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Managing Credit Risk: Assessing the Probability of Corporate Bankruptcy using Quantitative Risk Analysis

Granholm, David and Goumas, Theodoros (2007)
Department of Business Administration
Abstract
Managing credit risk might be the single most important business area for any commercial bank. The assessment of "good" and "bad" corporate clients is a important task for a creditor. A bad debtor is a corporate client with hardships in meeting the continous claims (interest payments) that a creditor requires. One way of evaluating or separating a "bad" client from a "good" client is to assess the propensity for the client to file for bankruptcy. This thesis examines 226 firms in the Swedsh market in the quest of predicting corporate bankruptcy. Three quantitative models are used: (i) Discriminant Analysis; (ii) Logistic Regression and; (iii) Neural Networks. Our results show that we are able to predict bankruptcy to up to 87%. Further,... (More)
Managing credit risk might be the single most important business area for any commercial bank. The assessment of "good" and "bad" corporate clients is a important task for a creditor. A bad debtor is a corporate client with hardships in meeting the continous claims (interest payments) that a creditor requires. One way of evaluating or separating a "bad" client from a "good" client is to assess the propensity for the client to file for bankruptcy. This thesis examines 226 firms in the Swedsh market in the quest of predicting corporate bankruptcy. Three quantitative models are used: (i) Discriminant Analysis; (ii) Logistic Regression and; (iii) Neural Networks. Our results show that we are able to predict bankruptcy to up to 87%. Further, the best model for predicting corporate bankurptcy in our study is logistic regression. (Less)
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author
Granholm, David and Goumas, Theodoros
supervisor
organization
year
type
H1 - Master's Degree (One Year)
subject
keywords
Credit risk, bankruptcy, bankruptcy prediction, financial ratios, discriminant analysis, logistic regression, neural networks, Management of enterprises, Företagsledning, management
language
Swedish
id
1351329
date added to LUP
2007-06-04 00:00:00
date last changed
2012-04-02 16:33:26
@misc{1351329,
  abstract     = {Managing credit risk might be the single most important business area for any commercial bank. The assessment of "good" and "bad" corporate clients is a important task for a creditor. A bad debtor is a corporate client with hardships in meeting the continous claims (interest payments) that a creditor requires. One way of evaluating or separating a "bad" client from a "good" client is to assess the propensity for the client to file for bankruptcy. This thesis examines 226 firms in the Swedsh market in the quest of predicting corporate bankruptcy. Three quantitative models are used: (i) Discriminant Analysis; (ii) Logistic Regression and; (iii) Neural Networks. Our results show that we are able to predict bankruptcy to up to 87%. Further, the best model for predicting corporate bankurptcy in our study is logistic regression.},
  author       = {Granholm, David and Goumas, Theodoros},
  keyword      = {Credit risk,bankruptcy,bankruptcy prediction,financial ratios,discriminant analysis,logistic regression,neural networks,Management of enterprises,Företagsledning, management},
  language     = {swe},
  note         = {Student Paper},
  title        = {Managing Credit Risk: Assessing the Probability of Corporate Bankruptcy using Quantitative Risk Analysis},
  year         = {2007},
}