Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Management | en_US |
| dc.creator | Wong, Chor-heung Verena | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/2350 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Credit risk management in financial institutions | en_US |
| dcterms.abstract | Credit risk management becoming significantly important to financial institutions during the current Asian financial crisis. Being able to form highly reliable early forecasts of the future health of companies is critically important to bank lending officers. One of the widely adopted model in the United States to evaluate the financial health of a company is the Z-score developed by Edward I. Altman thirty years before. It was found that the model is not so reliable by computing our selected samples as overall forecast error was over 50%. If Z-score had to used to predict Hong Kong companies, coefficient used had to adjust. This study compares the predictive capabilities for loan classification of neural networks and classical multivariate discriminant analysis Z-score. This study found that neural network performs significantly better than the Z-score at loan classification. Forecasting error on all case is below 10%. | en_US |
| dcterms.extent | v, 91, [71] leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1999 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.B.A. | en_US |
| dcterms.LCSH | Bank management | en_US |
| dcterms.LCSH | Risk management | en_US |
| dcterms.LCSH | Neural networks (Computer science) -- Design and construction | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14718637.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 4.86 MB | Adobe PDF | View/Open |
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