Artikel
Bankruptcy prediction and stress quantification using support vector machine: Evidence from Indian banks
Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature available indicates that research is currently limited on banks' stress quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific and quantitative approaches that can be used to predict bank survival and failure probabilities. Against this backdrop, the present study attempts to establish a bankruptcy prediction model using a machine learning approach and to compute and compare the financial stress that the banks face. The study uses the data of failed and surviving private and public sector banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features, and in the second step, important features are fed into the support vector machine to create a forecasting model. The threshold values of the features for the decision boundary which separates failed banks from survival banks are calculated using the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis function kernel shows 71.43% accuracy. The study helps to carry out comparative analyses of financial stress of the banks and has significant implications for their decisions of various stakeholders such as shareholders, management of the banks, analysts, and policymakers.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 2 ; Pages: 1-22 ; Basel: MDPI
- Classification
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Wirtschaft
Forecasting Models; Simulation Methods
Large Data Sets: Modeling and Analysis
Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
Economic Methodology
Econometric and Statistical Methods: Special Topics: General
- Subject
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failure prediction
relief algorithm
machine learning
support vector machine
kernel function
- Event
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Geistige Schöpfung
- (who)
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Shrivastava, Santosh Kumar
Ramudu, P. Janaki
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2020
- DOI
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doi:10.3390/risks8020052
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Shrivastava, Santosh Kumar
- Ramudu, P. Janaki
- MDPI
Time of origin
- 2020
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