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

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

Journal of Forecasting, 2022, vol. 41, issue 6, 1131-1155

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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/for.2852

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:wly:jforec:v:41:y:2022:i:6:p:1131-1155

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().