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Key variables and characteristics of loan loss given default : empirical evidence from 28 provinces in China

- Zhao, Zhichong, Colombage, Sisira, Chi, Guotai

  • Authors: Zhao, Zhichong , Colombage, Sisira , Chi, Guotai
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Emerging Markets Finance and Trade Vol. 56, no. 11 (2020), p. 2443-2460
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  • Description: This article empirically investigates the impact of key variables and characteristics on loan loss given default (LGD) of small farmers using data from 28 provinces in China. The default feature of loans is not only a financial issue and a risk management issue but also an exploration of the loan customers’ default rule. In this study, the key variables were selected using an F-test to identify which ones are critical in credit risk management. Then, we use a t-test to obtain the significant characteristics with an impact on LGD. We found that the 30-35-year-old age group, those living in houses with shared ownership, households with two to four workers, and those whose ratio of annual net income to GDP per capita is between 10 and 20 tend to have higher LGD. These results inform bank lenders and policymakers of the most significant factors that influence loan loss default. ©, Copyright © Taylor & Francis Group, LLC.

Credit scoring model based on a novel group feature selection method : the case of Chinese small-sized manufacturing enterprises

- Zhang, Zhipeng, Chi, Guotai, Colombage, Sisira, Zhou, Ying

  • Authors: Zhang, Zhipeng , Chi, Guotai , Colombage, Sisira , Zhou, Ying
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Journal of the Operational Research Society Vol. 73, no. 1 (2022), p. 122-138
  • Full Text: false
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  • Description: In building a predictive credit scoring model, feature selection is an essential pre-processing step that can improve the predictive accuracy and comprehensibility of models. In this study, we select the optimal feature subset based on group feature selection in lieu of the individual feature selection method, to establish a credit scoring model for small manufacturing enterprises. In our methodology, we first select a group of features using the 0-1 programming method, with the objective function of maximising the Gini coefficient (GINI) of the credit score to identify the possibility of default. Then we introduce constraints to remove any redundant features in the same subset, provided they reflect the same information. Finally, we assign weights to different features according to the Gini coefficient, ensuring that the weight of the features reflects their discriminatory power. Our empirical results show that the selection of a set of features more effectively identifies default status than the individual feature selection approach. Moreover, a rating system with more features does not necessarily have better discriminatory power. As the number of features exceeds the optimum number of features selected, the system's discriminatory ability begins to decrease. © Operational Research Society 2022.
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