Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.652665
Title: Developing retail performance measurement and financial distress prediction systems by using credit scoring techniques
Author: Hu, Y.-C.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2007
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Abstract:
The current research develops a theoretical framework based on the Resource-Advantage Theory of Competition (Hunt, 2000) for the selection of appropriate variables. Using a review of the literature as well as interviews and a survey, 170 potential retail performance variables were identified as possible for inclusion in the model. To produce a relatively simple model with the aim of avoiding over-fitting a limited number of key variables or principal components were selected to predict default. Five credit-scoring techniques: Naïve Bayes, Logistic Regression, Recursive Partitioning, Artificial Neural Network and Sequential Minimal Optimization (SMO) were employed on a sample of 195 healthy and 51 distressed businesses from the USA over five periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001 and 1998-2002. Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability in the year before financial distress. However it is difficult to conclude which modelling technique has the highest classification ability uniformly, since model performance varied in terms of different time scales. The analysis also showed that external environmental influence does impact on default assessment for all five credit-scoring techniques, but these influences are weak. These findings indicate that the developed models are theoretically sound. There is however, a need to compare their performance to other approaches. First, rankings from the study were compared with those from a standard rating system – the well established Moody’s Credit Rating. It is assumed that the higher the degree of similarity between the two sets of rankings, the greater the credibility of the prediction model. The results indicated that the logistic regression model and the SMO model were the most comparable with Moody’s. Secondly, the model’s performance was assessed by applying it to different geographical areas. The USA model was therefore applied to European and Japanese markets. Results indicated that all market models displayed similar discriminating ability one year prior to financial distress. However, the USA model performed relatively better than European and Japanese models five years before financial distress. This implied that a financial distress model has potentially better prediction ability when based on a single market. It was decided to explore the performance of a generic global model, since model construction is time-consuming and costly. A composite model was constructed by combining data from USA, European and Japanese markets. This composite model had sound prediction performance, even up to five years before financial distress, as the accuracy rate was above 85% and AUROC value was above 0.72. Comparing with the original USA model, the composite model has similar prediction performance in terms of the accuracy rate. However, the composite model presented a worse prediction utility based on the AUROC value.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.652665  DOI: Not available
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