Bankruptcy Prediction and Stress Quantification Using Support Vector Machine: Evidence from Indian Banks

Clicks: 207
ID: 114508
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
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.
Reference Key
shrivastav2020risksbankruptcy Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Santosh Kumar Shrivastav;P. Janaki Ramudu;Shrivastav, Santosh Kumar;Ramudu, P. Janaki;
Journal risks
Year 2020
DOI
10.3390/risks8020052
URL
https://www.mdpi.com/2227-9091/8/2/52
Keywords
Machine learning support vector machine failure prediction relief algorithm kernel function Machine learning support vector machine failure prediction relief algorithm kernel function

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.