Venture Capital (Mis)Allocation in the Age of AI

Fisher College of Business Working Paper No. 2022-03-002

Charles A. Dice Center Working Paper No. 2022-02

67 Pages Posted: 16 Feb 2022 Last revised: 10 Jun 2022

See all articles by Victor Lyonnet

Victor Lyonnet

Ohio State University (OSU)

Léa H. Stern

University of Washington - Michael G. Foster School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: June 9, 2022

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.

Keywords: venture capital, machine learning, entrepreneurship, algorithmic decision-aid, explainable AI (XAI), capital allocation

JEL Classification: G11, G24,G41, M13, D83, D8

Suggested Citation

Lyonnet, Victor and Stern, Lea H., Venture Capital (Mis)Allocation in the Age of AI (June 9, 2022). Fisher College of Business Working Paper No. 2022-03-002, Charles A. Dice Center Working Paper No. 2022-02, Available at SSRN: https://ssrn.com/abstract=4035930 or http://dx.doi.org/10.2139/ssrn.4035930

Victor Lyonnet (Contact Author)

Ohio State University (OSU) ( email )

Columbus, OH 43210
United States

Lea H. Stern

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

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