Performance

This paper focuses on funds of funds (FOFs) as a form of financial intermediation in private equity (both buyout and venture capital). After accounting for fees, FOFs provide returns equal to or above public market indices for both buyout and venture capital. While FOFs focusing on buyouts outperform public markets, they underperform direct fund investment strategies in buyout. In contrast, the average performance of FOFs in venture capital is on a par with results from direct venture fund investing. This suggests that FOFs in venture capital (but not in buyouts) are able to identify and access superior performing funds.

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.

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."