Investigating the investment readiness of European SMEs: A machine learning approach

Christos Alexakis, Periklis Gogas, Giovanni Petrella, Michael Polemis () and Federica Salvadè
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Christos Alexakis: ESC [Rennes] - ESC Rennes School of Business
Periklis Gogas: DUTH - Democritus University of Thrace
Giovanni Petrella: Unicatt - Università cattolica del Sacro Cuore [Milano]
Federica Salvadè: PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université

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Abstract: This study exploits machine learning techniques to investigate the investment readiness of European small and medium-sized enterprises (SMEs). Understanding the drivers behind SMEs' willingness to use equity capital and foster innovation is crucial for promoting economic growth. Our analysis is grounded on the Survey on the Access to Finance of Enterprises (SAFE) released by the European Commission and the European Central Bank, which covers a vast sample of European SMEs. The empirical findings reveal that factors associated with the entrepreneurial ecosystem—such as regulatory frameworks, the availability of skilled staff, and perceived market outlook within a country—are critical drivers of investment readiness. Importantly, we find that access to debt financing and firm risk do not significantly influence SMEs' willingness to raise equity capital. Lastly, this research offers valuable insights for policymakers and equity providers, suggesting that tailored investment readiness programs that consider cultural and country-specific characteristics can unlock the full potential dynamics of European SMEs.

Keywords: Investment readiness; SMEs; Equity aversion; Machine learning; Entrepreneurial ecosystem (search for similar items in EconPapers)
Date: 2025-09
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Published in International Review of Financial Analysis, 2025, 105, pp.104439. ⟨10.1016/j.irfa.2025.104439⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05148711

DOI: 10.1016/j.irfa.2025.104439

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