Venture Capital (Mis)allocation in the Age of AI

Victor Lyonnet and Lea H. Stern
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Victor Lyonnet: Ohio State University
Lea H. Stern: University of Washington

Working Paper Series from Ohio State University, Charles A. Dice Center for Research in Financial Economics

Abstract: 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.

JEL-codes: D8 D83 G11 G24 G41 M13 (search for similar items in EconPapers)
Date: 2022-02
New Economics Papers: this item is included in nep-big, nep-cfn and nep-sbm
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ecl:ohidic:2022-02

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