Title :
Building Default Predicting Model on Firm´s Short-term Loan Data with Artificial Neural Network - Considering Qualitative Indexes and Misclassification Costs
Author :
Ruowei, Ma ; Deyong, Yang
Author_Institution :
Sch. of Econ., Beijing Technol. & Bus. Univ., Beijing, China
Abstract :
To date, using model to predict whether firm´s default is still a problem. It presents: a. most model using pairwise pattern; b. lack of qualitative indexes that affect firm´s default; c. asymmetric between normal firm´s misclassification costs and default firm´s. So, introducing qualitative indexes, using all samples and considering misclassification costs, this paper builds an artificial neural network model on short-term-loan data. Though training, validating and testing, it´s performance is good.
Keywords :
bank data processing; neural nets; pattern classification; statistical analysis; artificial neural network; default predicting model; misclassification cost; pairwise pattern; qualitative index; short term loan data; Artificial neural networks; Banking; Biological system modeling; Data models; Electronic countermeasures; Indexes; Mathematical model; artificial neural network; misclassification costs; qualitative indexes;
Conference_Titel :
E-Business and E-Government (ICEE), 2010 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3997-3
DOI :
10.1109/ICEE.2010.946