vc

Road to IPO // by digital kompakt & Deutsche Börse: Ein Blick auf die Situation unseres Ökosystems

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Handelsblatt: Investor Mark Schmitz from the asset management platform Equation sees a clear connection between the incubator and increasing venture capital inflows.

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A new VC agenda for Germany and Europe - Why we need to mobilize private growth capital and how we can do it

In this study, we highlight the persistent major hurdles hindering access to venture capital, including a lack of private capital from institutional investors, internationally uncompetitive legal frameworks and a lack of opportunities for startup employees and citizens to participate in the value creation of startups and young companies.

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Using hand-collected data on the backgrounds of venture capitalists (VCs), we show that in a typical venture capital firm in our sample, 13.9% of VCs have been entrepreneurs before becoming a VC, referred to as entrepreneur VCs. Both OLS and 2SLS analyses suggest that venture capital firms employing a greater fraction of entrepreneur VCs have better performance. In addition, the positive effect of entrepreneur VCs on venture capital firm performance is stronger for smaller and younger venture capital firms, and venture capital firms specializing in high-tech industries and in early-stage investments.
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Over the past three decades, universities in industrialized countries have become increasingly active as venture capital financiers. Here, we analyze if investments in university-affiliated portfolio companies, in the form of an institutional-personal relation between the university and the founders, are a commercially successful investment proposition. We use a hand-collected data set of 706 university portfolio companies in the United States and the United Kingdom to extend previous case-based evidence that investments in faculty- and student-led start-ups are an elusive promise that rarely pays off commercially.
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An increasingly global venture capital (VC) business raises the question whether foreign VCs’ investments pull economic activity away from domestic economies. Using a large sample of VC-backed European ventures, we analyze whether involvement of foreign VCs influences firms’ and entrepreneurs’ migration patterns. We provide evidence that foreign investors, in particular from the U.S., on average, back much better European ventures and increase the likelihood of foreign exits and emigration of entrepreneurs.
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In this paper we explore whether or not the experience as a founder of a venture capital-backed startup influences the performance of founders who become venture capitalists (VCs). We find that nearly 7% of VCs were previously founders of a venture-backed startup. Having a successful exit and being male and white increase the probability that a founder transitions into a venture capital career. Successful founder-VCs have investment success rates that are 6.
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There has been an increasing asymmetry between the rising interest in private companies and the limited availability of data. While a group of new commercial data providers has identified this gap as a promising business opportunity, and has started to provide structured information on private companies and their investors, little is known about the quality of the data they provide. In this paper, we compare detailed and verified proprietary information on 339 actual venture capital (VC) financing rounds from 396 investors in 108 different (mostly European) companies, with data included in eight frequently used VC databases to help academic scholars and investors better understand the coverage and quality of these datasets and, thus, interpret the results more accurately.
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We use machine learning to study how venture capitalists (VCs) make investment decisions. Using a large administrative data set on French entrepreneurs that contains VC-backed as well as non-VC-backed firms, we use algorithmic predictions of new ventures’ performance to identify the most promising ventures. We find that VCs invest in some firms that perform predictably poorly and pass on others that perform predictably well. Consistent with models of stereotypical thinking, we show that VCs select entrepreneurs whose characteristics are representative of the most successful entrepreneurs ( i.
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I conduct an investment screening performance benchmarking between 111 venture capital (VC) investment professionals and a supervised gradient boosted tree (or “XGBoost”) classification algorithm to create trust in machine learning (ML) -based screening approaches, accelerate the adoption thereof and ultimately enable the traditional VC model to scale. Using a comprehensive dataset of 77,279 European early-stage companies, I train a variety of ML algorithms to predict the success/failure outcome in a 3- to 5-year simulation window.
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