.43) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.021***0.023** (0.005)(0.020) VC IPO Share0.095***0.108***0.106***0.117***0.116*** (0.000) VC IPO Share Square Term-0.007**-0.007*** -0.008*** (0.002)(0.001) (0.000)(0.001) LR Test Chi25181.294245.804209.224251.944213.83 Prob>Chi20.000 Table 18.4 Alternative Models: The Effect of Fund Size and Limited Attention on Valuation 18

19. © Cumming & Johan (2013) Fund Size Panel B: Heckman Selection Model Variables of Key Interest Ln(Fund Size)-0.105***-0.114**-0.090 (0.005)(0.034)(0.103) Ln(Fund Size) Square Term0.012***0.0080.005 (0.002)(0.159)(0.343) Fund Size/N of Partners: Comparable VCs Median Adjusted0.123**0.143** (0.012)(0.025) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.020***0.023** (0.005)(0.011) VC IPO Share0.097***0.102*** 0.117***0.118*** (0.000) VC IPO Share Square Term-0.006*** -0.007*** (0.003) (0.001) Inverse Mills Ratio1.019***0.751***0.768***0.758***0.778*** (0.000) Wald Chi24700.224356.484329.074360.584327.35 Prob>Chi20.000 19

20. © Cumming & Johan (2013) Fund Size Panel C. Heckman-Correct Company Random Effect Model Variables of Key Interest Ln(Fund Size)-0.094**-0.099*-0.076 (0.016)(0.069)(0.172) Ln(Fund Size) Square Term0.011***0.0070.005 (0.004)(0.206)(0.401) Fund Size/N of Partners: Comparable VCs Median Adjusted0.091*0.103 (0.087)(0.125) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.016**0.018* (0.040)(0.059) VC IPO Share0.100***0.108***0.107***0.119***0.1119*** (0.000) VC IPO Share Square Term-0.007*** -0.008*** (0.001) (0.000) Inverse Mills Ratio2.489***1.995***1.966***2.018***2.001*** (0.000) LR Test Chi25229.254270.454233.064277.164238.43 Prob>Chi20.000 20

21. © Cumming & Johan (2013) Fund Size Robustness Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance Similar performance quartiles 21

22. © Cumming & Johan (2013) Fund Size Quartile 1 (Most Reputable VCs) Variables of Key Interest Ln(Fund Size)-0.0240.1170.113 (0.865)(0.587)(0.600) Ln(Fund Size) Square Term0.010-0.002 (0.422)(0.911)(0.930) Fund Size/N of Partners: Comparable VCs Median Adjusted0.165*0.047 (0.065)(0.697) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.023*0.006 (0.085)(0.739) N21882006 Adjusted R231.5633.5933.5733.9033.89 Table 18.5 The Effect of Fund Size and Limited Attention on Valuation among VCs with Similar Performance 22

23. © Cumming & Johan (2013) Fund Size Quartile 2 Variables of Key Interest Ln(Fund Size)-0.070-0.231-0.207 (0.624)(0.140)(0.217) Ln(Fund Size) Square Term0.023*0.033**0.030* (0.068)(0.035)(0.079) Fund Size/N of Partners: Comparable VCs Median Adjusted0.108-0.193* (0.127)(0.094) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.019**-0.018 (0.046)(0.278) N19701617 Adjusted R236.8831.9131.9632.4432.38 23

24. © Cumming & Johan (2013) Fund Size Quartile 3 Variables of Key Interest Ln(Fund Size)-0.389***-0.503***-0.506*** (0.008)(0.001) Ln(Fund Size) Square Term0.061***0.072*** (0.000) Fund Size/N of Partners: Comparable VCs Median Adjusted1.220**-0.137 (0.018)(0.818) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.258*-0.058 (0.058)(0.698) N19481529 Adjusted R231.5932.6432.5433.7833.79 24

25. © Cumming & Johan (2013) Fund Size Quartile 4 (Least Reputable VCs) Variables of Key Interest Ln(Fund Size)-0.130*-0.251**-0.254** (0.071)(0.018)(0.017) Ln(Fund Size) Square Term0.037***0.044***0.047*** (0.000)(0.001)(0.000) Fund Size/N of Partners: Comparable VCs Median Adjusted3.528***1.808** (0.000)(0.013) Fund Size/(N of Partners/N of Parallel Funds): Comparable VCs Median Adjusted0.940***0.290 (0.000)(0.237) N23331574 Adjusted R227.9628.9628.4429.3329.74 25

26. © Cumming & Johan (2013) Fund Size Fund Size and Performance Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance We find that fund size is positively associated with the probability of successful exit, while its square term is negatively associated with the probability of successful exit. This supports Hypothesis 18.5. Both coefficients, nevertheless, are only marginally significant (at the 15% and 10% confidence levels, respectively). We don’t find that limited attention is significantly associated with the probability of successful exits, thus rejecting Hypothesis 18.6. Also noteworthy: More reputable VCs (higher IPO share) positively contribute to the success of portfolio companies. Technology companies overall and companies located in California and Massachusetts are more likely to exit through IPOs or M&As. 26

