Measuring retail company performance using credit scoring techniques

This paper proposes a theoretical framework for predicting financial distress based on Hunt’s (2000) Resource-Advantage Theory of Competition. The study focuses on the US retail market. Five credit scoring methodologies: Naïve Bayes, Logistic Regression, Recursive Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO), are used on a sample of 195 healthy companies and 51 distressed firms over five time periods from 1994 to 2002. Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability in the time period of one year before financial distress. Moreover, the methodologies remain sound even five years prior to financial distress with classification accuracy rates above 80% and AUROC values above 0.80. However, it is difficult to conclude that which modelling methodology has the absolute best classification ability, since the model’s performance varies in terms of different time scales and different v...

• Type: Journal
• Year: 2007
• Where: EOR
• Authors: Yu-Chiang Hu, Jake Ansell
• Keywords: Credit Scoring Methodologies, EOR 2007, Financial Distress, Logistic Regression