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.

• Questions?