Data-driven Investors

Maxime Bonelli
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Maxime Bonelli: HEC Paris

No 1470, HEC Research Papers Series from 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 (search for similar items in EconPapers)
JEL-codes: G24 L26 O30 (search for similar items in EconPapers)
Pages: 114 pages
Date: 2023-02-22
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-sbm
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1470

DOI: 10.2139/ssrn.4362173

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