Corporate credit rating analysis has attracted lots of research interests in the literature. Rece... more Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.
The migration approach to credit risk measurement is based on historic rates of movements of indi... more The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender's risk-rating or creditscoring system. This article applies the migration concept to farm-level data from Illinois to estimate migration rates for a farmer's credit score and other performance measures under different time-averaging approaches. Empirical results suggest greater stability in rating migrations for longer time-averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.
Credit risk analysis is an important topic in the financial risk management. Due to recent financ... more Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.
Corporate credit rating analysis has attracted lots of research interests in the literature. Rece... more Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.
The migration approach to credit risk measurement is based on historic rates of movements of indi... more The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender's risk-rating or creditscoring system. This article applies the migration concept to farm-level data from Illinois to estimate migration rates for a farmer's credit score and other performance measures under different time-averaging approaches. Empirical results suggest greater stability in rating migrations for longer time-averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.
Credit risk analysis is an important topic in the financial risk management. Due to recent financ... more Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.
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Papers by varun Satish