A Machine Learning Approach to Entrepreneurial Finance Modelling

Max Berre ()
Additional contact information
Max Berre: Audencia Business School

A chapter in Operational Research Methods in Business, Finance and Economics, 2023, pp 7-36 from Springer

Abstract: 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.

Keywords: CART; Decision tree; Valuation; Startup valuation; Startup selection; Investment selection; Startup survival startup survivability; Venture capital; Entrepreneurial finance; Machine learning; Hierarchical analysis (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-31241-0_2

Ordering information: This item can be ordered from
http://www.springer.com/9783031312410

DOI: 10.1007/978-3-031-31241-0_2

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().