Author:
Wang Zhenlei,Qin Song, , ,
Abstract
The balance of non-performing loans (NPLs) of Chinese banking financial institutions rebounded for the first time after 2005, and credit risk has emerged as one of the rapidly rising risks in today’s financial markets. In this study, we focus on the NPLs of financial institutions. In particular, the factors affecting their rate are studied. A dynamic control theory model is used to set up a Hamilton function for describing the effect of these factors. Moreover, the path of NPLs under the influence of various factors is obtained. It was found that improved risk control and macroeconomy factors reduced the number of NPLs. In particular, to reduce the number of NPLs, the capacity of banks to manage loans should be strengthened.
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference24 articles.
1. B. Aver, “An Empirical Analysis of Credit Risk Factors of the Slovenian Banking System,” Managing Global Transitions, Vol.6, pp. 317-334, 2008.
2. I. Bucur and S. Dragomirescu, “The Influence of Macroeconomic Conditions on Credit Risk: Case of Romanian Banking System,” Studies and Scientific Researches, Economics Edition, No.19, 2014.
3. M. D. Crouhy, D. Galai, and R. Mark, “A Comparative Analysis of Current Credit Risk Models,” J. of Banking and Finance, Vol.24, pp. 59-117, 2000.
4. V. Castro, “Macroeconomic determinants of the credit risk in the banking system: the case of GIPSI,” Economic Modeling, Vol.31, pp. 672-683, 2013.
5. A. Das and S. Ghosh, “Determinants of Credit Risk in Indian State-owned Banks: An Empirical Investigation,” MPRA Paper, No.17301, 2007.