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Using machine learning to identify and measure the family influence on companies

Author

Listed:
  • Mario Daniele Amore

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

  • Valentino D’angelo

    (Bocconi University (Italy, Milan))

  • Isabelle Le Breton-Miller

    (HEC Montréal - HEC Montréal)

  • Danny Miller

    (HEC Montréal - HEC Montréal)

  • Valerio Pelucco

    (Libera Università Internazionale degli Studi Sociali Guido Carli (Italy, Rome) - LUISS)

  • Marc van Essen

    (University of South Carolina [Columbia])

Abstract

Many studies have focused on family firms. Yet, grasping the nature of these organizations remains challenging because firms' familiness can take many forms, which are hard to trace with traditional data. We use ChatGPT – an application of machine learning – to try to unravel the complex and intangible influence of families on firms in large datasets. Whereas it often classifies family firms consistently with equity criteria, ChatGPT appears able to gauge families' legacy and values. Hence, it detects more family firms in countries where families have a strong influence on firms even without large equity stakes. Also, ChatGPT often treats lone-founder firms as non-family firms, whereas it assigns a higher family score to firms that are eponymous, heir-led, and with multiple family directors. Finally, classifying family firms using ChatGPT provides financially relevant information to investors.

Suggested Citation

  • Mario Daniele Amore & Valentino D’angelo & Isabelle Le Breton-Miller & Danny Miller & Valerio Pelucco & Marc van Essen, 2024. "Using machine learning to identify and measure the family influence on companies," Post-Print hal-05656773, HAL.
    • Handle: RePEc:hal:journl:hal-05656773
    DOI: 10.1016/j.jfbs.2024.100644
    as

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