Technical paper/Credit risk
Soft information in financial distress prediction: evidence of textual features in annual reports from Chinese listed companies
The authors use textual data in a model to predict financial distress, demonstrating that this can enhance prediction outcome versus traditional financial data alone.
Litigation risk assessment: a novel quantitative recency–frequency–monetary model
The authors assess litigation risk and credit risk of companies and investigate interrelationships between these risks, finding a correlation between them.
Backtesting correlated quantities
A technique to decorrelate samples and reach higher discriminatory power is presented
Consumer credit card payment dynamics over the economic cycle
This papers uses data from 1.8 million credit card accounts to investigate how consumers revolve credit card debt and the impact of this on default risk.
Weighting for leverage
A credit exposure model for leveraged collateralised counterparties is presented
Key indicators for the credit risk evaluation of clients and their changing characteristics
The authors propose a credit risk evaluation model for energy performance contracting projects with debt- paying ability and long-term capital debt ratio as optimal indicators.
Financial distress prediction with optimal decision trees based on the optimal sampling probability
The authors propose and validate a tree-based ensemble model for financial distress prediction which is demonstrated to outperform comparative models.
Default prediction based on a locally weighted dynamic ensemble model for imbalanced data
The authors put forward a locally weighted dynamic ensemble model which can predict financial institutions' default statues five years ahed.
Credit risk management: a systematic literature review and bibliometric analysis
The authors undertake a literature review and bibliometric analysis of 774 credit risk research papers.
Characteristics of student loan credit recovery: evidence from a micro-level data set
The authors investigate delinquent student loans, identifying factors which influence the likelihood of recovery and proposing means to improve student loan credit recovery rates.
Multi-factor default correlation model estimation: enhancement with bootstrapping
The authors propose using a three-factor Merton model to allow more accurate quantification when investigating the credit risk of portfolios.
Credit contagion risk in German auto loans
The authors employ a data set of over 5 million German auto loans to investigate credit contagion risk and show that defaults cannot be attributed to single factors.
Leveraged wrong-way risk
A model to assess the exposure to leveraged and collateralised counterparties is presented
Integrated stock–bond portfolio management
The authors put forward a stock-bond portfolio selection model which is based on CreditMetrics principles in which market and credit risks are naturally integrated.
Estimating the impact of climate change on credit risk
The author investigates the relationship between climate change and credit risk characteristics of individual obligors and portfolios of credit obligations.
Understanding and predicting systemic corporate distress: a machine-learning approach
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
The impact of treasury operations and off-balance-sheet credit business on commercial bank credit risk
Using a vine copula, he authors demonstrate that global systemically important banks face lower credit risk using data from commercial banks based on three risk factors.
Benchmarking machine learning models to predict corporate bankruptcy
Based on a comprehensive sample, the authors benchmark machine learning models in the prediction of financial distress of publicly traded US firms, with gradient-boosted tress outperforming other models in one-year-ahead forecasts.
A modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting
This paper proposes the chi-squared with recursive feature elimination method: a means of feature-selection which aims to improve classification performance using fewer features.
Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates
The authors put forward a method using a support vector machine to enhance the exploration of nonlinear covariate effects if SMEs never default while also considering time-varying and fixed covariates for the incidence and latency of an event.