Post on 20-Dec-2015
The Impact of Venture Capital Investments on Industry
Performance
Tim Loughran
Sophie Shive
University of Notre Dame
Venture Capital – Background
• From 1980-2005, VC funding totaled $394.2 billion.
• 500 VC funds concentrated in CA, MA and a few other states.
• Raise funds from wealthy individuals, pension funds and endowments and invest it in new companies.
• Began just after WW II, but took off in 1979 when “prudent man” rule allowed pensions to invest.
Venture Capital - Background
• VCs provide both cash and expertise to young firms, and then plan their exit
• 20-35% of VC-funded firms are taken public – bulk of VC’s returns are here (Gompers and Lerner, 2002).
• Brau, Francis, and Kohers (2003) find that IPOs get a 22% premium over the average price paid for privately-held firms.
• Apple Computer, Sun Microsystems, Yahoo, eBay, and Google were all VC funded firms.
Research: Venture Capital
• Some evidence that VCs are smart: – When public market prices are high, VCs take
more firms public (Lerner, 1994)– VC funded IPOs have higher subsequent
returns than non-VC funded IPOs (Brav and Gompers, 1997)
– The most experienced ones make the most money (Gompers, Kovner, Lerner and Scharfstein, 2007).
Research: Industry Returns
• Less competitive industries earn lower returns (Hou and Robinson, 2006)
• The market reacts slowly to information contained in some industries (Hong, Torous and Valkanov, 2007)
Question
• What is the effect on public companies of VC funding in their industry? We explore the relation between public market returns and VC funding activity
• 3 possibilities: 1. It decreases the value of existing companies due to
competition
2. It does nothing
3. It encourages other investors, increasing the value of existing public companies
Summary of Findings
• In an panel of 3,502 quarterly industry observations, more VC funding is related to lower subsequent industry returns
• VC funding is negatively related to industry ROA in the subsequent year.
• Our results are generally significant for both the EW and VW returns.
• Also true for both the bubble and non-bubble periods.
Merck Example
• December 1999, Merck employed 62,300 workers and had mkt value of $157 billion.
• Yet, Merck was dependent on only a few successful drugs related to elevated cholesterol (Zocor and Mevacor) or hypertension/heart failure (Vasotec).
• A young bio-tech needs only a single successful product to hurt the giant Merck.
Yahoo/Google Example
• In our dataset, Google received two rounds of VC money: June 4, 1999 “other early stage” infusion of $25 million and September 1, 2000 “expansion” infusion of $15.175 million.
• Yahoo was more established than Google, and it also had older technology.
Market Values
• Yahoo had mkt value of $67 billion when Google received “expansion” money.
• In August of 2004 (Google’s IPO date), Yahoo had $42 billion value.
• By September 2007, Yahoo had $30 billion market value (Google is now $164 billion).
Outline of talk
• Data
• Methodology
• Main results
• Robustness
• Industry Return on Assets
• Conclusion
Data
• Venture capital (VC) data is from Thomson’s VentureXpert database: – Data is self-reported by the VCs or the
companies they invest in– all VC disbursements between 1980 and
2005. – Re-code industries into Fama-French 48: 34
industries have at least one round of funding.
• Other sources: Ken French’s website, CRSP, Compustat
Fig 1: Quarterly level of Nasdaq and VC disbursements
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Nasdaq VC Funding
Fig 2: Quarterly level of Nasdaq and VC disbursements scaled by assets
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Nasdaq VC Funding/Total Assets
Types of VC Funding
• Early Stage– Seed (1.9% of total funding)– Startup (17.2%)– Other Early Stage (6.9%)
• Expansion (57.3%)
• Later Stage (16.8%)
• We exclude Other Stage (Acquisitions, Special Situation, VC Partnership)
Data
• Over 83% of total VC funding goes to five industries: – Business Services: 37.3%– Telecommunications: 24.4%– Pharmaceutical Products: 9.2%– Computers: 6.6%– Chips and Electronic Equipment: 5.7%
Fig. 3. VC Disbursements for the Five Most-Funded Industries, 1980-20050
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Data• Other variables:
– Value and equal weighted industry returns and book/market from Ken French’s website
– Log (Number of IPOs + 1) from SDC– Herfindahl Index of sales from Compustat:
Sum of squares of firm sales
(Total industry sales)2
A high value means low competition
Hou and Robinson (2006) show H is related to returns
H =
Table 2: Summary Statistics by Industry and Quarter
Variable N Mean Min Max Median
VC Dollars (M)
3,502 110.1 0
15,174 7.3
VC Dollars/Assets
3,502 0.21% 0.00% 45.38% 0.01%
VW - Industry Return
3,502 3.8% -42.2% 68.6% 4.1%
EW - Industry Return
3,502 4.1% -41.8% 78.8% 3.5%
VW - Book/Market
3,502 0.58 0.09 1.81 0.51
EW - Book/Market
3,502 0.54 0.08 1.74 0.47
Log(NIPOs+1)
3,502 0.28 0 3.43 0
Herfindahl Index
3,502 0.14 0.01 0.73 0.10
Methodology
• Regression with quarterly and industry fixed effects and errors clustered by quarter and industry (Petersen, 2007, Thompson, 2006).
