Yael V. Hochberg

Whom You Know Matters: Venture Capital Networks and Investment Performance

Many financial markets are characterized by strong relationships and networks, rather than arms-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company investments. VC firms that enjoy more influential network positions have significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or sale to another company. Similarly, the portfolio companies of better-networked VC firms are significantly more likely to survive to subsequent financing and to eventual exit. Finally, we provide initial evidence on the evolution of VC networks.

Networking as a Barrier to Entry and the Competitive Supply of Venture Capital

We examine whether networks among incumbent venture capital firms help restrict entryinto local VC markets in the U.S., thus improving VCs bargaining power over entrepreneurs. We show that VC markets with more extensive networking among the incumbent players experience less entry. The effect is sizeable economically and appears robust to plausible endogeneity concerns. Entry is accommodated if the entrant has established relationships with a target-market incumbent in its own home market. In turn, incumbents react strategically to an increased threat of entry, in the sense that they freeze out any incumbent that builds a relationship with a potential entrant. Finally, companies seeking venture capital raise money on worse terms in more densely networked markets while increased entry is associated with higher valuations.

Venture Capital and Corporate Governance in the Newly Public Firm

I examine the effects of venture capital backing on the corporate governance of the entrepreneurial firm at the time of transition from private to public ownership. Using a selection model framework that instruments for venture backing with variations in the supply of venture capital, I conduct three sets of tests comparing corporate governance in venture- and non-venture-backed IPO firms. Venture-backed firms have lower levels of earnings management, more positive reactions to the adoption of shareholder rights agreements, and more independent board structures than similar non-venture-backed firms, consistent with better governance. These effects are not common to all pre-IPO large shareholders.

Accelerating Startups: The Seed Accelerator Phenomenon

We examine and discuss the seed accelerator phenomenon which has recently received much attention both in the US and across the globe. While accelerators appear to be proliferating quickly, little is known regarding the value of these programs; how to define accelerator programs; the differences between accelerators, incubators, angel investors and co-working environments; and the importance of the various aspects of these programs to the ultimate success of their graduates, the local entrepreneurship ecosystems and the broader U.S. economy.

Is Ipo Underperformance a Peso Problem

Recent studies suggest that the underperformance of IPOs in the post-1970 sample may be a small sample effect or “Peso problem.” That is, IPO underperformance may result from observing too few star performers ex post than were expected ex ante. We develop a model of IPO performance that captures this intuition by allowing returns to be drawn from mixtures of outstanding, benchmark, or poor performing states. We estimate the model under the null of no ex ante average IPO underperformance and construct small sample distributions of various statistics measuring IPO relative performance. We find that small sample biases are extremely unlikely to account for the magnitude of the post-1970 IPO underperformance observed in data.

The Size and Specialization of Direct Investment Portfolios

Existing models of the size and scope of investment activity traditionally assume an infinite pool of ex-ante identical projects, despite the fact that managers often face a limited choice of projects that vary in quality. In this paper, we investigate optimal project selection in a model in which a portfolio manager observes a limited pool of heterogeneous investment opportunities. We use an order statistics argument to derive predictions on the size and scope of the investment portfolio. Counter to existing models, we show that the number and specialization of investments within a portfolio are substitutes; variables that increase (decrease) the set of available projects or returns to investment activity, such as skill (competition) cause the portfolio to be larger (smaller) and more generalized (specialized). We test our models predictions and document new stylized facts in a setting with highly skewed payoffs, formalized specialization, and a high shadow cost of access to potential projects: the U.S. venture capital industry.

Accelerating Entrepreneurs and Ecosystems: The Seed Accelerator Model

Recent years have seen the emergence of a new institutional form in the entrepreneurial ecosystem: the seed accelerator. These fixed-term, cohort-based “boot camps” for start-ups offer educational and mentorship programs for start-up founders, exposing them to a wide variety of mentors, including former entrepreneurs, venture capitalists (VCs), angel investors, and corporate executives, and culminate in a public pitch event, or “demo day,” during which the graduating cohort of start-up companies pitch their businesses to a large group of potential investors. In practice, accelerator programs are a combination of previously distinct services or functions that were each individually costly for an entrepreneur to find and obtain. The accelerator approach has been widely adopted by private groups, public and government efforts, and by corporations. While proliferation of accelerators is clearly evident, with worldwide estimates of 3000+ programs in existence, research on the role and efficacy of these programs has been limited. In this article, I provide an introduction to the accelerator model and summarize recent evidence on its effects on the regional entrepreneurial environment.

Market Timing and Investment Selection: Evidence from Real Estate Investors

We examine commercial real estate fund managers’ abilities to generate abnormal profits through selection of outperforming property submarket segments or through the timing of entry into and exit from submarkets. The vast majority of portfolio managers exhibit little market timing ability, with the exception of non-NYSE real estate investment trusts after the financial crisis. A substantial fraction of managers seems able to successfully select property submarkets. Selection performance exhibits significant persistence. Managers that are active in more liquid markets tend to exhibit better timing performance, while managers exhibiting better selection ability appear to be active in less liquid markets.

Accelerators and the Regional Supply of Venture Capital Investment

Recent years have seen the rapid emergence of a new type of program aimed at seeding startup companies. These programs, often referred to as accelerators, differ from previously known seed-stage institutions such as incubators and angel groups. While proliferation of such accelerators is evident, evidence on efficacy and role of these programs is scant. Nonetheless, local governments and founders of such programs often cite the motivation for their establishment and funding as the desire to transform their local economies through the establishment of a startup technology cluster in their region. In this paper, we attempt to assess the impact that such programs can have on the entrepreneurial ecosystem of the regions in which they are established, by exploring the effects of accelerators on the availability and provision of seed and early stage venture capital funding in the local region.

Is IPO Underperformance a Peso Problem

Recent studies suggest that the underperformance of IPOs in the post-1970 sample may be a small sample effect or %u201CPeso%u201D problem. That is, IPO underperformance may result from observing too few star performers ex-post than were expected ex-ante. We develop a model of IPO performance that captures this intuition by allowing returns to be drawn from mixtures of outstanding, benchmark, or poor performing states. We estimate the model under the null of no ex-ante average IPO underperformance and construct small sample distributions of various statistics measuring IPO relative performance. We find that small sample biases are extremely unlikely to account for the magnitude of the post-1970 IPO underperformance observed in data.