Benchmarking

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. We further explore performance implications of VCs with prior experience in a finance-related field (i.e., Wall Street experience) and prior experience in a non-finance related field (i.e., Main Street experience). We find that contrary to prior experience in entrepreneurship, neither prior experience in Wall Street nor in Main Street is significantly related to venture capital firm performance. Finally, we provide evidence that entrepreneur VCs have greater individual performance in terms of VC rankings established by Forbes. Overall, our results are consistent with the idea that entrepreneur VCs have a better understanding of the business of starting and developing a new firm due to their first-hands experience, and play an important role in reducing the gaps in information and difference of opinions between an entrepreneur and the VCs backing the entrepreneur.

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. We find that greater financing rounds are more likely to be reported than lower ones. Similarly, financing round sizes and post-money valuations are more likely to be reported for greater financing rounds than for lower ones. Our analysis reveals that VentureSource, Pitchbook and Crunchbase have the best coverage, and are the most accurate databases across our key dimensions of general company data, founders and funding information. We describe our findings in detail and discuss potential implications for researchers and practitioners.

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. XGBoost algorithms show particularly excellent performance in terms of accuracy and recall, which denote the most important metrics in my setup. I benchmark the performance of the selected algorithm against that of the VC investment professionals by providing equal information in the form of 10 company one-pagers via an online survey and requesting respondents to select the five most promising companies for further evaluation. In addition to finding characteristic- specific performance dependencies for VCs, I find that the XGBoost algorithm outperforms the median VC by 25% and the average VC by 29%. Although I do not suggest replacing humans with ML-based approaches, I recommend an augmented solution where intelligent algorithms narrow down the upper part of the deal-flow funnel, allowing VC investment professionals to focus their manual efforts on the lower part of the funnel. Using this approach, they can rely on a scalable but objective pre-selection and focus their manual resources on evaluating the most promising opportunities and putting themselves into the best position to secure these deals.