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.
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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.
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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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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.
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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.
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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.
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