HEC Research Papers Series,
HEC Paris

Abstract: Using data technologies, like machine learning, investors can gain a comparative advantage in forecasting outcomes frequently observed in historical data. I investigate the implications for capital allocation using venture capitalists (VCs) as a laboratory. VCs adopting data technologies tilt their investments towards startups developing businesses similar to those already explored, and become better at avoiding failures within this pool. However, these VCs become concurrently less likely to pick startups achieving rare major success. Plausibly exogenous variations in VCs' screening automation suggest a causality between data technologies adoption and these effects. These findings highlight potential downsides of investors embracing data technologies.

Keywords: big data; machine learning; artificial intelligence; venture capital; entrepreneurship; innovation; capital allocation

JEL-codes: G24; L26; O30

114 pages, February 22, 2023

Full text files

papers.cfm?abstract_id=4362173 HTML file Full text

Download statistics

• HEC Paris
• Ordering Working Papers
• Home page for this series

Questions (including download problems) about the papers in this series should be directed to Antoine Haldemann ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:ebg:heccah:1470This page generated on 2024-09-13 22:19:53.