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The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage

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  • Clement Gastaud
  • Theophile Carniel
  • Jean-Michel Dalle

Abstract

We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as well as the position of investors in the investment network, pioneering the use of Graph Neural Networks (GNN), a recent deep learning technique that enables the handling of graphs as such and as a whole. We show that the different stages of fundraising, early- and growth-stage, are associated with different success factors. Our results suggest a marked relevance of startup competition for early stage while growth-stage fundraising is influenced by network features. Both of these factors tend to average out in global models, which could lead to the false impression that startup success in fundraising would mostly if not only be influenced by its intrinsic characteristics, notably those of their founders.

Suggested Citation

  • Clement Gastaud & Theophile Carniel & Jean-Michel Dalle, 2019. "The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage," Papers 1906.03210, arXiv.org.
  • Handle: RePEc:arx:papers:1906.03210
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    File URL: http://arxiv.org/pdf/1906.03210
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    Cited by:

    1. Lele Cao & Vilhelm von Ehrenheim & Sebastian Krakowski & Xiaoxue Li & Alexandra Lutz, 2022. "Using Deep Learning to Find the Next Unicorn: A Practical Synthesis," Papers 2210.14195, arXiv.org, revised Jun 2024.
    2. Vitalis, Kyriacos & Stefanidis, Dimosthenis & Pallis, George & Dikaiakos, Marios & Nicolaou, Nicos & Nicolaides, Christos, 2024. "Quantifying the impact of online social networks on the success of entrepreneurs," OSF Preprints x6vda, Center for Open Science.
    3. Lele Cao & Gustaf Halvardsson & Andrew McCornack & Vilhelm von Ehrenheim & Pawel Herman, 2023. "Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems," Papers 2309.16888, arXiv.org, revised Jun 2024.

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