scholarly journals ENTERPRISE CREDIT RISK ASSESSMENT ANALYZING THE DATA OF SHORT TERM ACTIVITY PERIOD

2012 ◽  
Vol 8 (1) ◽  
pp. 158-164
Author(s):  
Ričardas Mileris
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Regression Tree ◽  
Probit Model ◽  
Financial Data ◽  
Classification And Regression Tree ◽  
Activity Period ◽  
Short Term ◽  
Credit Risk Assessment ◽  
Complementary Analysis

This research investigates the possibility to classify the companies into default and non-default groups analyzing the financial data of 1 year. The developed statistical model enables banks to predict the default of new companies that have no sufficient financial information for the credit risk assessment using other models. The classification and regression tree predicts the default of companies with the 96 % probability. The complementary analysis the financial data of 2 years by probit model allows to increase the classification accuracy to 99 %. Key words: bank, classification, credit risk, statistical analysis.

Establishing decision tree-based short-term default credit risk assessment models

2016 ◽  
Vol 45 (23) ◽  
pp. 6803-6815 ◽  
Author(s):  
Yung-Chia Chang ◽  
Kuei-Hu Chang ◽  
Heng-Hsuan Chu ◽  
Lee-Ing Tong
Keyword(s):  
Risk Assessment ◽  
Decision Tree ◽  
Credit Risk ◽  
Short Term ◽  
Credit Risk Assessment ◽  
Risk Assessment Models

scholarly journals A random thinning model with a latent factor for improvement of top-down credit risk assessment

JSIAM Letters ◽  
2016 ◽  
Vol 8 (0) ◽  
pp. 37-40 ◽  
Author(s):  
Suguru Yamanaka ◽  
Hidetoshi Nakagawa ◽  
Masaaki Sugihara
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Top Down ◽  
Latent Factor ◽  
Credit Risk Assessment ◽  
Random Thinning
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