Table 3: Industry Fixed Effects Regressions
VC/A B/M Ret-1 Ret-2 IPO Herf Mkt-Rf SMB HML MOM I Q
VWRaw
-33.10 Y Y
(-4.92)
-30.90 3.86 0.07 0.05 -0.08 3.24 Y Y
(-5.64) (2.46) (1.72) (1.43) (-0.15) (1.29)
VWExcess
-22.22 4.54 0.04 0.01 -0.50 2.43 0.95 0.14 0.13 2.67 Y N
(-6.25) (6.57) (1.97) (0.26) (-1.02) (0.84) (25.55) (2.07) (1.77) (0.51)
EWRaw
-36.00 Y Y
(-4.01)
-34.09 -1.03 0.17 0.00 -0.34 1.64 Y Y
(-4.55) (-0.54) (3.19) (0.11) (-0.82) (0.57)
EWExcess
-27.64 1.03 0.09 -0.01 -0.65 1.08 0.88 1.03 0.28 6.42 Y N
(-4.39) (0.77) (3.30) (-0.68) (-1.45) (0.33) (16.12) (7.67) (2.54) (0.95)
N ranges from 3,468 to 3,502, R-Square ranges from 0.52 to 0.65
VC/A is the VC funding for each industry-quarter divided by total industry assets
Mkt-Rf, SMB, HML, MOM are contemporaneous; remaining variables are lagged.
Ind and Time are industry and quarter dummy variables.
T-statistics in parentheses; Standard errors clustered by quarter and industry.
Comments
• VC/Assets consistently significant
• IPOs and Herfindahl are generally not significant
• Are our results due to a certain time period, like the internet bubble period of 1998-2001?
Table 4: Time-period Break-down
VC/A is the VC funding for each industry-quarter divided by total industry assets
Industry and quarterly dummies are included
T-statistics in parentheses; Standard errors clustered by quarter and industry.
VC/A B/M Ret-1 Ret-2 IPO Herf N R2
VW Returns
All -30.90 3.86 0.07 0.05 -0.08 3.24 3,468 0.56
(-5.64) (2.46) (1.72) (1.43) (-0.15) (1.29)
NB -97.10 2.66 0.05 0.04 0.03 2.89 2,924 0.61
(-4.85) (1.99) (1.81) (1.12) (0.08) (1.01)
B -68.21 11.68 0.08 0.02 -0.59 5.26 544 0.48
(-10.23) (1.55) (0.74) (0.19) (-0.25) (0.50)
EW Returns
All -34.09 -1.03 0.17 0.00 -0.34 1.64 3,468 0.65
(-4.55) (-0.54) (3.19) (0.11) (-0.82) (0.57)
NB -97.55 -2.62 0.11 0.00 -0.19 2.14 2,924 0.70
(-2.00) (-1.90) (3.47) (0.00) (-0.43) (0.65)
B -73.59 9.40 0.27 -0.01 -2.10 -8.99 544 0.56
(-9.09) (0.71) (2.02) (-0.10) (-0.99) (-0.68)
Table 5: Funding Stage Break-down
VC/A B/M Ret-1 Ret-2 IPO Herf I&Q N R2
VW Returns
Seed -496.00 3.87 0.08 0.05 -0.08 3.27 Yes 3,468 0.56
(-1.47) (2.48) (1.74) (1.42) (-0.16) (1.31)
Startup -137.00 3.86 0.07 0.05 -0.08 3.26 Yes 3,468 0.56
(-5.12) (2.46) (1.73) (1.46) (-0.16) (1.30)
OtherEarly -402.00 3.84 0.07 0.05 -0.08 3.23 Yes 3,468 0.56
(-4.72) (2.47) (1.73) (1.43) (-0.15) (1.29)
Expansion -53.94 3.86 0.07 0.05 -0.08 3.24 Yes 3,468 0.56
(-5.87) (2.47) (1.72) (1.43) (-0.15) (1.29)
Later -212.00 3.89 0.07 0.05 -0.07 3.22 Yes 3,468 0.56
(-5.80) (2.46) (1.73) (1.42) (-0.14) (0.74)
EW Returns
Seed -1230.00 -1.04 0.17 0.00 -0.35 1.61 Yes 3,468 0.65
(-2.79) (-0.55) (3.20) (0.07) (-0.83) (0.55)
Startup -158.00 -1.03 0.17 0.01 -0.35 1.66 Yes 3,468 0.65
(-5.22) (-0.55) (3.18) (0.12) (-0.83) (0.57)
OtherEarly -411.00 -1.04 0.17 0.00 -0.34 1.64 Yes 3,468 0.65
(-4.17) (-0.55) (3.19) (0.10) (-0.82) (0.57)
Expansion -57.91 -1.03 0.17 0.00 -0.34 1.65 Yes 3,468 0.65
(-4.06) (-0.54) (3.18) (0.11) (-0.82) (0.57)
Later -221.00 -0.99 0.17 0.00 -0.33 1.63 Yes 3,468 0.65
(-5.23) (-0.52) (3.20) (0.09) (-0.80) (0.57)
Table 6: Industry Break-down
