We shed light on computational challenges when fitting the Nelson-Siegel, Bliss and Svensson parsimonious yield curve models to observed US Treasury securities with maturities up to 30 years. As model parameters have a specific financial meaning, the stability of their estimated values over time becomes relevant when their dynamic behavior is interpreted in risk-return models. Our study is the first in the literature that compares the stability of estimated model parameters among different parsimonious models and for different approaches for predefining initial parameter values. We find that the Nelson-Siegel parameter estimates are more stable and conserve their intrinsic economical interpretation. Results reveal in addition the patterns of confounding effects in the Svensson model. To obtain the most stable and intuitive parameter estimates over time, we recommend the use of the Nelson-Siegel model by taking initial parameter values derived from the observed yields. The implications of excluding Treasury bills, constraining parameters and reducing clusters across time to maturity are also investigated.

The purpose of our paper is to investigate whether any differences between International Financial Reporting Standards (IFRS) and local Generally Accepted Accounting Principles (GAAP) impact the transparency of financial reporting of non-listed companies through bankruptcy prediction. This contributes to extant research that has focused on the effects of IFRS adoption in the context of listed companies. For our investigation, we used logistic regression, well-established accounting-based predictors, and a sample of financial statements from privately held Swedish companies using IFRS, and Norwegian companies using Norwegian GAAP. The results indicate that financial statements made under IFRS may be better suited for bankruptcy prediction than those made under Norwegian GAAP. Our findings suggest that the use of IFRS could aid in increasing the informativeness of financial reports by promoting transparency and prevent managers of firms facing insolvency from engaging in creative accounting practices. Our results should, however, be applied with caution, as they may be due to the differences in characteristics across firms that are not captured by our research design. We leave this issue open to future research.

We utilize machine learning methods to model the credit risk of mortgages in a significant emerging market. For this purpose, we investigate a multitude of variables that explain the characteristics of the loans, the demographics of the borrowers, and macroeconomic factors. We employ SHapley Additive exPlanations (SHAP) values in conjunction with five different tree-based machine learning methods, as well as the least absolute shrinkage and selection operator (LASSO) in conjunction with logistic regressions. Our findings, which are robust across two sampling schemes, reveal that while demographic variables are significant and important, loan-specific and macroeconomic variables are the most crucial in explaining mortgage defaults. As existing literature on mortgage default has primarily focused on advanced markets, we aim to bridge this gap by concentrating on emerging market data. We also share our code, which we hope will encourage others to utilize the methods we have applied.

We test alternative feature selection methods for bankruptcy prediction and illustrate their superiority versus popular models used in the literature. We apply these methods to a comprehensive dataset of more than 1.8 million financial statements covering the entire universe of privately held Norwegian SMEs in 2006–2020. We find that input variables chosen by an embedded least absolute shrinkage and selection operator (LASSO) method yield the best in-sample fit, out-of-sample performance, and stability, robust across different time periods and estimation techniques. In a real-world simulation of a competitive credit market, even small differences in model performance translate into large differences in bank profitability, with LASSO outperforming all alternatives. Finally, contrasting bankruptcy predictors for SMEs with those for larger firms reveals economically meaningful differences consistent with theory: leverage and liquidity dominate for SMEs while profitability matters more for larger firms, reflecting SMEs’ higher refinancing risk and limited access to external financing. Predictors tailored specifically to SMEs yield superior prediction performance and higher bank profitability than those derived from larger firms.