27. © Cumming & Johan (2013) Fund Size DV: Probability of Successful Exit (1)(2)(3)(4)(5) Intercept-1.104***-0.984***-0.978***-1.082***-1.054*** (0.000) Variables of Key Interest Ln(Fund Size)0.0860.0780.063 (0.124)(0.336)(0.442) Ln(Fund Size) Square Term-0.010*-0.011-0.009 (0.069)(0.187)(0.285) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)-0.0240.096 (0.759)(0.350) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted-0.0070.007 (0.551)(0.629) VC IPO Share0.029***0.018** 0.028***0.027** (0.002)(0.036)(0.035)(0.008)(0.011) Characteristics of Firms Stage of Firm Seed/Start-up Stage-0.123**-0.154**-0.153**-0.146**-0.147** (0.041)(0.022) (0.030)(0.029) Early Stage-0.129***-0.179***-0.178***-0.173*** (0.007)(0.001) Expansion Stage-0.019-0.054-0.053-0.052 (0.667)(0.271)(0.274)(0.286)(0.288) Industry of Firm DummiesYes Location of Firm DummiesYes Other Control VariablesYes Year Fixed EffectYes N70705608 Pseudo R 2 (%)2.392.68 2.722.71 Table 18.6 Fund Size, Limited Attention, and Venture Performance 27

28. © Cumming & Johan (2013) Fund Size Robustness: Fund Size and Performance Introduction Hypotheses Data Empirical Tests Conclusions 1. Valuation 2. Exit Performance In Table 18.7, we group our sample into quartiles based on the pre-money valuation and repeat the regressions as shown in Table 18.6 for each quartile. We show that the influence of fund size on the probability of successful exits is particularly strong among the group with relatively high pre-money valuation (quartiles 1 and 2). Together with our findings in Section 18.4, this finding suggests that one of the mechanisms for the diseconomy of size documented in the literature is the overpricing of entrepreneurial firms by large VC funds. We provide empirical evidence, for the first time, that such agency problem exists in the venture capital investments and it has a negative impact on the performance of portfolio companies. Similar to Table 18.6, we do not find limited attention has a significant association with venture performance 28

29. © Cumming & Johan (2013) Fund Size DV: Probability of Successful Exits Quartile 1 (Highest Valuation) Ln(Fund Size)0.195*0.1830.176 (0.098)(0.289)(0.311) Ln(Fund Size) Square Term-0.023**-0.026-0.025 (0.038)(0.123)(0.141) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)-0.0570.243 (0.636)(0.170) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted-0.0070.029 (0.649)(0.207) N18131428 Adjusted R 2 2.913.74 4.724.02 Quartile 2 Ln(Fund Size)0.259**0.244*0.229 (0.026)(0.097)(0.122) Ln(Fund Size) Square Term-0.026**-0.023-0.021 (0.023)(0.132)(0.171) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)-0.113-0.011 (0.459)(0.956) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted-0.021-0.008 (0.340)(0.765) N17771427 Pseudo R 2 3.664.124.144.27 Table 18.7 The Relation between Fund Size, Limited Attention and Exit Performance by Pre-Money Valuation Quartiles 29

30. © Cumming & Johan (2013) Fund Size Quartile 3 Ln(Fund Size)-0.0100.0240.030 (0.927)(0.887)(0.859) Ln(Fund Size) Square Term0.002-0.003-0.004 (0.866)(0.873)(0.842) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)0.0260.046 (0.891)(0.840) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted0.0060.010 (0.836)(0.776) N17571399 Adjusted R 2 2.563.12 Quartile 4 (Lowest Valuation) Ln(Fund Size)-0.166-0.276-0.342* (0.163)(0.137)(0.067) Ln(Fund Size) Square Term0.0180.0270.036* (0.186)(0.183)(0.076) Ln(Fund Size/N of Partners): Comparable VCs Median Adjusted)0.2230.134 (0.398)(0.665) Ln[Fund Size/(N of Partners/N of Parallel Funds)]: Comparable VCs Median Adjusted-0.004-0.037 (0.942)(0.510) N17231354 Pseudo R 2 4.254.584.544.724.74 30

31. © Cumming & Johan (2013) Fund Size Conclusions (1 of 3) Introduction Hypotheses Data Empirical Tests Conclusions Valuations: The most reputable VCs pay lower price, ceteris paribus. We find a convex relationship between fund size and firm valuation and a significantly positive correlation between limited attention and valuation. – However, the positive correlation between fund size square term and valuation disappears when we control for limited attention with the exception of the group of less reputable VCs. These findings suggest that, in general, fund size is positively correlated with negotiation power and thus reduces pre-money valuation. However, human capital is overstretched when funds grow larger and the diluted attention reduces VCs’ outside option, which weakens their negotiation power and thus increases pre-money valuation. At the same time, the agency problem may also kick in, especially for the less reputable VCs who presumably have weaker inside governance mechanisms. 31

32. © Cumming & Johan (2013) Fund Size Conclusions (2 of 3) Introduction Hypotheses Data Empirical Tests Conclusions Performance We find a concave relationship between fund size and the probability of successful exits. We further show that the negative correlation between fund size square term and the probability of successful exits are particularly strong and significant when the pre-money valuation is high. Our findings show that the price of private equity investments are not just determined by the quality of the ventures or entrepreneurs, or the supply and demand of capital, but also influenced by various characteristics of the VCs investing in the ventures. – There is a tradeoff between being affiliated with the most reputable VCs and the valuation that ventures can get. – Large VC funds may provide entrepreneurs with larger investment and higher prices, however, as a trade-off, the probability of successful exits is lower. 32

33. © Cumming & Johan (2013) Fund Size Conclusions (3 of 3) Introduction Hypotheses Data Empirical Tests Conclusions Our findings also suggest that there is scale diseconomy in the venture capital industry, Further, we show that the scale diseconomy is at least partially due to the constraints from human capital. VCs often do not increase human capital in proportion to the growth in fund size, which reduces VCs’ outside options. Their bargaining power is thus reduced and they pay a higher price for investments of similar quality. 33