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

Author

Listed:
  • 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])

  • Michael Polemis

    (University of Piraeus, Hellenic Open University [Patras])

  • Federica Salvadè

    (PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

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.

Suggested Citation

  • Christos Alexakis & Periklis Gogas & Giovanni Petrella & Michael Polemis & Federica Salvadè, 2025. "Investigating the investment readiness of European SMEs: A machine learning approach," Post-Print hal-05148711, HAL.
  • Handle: RePEc:hal:journl:hal-05148711
    DOI: 10.1016/j.irfa.2025.104439
    as

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