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

Author

Listed:
  • Mariia Garkavenko
  • Tatiana Beliaeva
  • Eric Gaussier
  • Hamid Mirisaee
  • Cédric Lagnier
  • Agnès Guerraz

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," Entrepreneurship Theory and Practice, , vol. 47(5), pages 2017-2044, September.
    • Handle: RePEc:sae:entthe:v:47:y:2023:i:5:p:2017-2044
    DOI: 10.1177/10422587221121291
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    References listed on IDEAS

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