Research On Credit Risk Measurement Of Listed Companies

This paper proposes an empirical back-testing framework based on panel data,and a quality appraisal system of credit risk measurement with ROC(Receiver Operating Characteristic)as core index,which covers consistency,stability and timeliness.Based on the back-test framework and quality evaluation system,taking the a-share listed companies in China from 2000 to 2019 as research samples,setting forecast periods as both 1 year and 2 years,using both labels of credit risk—— "ST" and "default",this paper compares the quality of credit risk measurement of KMV model,data mining technology and credit rating.This paper comprehensively discusses the similarities and differences between "ST" and "default",and creatively use "ST" to predict "default" : train machine learning model by sufficient ST sample to predict default samples which is relatively scarce,solve the difficulty in directly modeling on default sample because of its current insufficient numbers and occurring centrally in time.The results show that the prediction effect of the model trained with "ST" based on data mining technology is significantly better than that of credit rating,and it is feasible to predict "default" with "ST".In the empirical study of KMV model,this paper explores the influence of different input variables and default distance(DD)forms on the prediction effect of ST,and finds that the KMV-Merton model with default point as short-term liability,volatility as static volatility,and excess return of individual stocks as the expected return on the firm's assets has better prediction effect than other models.Moreover,the DD of KMV model has good accuracy and stability from the cross-sectional data(year by year),while the accuracy based on the panel data decreases significantly.The reason for the above is that the DD varies too dramatically with time,and as an estimate of the expected growth rate of assets,the excess return rate is "pro-cyclical",which further aggravates the volatility of the DD;Therefore,the DD becomes less comparable across time.As for default prediction,the results show that the ROC of KMV model is around 0.5,and it fails to identify default samples with "high market value,high leverage,high growth and low equity volatility".In the empirical research based on data mining technology,this paper explores the influence of data preprocessing,feature selection and model selection on predicting.In terms of data preprocessing,data discretization has more effect on model promotion than model selection;In the aspect of feature selection,feature selection based on IV value and “expert feature” have advantages respectively;compared with financial data,market data can provide additional information about credit risk during the 1-year forecast period,thus improving model performance;among which market value and excess returns play a major role,the contribution of DD is not significant,and the improvement is reduced with the increase of the forecast period.In terms of model selection,the performance of logistic regression and SVM model based on linear kernel is better than that of random forest model.However,when the forecast period is 1 year,there is not much difference between them.