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Assessing the Factors Related to a Start-Up’s Valuation Using Prediction and Causal Discovery

Author

Listed:
  • Mariia Garkavenko

    (LIG - Laboratoire d'Informatique de Grenoble - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Tatiana Beliaeva

    (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University))

  • Eric Gaussier

    (LIG - Laboratoire d'Informatique de Grenoble - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, UGA - Université Grenoble Alpes)

  • Hamid Mirisaee

    (Skopai)

  • Cédric Lagnier

    (Skopai)

  • Agnès Guerraz

    (Skopai)

Abstract

Research indicates that investors rely on various criteria to evaluate early-stage companies. However, past research in this area has focused on subsets of factors and does not distinguish between the predictors and causal determinants of start-up valuation. In our study, we applied machine learning and causal discovery to analyze a comprehensive dataset with 57 independent variables and 2,366 valuations of start-ups in the United Kingdom. The results show a strong relationship between good predictors and causal determinants of valuation. However, noncausal variables may still be useful for prediction, and inversely, some observed causes may not help in the prediction task.

Suggested Citation

  • Mariia Garkavenko & Tatiana Beliaeva & Eric Gaussier & Hamid Mirisaee & Cédric Lagnier & Agnès Guerraz, 2023. "Assessing the Factors Related to a Start-Up’s Valuation Using Prediction and Causal Discovery," Post-Print hal-04278158, HAL.
    • Handle: RePEc:hal:journl:hal-04278158
    DOI: 10.1177/10422587221121291
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