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

作者：Liu Yang; Zeng Qingguo; Li Bobo; Ma Lili; OrdieresMeré Joaquín

来源：Journal of Forecasting, 2022, 41(6).

DOI：10.1002/FOR.2852

• acquired prediction
• failure prediction
• financial distress
• high‐tech startups
• imbalanced samples
• machine learning

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.

• 单位
  华南师范大学