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Is firm growth random? A machine learning perspective

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  • van Witteloostuijn, Arjen
  • Kolkman, Daan

Abstract

This study contributes to the firm growth debate by applying machine learning. We compare a prominent machine learning technique – random forest analysis (RFA) – to traditional regression in terms of their goodness-of-fit on a dataset of 168,055 firms from Belgium and the Netherlands. For each of these firms, we have one to six years of historical data involving demographic and financial information. The data show high variation in firm growth rates, which is difficult to capture with traditional linear regression (R2 in the range of 0.05–0.06). The RFA fares three to four times better, achieving a much higher goodness-of-fit (R2 of 0.16–0.23). RFA indicates that perhaps firm growth is less random than suggested by traditional regression analysis. Generally, given the modest selection of variables in our dataset, this demonstrates that machine learning can be of value to firm growth research.

Suggested Citation

  • van Witteloostuijn, Arjen & Kolkman, Daan, 2019. "Is firm growth random? A machine learning perspective," Journal of Business Venturing Insights, Elsevier, vol. 11(C), pages 1-1.
    • Handle: RePEc:eee:jobuve:v:11:y:2019:i:c:3
    DOI: 10.1016/j.jbvi.2018.e00107
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    Cited by:

    1. Zhang, Jianhong & van Witteloostuijn, Arjen & Zhou, Chaohong & Zhou, Shengyang, 2024. "Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis," Journal of World Business, Elsevier, vol. 59(2).
    2. Coad, Alex & Karlsson, Johan, 2022. "A field guide for gazelle hunters: Small, old firms are unlikely to become high-growth firms," Journal of Business Venturing Insights, Elsevier, vol. 17(C).
    3. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    4. Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
    5. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    6. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
    7. Juan R. Ferrer & Mar�a Carmen Garc�a-Cortijo & Vicente Pinilla & Juan Sebasti�n Castillo-Valero & Ra�l Serrano, 2022. "Business growth and sustainability in the spanish wine industry," Documentos de Trabajo dt2022-03, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    8. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    9. Ho-Chang Chae, 2024. "In search of gazelles: machine learning prediction for Korean high-growth firms," Small Business Economics, Springer, vol. 62(1), pages 243-284, January.
    10. Williamson, Amanda Jasmine & Battisti, Martina & Pollack, Jeffrey M., 2022. "Capturing passion expressed in text with artificial intelligence (AI): Affective passion waned, and identity centrality was sustained in social ventures," Journal of Business Venturing Insights, Elsevier, vol. 17(C).
    11. Delmar, Frédéric & Wallin, Jonas & Nofal, Ahmed Maged, 2022. "Modeling new-firm growth and survival with panel data using event magnitude regression," Journal of Business Venturing, Elsevier, vol. 37(5).

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