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

Mariia Garkavenko, Tatiana Beliaeva (), Eric Gaussier (), Hamid Mirisaee, Cédric Lagnier and Agnès Guerraz
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Mariia Garkavenko: LIG - Laboratoire d'Informatique de Grenoble - 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 - 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

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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.

Date: 2023-09
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Published in Entrepreneurship Theory and Practice, 2023, 47 (5), pp.2017-2044. ⟨10.1177/10422587221121291⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04278158

DOI: 10.1177/10422587221121291

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