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Data-driven Investors

Author

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  • Bonelli, Maxime

    (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.

Suggested Citation

  • Bonelli, Maxime, 2023. "Data-driven Investors," HEC Research Papers Series 1470, HEC Paris.
    • Handle: RePEc:ebg:heccah:1470
    DOI: 10.2139/ssrn.4362173
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    More about this item

    Keywords

    big data; machine learning; artificial intelligence; venture capital; entrepreneurship; innovation; capital allocation;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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