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A Machine Learning Approach to Entrepreneurial Finance Modelling

In: Operational Research Methods in Business, Finance and Economics

Author

Listed:
  • Max Berre

    (Audencia Business School
    Nyenrode Business University)

Abstract

Traditionally, estimating valuation relies on firm data and concrete economic indicators. So does modelling of startup investment selection and startup survivability. However, recent advancements in machine learning have given rise to customizable segmented-modelling approaches. While classical economic theory describes that firm valuations and survival rates are modelled based on revenues, growth rates, and risk, the valuation of startup often proves the exception to the rule. Meanwhile both startup investor selection and startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Likewise, for startup survivability, which is known to be influenced by risks, revenues, age-of-firm, and access to finance, specific causality is also unclear. Because details are not disclosed, roles played by other factors (industry, business models, geography, and intellectual property) can often only be guessed at. This study is an in-depth examination outlining methods and approaches for application of segmented modelling in entrepreneurial finance, as well as ways in which they can be applied using existing data for purposes to examine selection, valuation, and survivability.

Suggested Citation

  • Max Berre, 2023. "A Machine Learning Approach to Entrepreneurial Finance Modelling," Lecture Notes in Operations Research, in: Constantin Zopounidis & Angeliki Liadaki & Marianna Eskantar (ed.), Operational Research Methods in Business, Finance and Economics, pages 7-36, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-31241-0_2
    DOI: 10.1007/978-3-031-31241-0_2
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