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Valuation of Startups: A Machine Learning Perspective

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12656)

Abstract

We address the problem of startup valuation from a machine learning perspective with a focus on European startups. More precisely, we aim to infer the valuation of startups corresponding to the funding rounds for which only the raised amount was announced. To this end, we mine Crunchbase, a well-established source of information on companies. We study the discrepancy between the properties of the funding rounds with and without the startup’s valuation announcement and show that the Domain Adaptation framework is suitable for this task. Finally, we propose a method that outperforms, by a large margin, the approaches proposed previously in the literature.

Keywords

  • Predictive models
  • Domain adaptation
  • Startup valuation

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Notes

  1. 1.

    https://news.crunchbase.com/news/the-q4-eoy-2019-global-vc-report-a-strong-end-to-a-good-but-not-fantastic-year/.

  2. 2.

    https://berkonomics.com/?p=2752.

  3. 3.

    https://beta.companieshouse.gov.uk/.

  4. 4.

    https://github.com/garkavem/Company-House-SH01-Parsing.

  5. 5.

    The list of European countries, referred to as Europe and abbreviated as EU hereafter, is detailed in [1] for space reasons.

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Correspondence to Mariia Garkavenko .

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Garkavenko, M., Mirisaee, H., Gaussier, E., Guerraz, A., Lagnier, C. (2021). Valuation of Startups: A Machine Learning Perspective. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-72113-8_12

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