Bankruptcy prediction and credit evaluation for small and medium size enterprises : a neural networks-expert system hybrid approach

Pao Yue-kong Library Electronic Theses Database

Bankruptcy prediction and credit evaluation for small and medium size enterprises : a neural networks-expert system hybrid approach

 

Author: Tsang, Shu-fung
Title: Bankruptcy prediction and credit evaluation for small and medium size enterprises : a neural networks-expert system hybrid approach
Degree: M.Sc.
Year: 1998
Subject: Hong Kong Polytechnic University -- Dissertations
Small business -- China -- Hong Kong
Bankruptcy -- Forecasting -- Mathematical models
Business failures -- Forecasting -- Mathematical models
Credit ratings -- China -- Hong Kong
Neural networks (Computer science) -- Mathematical models
Department: Dept. of Computing
Pages: 153, [50] leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1439156
URI: http://theses.lib.polyu.edu.hk/handle/200/4883
Abstract: Bankruptcy has long been an issue that arouse great concerns. Altman is one of the pioneers in the study of bankruptcy prediction. He has developed the Z-score model and the Zeta model which are widely adopted by financial institutions for evaluation of financial risk. Altman employed multiple discriminant analysis (MDA) in development of the models. Although Altman's models attain quite good prediction accuracy, the models are not targeted for environment as Hong Kong where significant proportion of the companies are small and medium size enterprises (SMEs). After the introduction of neural networks (NN), many researches have been conducted which aimed at using NN to obtain a better solution. The objective of this study are to develop a NN bankruptcy prediction model for SMEs in Hong Kong. The NN model so developed will be compared with the Altman's models and the MDA result. Finally, a front end expert system prototype for the support of loan application for financial institutions will be developed.

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