Abstract
Classification of whether recovery of non-performing loans (NPL) is zero or positive is not only important in management of non-performing loans, but also is essential for estimating recovery rate and implementing the new Basel Capital Accord. Based on the largest database of NPL's recovering information in China, this paper tries to establish discriminant models to predict the loan with zero recovery. We first use Step-wise discrimination method to select variables; then give an in-depth analysis on why the selected variables are important factors influencing whether a loan is zero or positive recovery rate. Using the selected variables, we establish two-type discriminant models to classify the NPLs. Empirical results show that both models achieve high prediction accuracy, and the characteristics of obligors are the most important factors in determining whether a NPL is positively recovered or zero recovered.
Highlights
Credit risk is the major risk commercial banks are faced with, measurement and management of credit risk is the core task of risk management
We find that there are significant differences between the NPLs with recovery and the NPLs without recovery, which are shown in business situation, five-category classifications for loans, transfer mechanism of loan, collateral, industry, and region, respectively
The business situation of obligors and the quality of loans play a major role in classification
Summary
Credit risk is the major risk commercial banks are faced with, measurement and management of credit risk is the core task of risk management. He thought that data was the basis for modeling LGD and suggested that in order to make up for deficiency in data, the central bank should take the lead to establish the joint NPL database. This classification is a part of estimating LGD, more importantly if the recovery of an NPL can be determined to be zero or positive, the information can help commercial banks and AMCs to manage and dispose NPLs as they can allocate more resources to assets with positive recovery and avoid wasting money and time on loans with zero recovery, which help reduce financial cost and improve management efficiency.
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