Research

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.e., characteristics that occur more frequently among the best performing entrepreneurs relative to the other ones). Although VCs rely on accurate stereotypes, they make prediction errors as they exaggerate some representative features of success in their selection of entrepreneurs (e.g., male, highly educated, Paris-based, and high-tech entrepreneurs). Overall, algorithmic decision aids show promise to broaden the scope of VCs’ investments and founder diversity.

Investors increasingly look for private equity managers to provide opportunities for co-investing outside the fund structure, thereby saving fees and carried interest payments. In this paper we use a large sample of buyout and venture capital co-investments to test how such deals compare with the remaining fund investments. In contrast to Fang, Ivashina and Lerner (2015) we find no evidence of adverse selection. Gross return distributions of co-investments and other deals are similar. Co-investments generally have lower costs to investors. We simulate net returns to investors and demonstrate how reasonably sized portfolios of co-investments significantly out-perform fund returns.

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.

We use investment-level data to study performance persistence in venture capital (VC). Consistent with prior studies, we find that each additional initial public offering (IPO) among a VC firm’s first ten investments predicts as much as an 8% higher IPO rate on its subsequent investments, though this effect erodes with time. In exploring its sources, we document several additional facts: successful outcomes stem in large part from investing in the right places at the right times; VC firms do not persist in their ability to choose the right places and times to invest; but early success does lead to investing in later rounds and in larger syndicates. This pattern of results seems most consistent with the idea that initial success improves access to deal flow. That preferential access raises the quality of subsequent investments, perpetuating performance differences in initial investments.

We use investment-level data to study performance persistence in venture capital (VC). Consistent with prior studies, we find that each additional initial public offering (IPO) among a VC firm’s first ten investments predicts as much as an 8% higher IPO rate on its subsequent investments, though this effect erodes with time. In exploring its sources, we document several additional facts: successful outcomes stem in large part from investing in the right places at the right times; VC firms do not persist in their ability to choose the right places and times to invest; but early success does lead to investing in later rounds and in larger syndicates. This pattern of results seems most consistent with the idea that initial success improves access to deal flow. That preferential access raises the quality of subsequent investments, perpetuating performance differences in initial investments.

Our study introduces venture capital (VC) investors’ personality as a new perspective on investment performance. We assemble a sample of 911 VC investors with 8,258 investments and apply novel text analysis methods to assess investors’ personality traits based on their Tweets. Drawing on interactionist perspectives of the personality– performance relationship, we develop and empirically test theory on the impact of VC investors’ personality traits on investment success. We find that extraversion relates positively, while agreeableness and conscientiousness relate negatively, to the likelihood of a successful exit. By disentangling treatment and selection effects, we find that personality is primarily related to investors’ ability to create more value for the venture post-investment rather than select higher quality ventures ex-ante."