Empirical models based on features ranking techniques for corporate financial distress prediction

DOI10.1016/J.CAMWA.2012.06.003zbMath1268.91173OpenAlexW2004177594MaRDI QIDQ356191

Jerome Yen, Kin Keung Lai, Li-Gang Zhou

Publication date: 25 July 2013

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2012.06.003

zbMATH Keywords

empirical modelsfeatures rankingfinancial distress prediction

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Economic models of real-world systems (e.g., electricity markets, etc.) (91B74) Corporate finance (dividends, real options, etc.) (91G50)

Uses Software

• RegEM
• C4.5
• STPRTool

Cites Work

• Bankruptcy prediction in banks and firms via statistical and intelligent techniques -- a review
• A decision-theoretic generalization of on-line learning and an application to boosting
• Data Mining and Knowledge Discovery Handbook
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Empirical models based on features ranking techniques for corporate financial distress prediction