The Default Risk of Firms Examined with Smooth Support Vector
نویسندگان
چکیده
In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank’s objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.
منابع مشابه
The Default Risk of Firms Examined with Smooth Support Vector Machines
In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank’s objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate ...
متن کاملComparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black–Scholes–Merton Model
Default risk is one of the most important types of risks, and credit default swap (CDS) is one of the most effective financial instruments to cover such risks. The lack of these instruments may reduce investment attraction, particularly for international investors, and impose potential losses on the economy of the countries lacking such financial instruments, among them, Iran. After the 2007 fi...
متن کاملCredit Risk Predictive Ability of G-ZPP Model Versus V-ZPP Model
Credit risk management is becoming more and more important in recent years. When a company deals with a financial problem, it may not be able to fulfill its financial obligations, which can cause direct and indirect financial losses to shareholders, creditors, investors and other people in the community. Advanced credit risk models that are based on market value include improving credit quality...
متن کاملSupport Vector Machines with Evolutionary Feature Selection for Default Prediction
Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client’s degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database. We use classical methods such as discriminan analysis (DA), logit and probit models as benchmark O...
متن کاملEstimation of Default Probabilities with Support Vector Machines
Predicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The res...
متن کامل