We survey over 1,000 institutional and corporate venture capitalists (VCs) at more than 900 different firms to learn how their decisions and investments have been affected by the COVID-19 pandemic. We compare their survey answers to those provided by a large sample of VCs in early 2016 and analyzed in Gompers, Gornall, Kaplan, and Strebulaev (2020). VCs have slowed their investment pace (71% of normal) and expect to invest at 81% of their normal pace over the coming year. Not surprisingly, they have devoted more time to guiding the portfolio companies through the pandemic. VCs report that 52% of their portfolio companies are positively affected or unaffected by the pandemic; 38% are negatively affected; and 10% are severely negatively affected. Overall, they expect the pandemic to have a small negative effect on their fund IRRs (-1.6%) and MOICs (-0.07). Surprisingly, we find little change in the allocation of their time to helping portfolio companies relative to looking for new investments. In general, we find only modest differences between institutional and corporate VCs.
We develop a valuation model for venture capital--backed companies and apply it to 135 US unicorns, that is, private companies with reported valuations above $1 billion. We value unicorns using financial terms from legal filings and find that reported unicorn post--money valuations average 48% above fair value, with 14 being more than 100% above. Reported valuations assume that all shares are as valuable as the most recently issued preferred shares. We calculate values for each share class, which yields lower valuations because most unicorns gave recent investors major protections such as initial public offering (IPO) return guarantees (15%), vetoes over down-IPOs (24%), or seniority to all other investors (30%). Common shares lack all such protections and are 56% overvalued. After adjusting for these valuation-inflating terms, almost one-half (65 out of 135) of unicorns lose their unicorn status.
We survey 885 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment decisions; valuation; deal structure; post-investment value-added; exits; internal organization of firms; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. We compare our results to those for CFOs (Graham and Harvey 2001) and private equity investors (Gompers, Kaplan and Mukharlyamov forthcoming).
We develop a model of the joint capital structure decisions of banks and their borrowers. Strikingly high bank leverage emerges naturally from the interplay between two sets of forces. First, seniority and diversification reduce bank asset volatility by an order of magnitude relative to that of their borrowers. Second, previously unstudied supply chain effects mean that highly levered financial intermediaries can offer the lowest interest rates. Low asset volatility enables banks to take on high leverage safely; supply chain effects compel them to do so. Firms with low leverage also arise naturally, as borrowers internalize the systematic risk costs they impose on their lenders. Because risk assessment techniques from the Basel framework underlie our model, we can quantify the impact capital regulation and other government interventions have on leverage and fragility. Deposit insurance and the expectation of government bailouts increase not only bank risk taking, but also borrower risk taking. Capital regulation lowers bank leverage but can lead to compensating increases in the leverage of borrowers, which can paradoxically lead to riskier banks. Doubling current capital requirements would reduce the default risk of banks exposed to moral hazard by up to 90%, with only a small increase in bank interest rates.
Appendix included at above link.
Code. (Can be freely used for non-comercial purposes as long as original paper is cited.)
Locally-capped products are a popular but poorly understood type of structured
financial product. These contracts combine a guaranteed payoff with a bonus based
on the capped periodic returns of a reference portfolio. We show that in the USA
these products often contain unreasonably optimistic hypothetical scenarios in their
prospectuses,and conjecture that these unrealistic scenarios may contribute to their
popularity with uninformed investors. We also explain why locally-capped products
perform badly in turbulent markets and confirm this with evidence from the 2008
Open banking is the trend of empowering customers to share their banking data with fintechs and other banks. We compile a novel dataset documenting that governments in 49 countries have implemented open banking policies and 31 more are in active discussions. Following adoption, fintech venture capital investment increases by 50%, with more comprehensive policies showing larger effects. We examine the policy tradeoffs with a quantitative model of consumer data production and usage. Our calibrations show that customer-directed data sharing increases entry by improving entrant screening ability and product offerings, but harms some customers and can reduce ex-ante information production.
We study gender and race in high-impact entrepreneurship within a tightly controlled random
field experiment. We sent out 80,000 pitch emails introducing promising but fictitious start-ups to
28,000 venture capitalists and business angels. Each email was sent by a fictitious entrepreneur
with a randomly selected gender (male or female) and race (Asian or White). Female entrepreneurs
received an 8% higher rate of interested replies than male entrepreneurs pitching identical projects.
