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Ravi, V. and Pramodh, C. (2008) Threshold Accepting Trained Principal Component Neural Network and Feature Subset Selection: Application to Bankruptcy Prediction in Banks. Applied Soft Computing, 8, 1539-1548.
https://doi.org/10.1016/j.asoc.2007.12.003

has been cited by the following article:

• TITLE: Bank Failure Prediction in Relation to the Business Life Cycle

  AUTHORS: Yishu Wang, Jing Yang, Zhenjia Liu

  KEYWORDS: Bank Failure Prediction, Bank, Global

  JOURNAL NAME: Modern Economy, Vol.10 No.3, March 20, 2019

  ABSTRACT: The results suggest that the capital ratio, loan ratio, non-performing loans, provisions for loan losses, fixed assets, return of equity, ratio of interest income to interest expenses, and ratio of non-interest income to non-interest expenses all had different correlations to the financial distress experienced by banks that were at a business life cycle stage. The logistic model employed in this study for predicting bank failure explains most of the banking trends in NIC banks at the declining stage. The accuracy of G8 banks at the growth stage performed well, whereas NIC banks at the declining stage performed poorly.