Conference Agenda

Please note that all times are shown in the time zone of the conference. The current conference time is: 1st Nov 2024, 12:14:24am CET

 
 
Session Overview
Session
CF 09: Entrepreneurship and Growth
Time:
Friday, 18/Aug/2023:
8:30am - 10:00am

Session Chair: Isil Erel, The Ohio State University
Location: 4A-33 (floor 4)


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Presentations
ID: 667

Venture Labor: A Nonfinancial Signal for Start-up Success

Sean Cao1, Jie He2, Zhilu Lin3, Xiao Ren4

1University of Maryland; 2University of Georgia; 3Clarkson University, United States of America; 4Chinese University of Hong Kong, Shenzhen

Discussant: Jing Xue (University of Maryland)

We examine an emerging phenomenon that talented employees leave successful entrepreneurial firms to join less mature start-ups. Using proprietary person-level data and private firm data, we find that the presence of these “serial venture employees” positively predicts their new employers’ future success in terms of exit likelihoods, size growth, venture capital financing, and innovation productivity. Such predictive power is more likely driven by a screening/matching channel rather than venture labor’s nurturing role. Our paper sheds light on an underexplored pattern of inter-firm labor flow, which provides a nonfinancial yet value-relevant signal about private firms for investors and stakeholders.

EFA2023_667_CF 09_1_Venture Labor.pdf


ID: 2096

Venture Capital (Mis)Allocation in the Age of AI

Victor Lyonnet1, Lea Stern2

1Ohio State University, United States of America; 2University of Washington

Discussant: Matthias Qian (University of Oxford)

How do 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.

EFA2023_2096_CF 09_2_Venture Capital (Mis)Allocation in the Age of AI.pdf


ID: 275

How do firms choose between growth and efficiency?

Laurent Fresard1, Loriano Mancini1, Enrique Schroth2, Davide Sinno1

1Institute of Finance, USI Lugano; 2EDHEC Business School

Discussant: Roberto Steri (University of Luxembourg)

We estimate the unobservable effort that firms put into boosting their efficiency. Identification comes from a model in which firms accumulate capital but also choose a flow of effort that controls efficiency period by period. Model estimates show that, for all cohorts and industries, young firms choose relatively more growth and old firms choose more efficiency. Amongst young firms, higher capital growth predicts higher markups in the long-term, but increases the risk of not surviving into maturity. Our model estimates help explain the priced firms’ exposures to the profitability and in- vestment risk factors of the investment CAPM.

EFA2023_275_CF 09_3_How do firms choose between growth and efficiency.pdf


 
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