Asian entrepreneurs received a 6% higher rate than White entrepreneurs. Our results are not
consistent with discrimination against females or Asians at the initial contact stage of the investment
We show that private equity leveraged buyouts (LBOs) reduce perceived job quality despite not impacting average base pay. This appears to reflect higher risk for employees. Both job quality and employee incentive pay are more related to firm performance at private equity-owned companies than at public control firms, with 1% higher deal IRR associated with 0.7% more employee incentive pay. Also, higher leverage deals and employees with worse outside options and longer tenure drive the post-LBO satisfaction declines. Our results highlight how job quality is tied to job security and how ownership affects employees through mechanisms beyond base pay.
This paper develops the first option pricing model of venture capital-backed companies and their security values that incorporates the dilutive future financing rounds prevalent in the industry. Applying our model to 19,000 companies raising 37,000 rounds shows post-money valuations exceed fair values by 39%. Ignoring future rounds overstates the valuation impact of liquidation preferences by more than 100%. Counterintuitively, future “investor-friendly” rounds transfer value from current investors to founders and other common shareholders. Future terms closely resemble current terms, which makes current “investor-friendly” terms much less valuable to investors. Our valuations predict outcomes and the prices reported by specialized venture capital investors but are lower than values reported by mutual funds and dramatically higher than the values companies report for tax purposes.
This paper uses bank fragility to explain why bank loans are senior in firm capital structure. High leverage makes banks more vulnerable to financial distress than the typical bond investor, and thus makes banks willing to pay for seniority. Bank seniority emerges even when banks need skin in the game, as bank effort has more impact on a large senior loan than on a smaller junior claim with the same systematic risk. Adding deposit insurance or bailouts adds a subsidy to tail risk, which makes large senior claims even more attractive to banks. Empirically, this model explains why procyclical firms avoid bank loans and provides a host of debt structure predictions.
Over the past 30 years, venture capital has become a dominant force in the financing of innovative American companies. From Google to Intel to FedEx, companies supported by venture capital have profoundly changed the U.S. economy. Despite the young age of the venture capital industry, public companies with venture capital backing employ four million people and account for one-fifth of the market capitalization and 44% of the research and development spending of U.S. public companies. From research and development to employment to simple revenue, the companies funded by venture capital are a major part of the U.S. economy.
Fast-growing innovative companies—startups—operate unlike other businesses and raise money in similarly distinctive ways. Eschewing traditional banks and equity markets, they turn to a startup financing ecosystem that includes corporate and institutional VC funds, crowdfunding, angel investors, growth equity, and private equity. We start by discussing key relevant stylized facts and how they lead to contracting frictions. We then discuss the various investment contracts used by startups and their associated cash flow and control rights. Given that startups almost invariably raise multiple rounds of funding, we then discuss contracting issues associated with the evolution of cash flow and control rights. We finally discuss approaches to valuing startups, their financial securities, and the impact of contractual terms on valuation.
"Venture Capital Valuation," 2022, Forthcoming, Palgrave Encyclopedia of Private Equity
(with Ilya Strebulaev)
Startups are notoriously hard to value. First, by their very nature, they are high-risk bets that may take years to realize. Second, startups raise money
using complicated financial contracts; with distinct equity securities issued each time the company raises money. Yet assigning valuations to startups is
essential. Such valuations allow entrepreneurs to get the money they need, and more broadly facilitate the evaluation of VCs, risk management, and secondary market trading.
For decades now, venture capitalists have played a crucial role in the economy by financing high-growth start-ups. While the companies they’ve backed—Amazon, Apple, Facebook, Google, and more—are constantly in the headlines, very little is known about what VCs actually do and how they create value. To pull the curtain back, Paul Gompers of Harvard Business School, Will Gornall of the Sauder School of Business, Steven N. Kaplan of the Chicago Booth School of Business, and Ilya A. Strebulaev of Stanford Business School conducted what is perhaps the most comprehensive survey of VC firms to date. In this article, they share their findings, offering details on how VCs hunt for deals, assess and winnow down opportunities, add value to portfolio companies, structure agreements with founders, and operate their own firms. These insights into VC practices can be helpful to entrepreneurs trying to raise capital, corporate investment arms that want to emulate VCs’ success, and policy makers who seek to build entrepreneurial ecosystems in their communities.
Based on the paper "How Do Venture Capitalists Make Decisions?" 2020, Journal of Financial Economics 135(1), 169–190 (with Paul A. Gompers, Steven N. Kaplan, and Ilya A. Strebulaev)
Email me if you are a Canadian university student interested in volunteering as a research assistant. I am presently working with the Stanford Graduate School of Business Venture Capital Initiative. There are several possible opportunities:
1) Read venture capital financing contracts and enter the data from them into spreadsheets. This is likely of particular interest to students interested in law school.
2) Write Python code to understand the meaning of financial contracts. This would involve NLP and other cool programing stuff.