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

by Yang Liu, Qingguo Zeng, Bobo Li, Lili Ma and Joaquín Ordieres-Meré
Reference:
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), In Journal of Forecasting, volume 41, 2022.
Bibtex Entry:
@Article{https://doi.org/10.1002/for.2852,
  author = {Yang Liu and Qingguo Zeng and Bobo Li and Lili Ma and Joaqu'in Ordieres-Meré},
  title = {Anticipating financial distress of high-tech startups in the European Union: A machine learning approach for imbalanced samples},
  journal = {Journal of Forecasting},
  volume = {41},
  number = {6},
  pages = {1131-1155},
  keywords = {acquired prediction, failure prediction, financial distress, high-tech startups, imbalanced samples, machine learning},
  doi = {https://doi.org/10.1002/for.2852},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2852},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2852},
  gsid = {507598400150836401},
  ncites = {4},
  year = {2022},
}