Abstract
AbstractThe 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
