Decision making on creditworthiness, using a fuzzy connectionist model
Fuzzy Sets and Systems
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Credit Scoring and Its Applications
Credit Scoring and Its Applications
Investment using technical analysis and fuzzy logic
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
A fuzzy neural network for pattern classification and feature selection
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers
Expert Systems with Applications: An International Journal
Fuzzy risk analysis based on interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers
Expert Systems with Applications: An International Journal
Analyzing fuzzy risk based on similarity measures between interval-valued fuzzy numbers
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Employing pooled data of 3344 listed firms from seven Asia-Pacific countries, this is the first empirical study to classify and predict trade credit risks in the international trade context. In addition, this paper extends previous work by applying receiver operating characteristic (ROC) curve analysis to compare the model performance of Logit to that of Fuzzy Logic (FL). We are unaware of any other paper that has discussed the application of ROC curve analysis in the business and finance literature. The results show that FL exceeds Logit in terms of overall classification accuracy and prediction accuracy. However, by incorporating measurement in the form of ROC curves, Logit is proven to outperform FL in classifying non-default firms. This suggests that though FL is superior in overall accuracy and in classifying default firms, Logit is preferable in situations where higher accuracy in classifying non-default firms is preferred. The stability of the models is also demonstrated.