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Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples

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  • Yang Liu
  • Qingguo Zeng
  • Bobo Li
  • Lili Ma
  • Joaquín Ordieres‐Meré

Abstract

The financial status of high‐tech startups directly reflects a country's economic vitality. Hence, the financial distress of high‐tech startups will hinder a country's economic growth. In this study, we extract financial data of seven countries from the VICO 2.0 dataset; the sample includes high‐tech startups (labeled as acquired, nonacquired, failure, and nonfailure) between 2005 and 2014. We utilize seven algorithms in machine learning to identify critical variables that predict financial distress of high‐tech startups in the European Union (EU), thereby preventing high‐tech startup failure or acquisition. Specifically, our experimental results also thoroughly verify the seven countries' economic situation after the 2008 economic crisis. Overall, this work can provide sufficient financial risk warnings for creditors, investors, and managers and support businesses from each countries' commercial sectors.

Suggested Citation

  • Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré, 2022. "Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1131-1155, September.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:6:p:1131-1155
    DOI: 10.1002/for.2852
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