VC/A B/M Ret-1 Ret-2 IPO Herf N R2
VW Returns
Business -51.2 -5.81 -0.04 -0.06 -3.30 -30.32 102 0.08
Services (-2.55) (-0.36) (-0.38) (-0.59) (-0.88) (-0.66)
Telecom -1391.1 -0.91 -0.08 0.10 -5.33 -442.52 102 0.22
(-2.74) (-0.39) (-0.85) (0.84) (-2.03) (-1.88)
Drugs -1538.0 -4.10 -0.01 0.05 -2.66 -3.71 102 0.09
(-1.11) (-0.46) (-0.07) (0.54) (-1.62) (-0.06)
Computers -1412.7 13.02 0.04 0.11 -0.96 -591.67 102 0.05
(-1.70) (1.17) (0.27) (0.78) (-0.46) (-1.55)
Chips -9462.5 11.13 0.01 0.03 -1.06 -147.77 102 0.21
(-5.40) (0.78) (0.09) (0.27) (-0.48) (-2.32)
EW Returns
Business -44.3 -6.50 0.00 -0.06 -3.72 -13.13 102 0.04
Services (-1.59) (-0.33) (0.01) (-0.57) (-0.87) (-0.25)
Telecom -2246.0 -5.32 0.03 -0.00 -7.56 -202.84 102 0.10
(-2.00) (-1.27) (0.32) (-0.04) (-1.85) (-0.38)
Drugs -5786.3 -32.89 0.08 0.10 -6.40 196.86 102 0.11
(-3.03) (-1.70) (0.81) (0.95) (-2.15) (1.71)
Computers -1881.0 1.45 0.03 -0.05 -2.79 -448.87 102 0.05
(-1.76) (0.10) (0.29) (-0.43) (-0.97) (-0.95)
Chips -8310.3 3.62 0.01 0.07 -2.36 -137.73 102 0.10
(-3.35) (0.20) (0.09) (0.64) (-0.74) (-1.77)
Some Robustness Checks
• Including industries with zero funding
• Including “other stages”
• Other control variables: industry size, dividend yield, IPO initial returns, additional lags of the number of IPOs, equity share in new issues (Baker & Wurgler, 2000)
More Robustness: Redefining Industries
• Same analysis for “Tech” industry as defined by Loughran and Ritter (2004).
• Consistent significance at 1% level for EW and VW returns
Industry Operating Performance
• Does VC funding affect the subsequent year’s ROA for firms that are already public?
• Poor stock returns and poor operating performance for the same industry should be expected to go together.
• We compute ROA as Industry net income before extraordinary items from Compustat divided by total assets.
• We include Capex to assets and R&D to assets ratios.
• Also include industry and yearly dummies, and errors are clustered by both industry and year.
Table 7: Return on Assets
VC/ACAPEX/
A R&D/A Const I Y N R2
-38.43 0.04 No No 850 0.00
(-3.52) (8.78)
-28.47 0.03 Yes Yes 850 0.47
(-7.94) (8.06)
-25.41 0.11 0.20 0.01 Yes Yes 850 0.47
(-5.59) (1.64) (0.91) (2.10)
Dependent variable is industry-level return on assets: Net Income Before Extraordinary Items/Assets. Independent variables lagged one year.I and Y are industry and annual dummiesT-statistics are in parentheses; standard errors are clustered by industry and year
Conclusion
• Findings– Higher VC funding is associated with lower industry
returns in the subsequent quarter during 1980-2005.– True for equal and value weighted returns– Remains true in or out of the bubble. – True for most funding types and most of the 5 main
industries. – VC funding is negatively related to industry ROA in
the subsequent year.
Conclusion
• Our empirical results are consistent with the capital market myopia work of Sahlman and Stevenson (1985).
• Overoptimism on the part of venture capitalists leads directly to overfunding of a few key industries which precedes a decline in both industry stock returns and operating performance.