Purpose: In theory, the impact of debt liquidity risk (DLR) on the firm's future growth is ambiguous. This study aims to examine the empirical relationship between the DLR and firms' growth rate using annual data for USA companies from 1976 to 2020.Design/methodology/approach: Given the longitudinal nature of the data, the author uses OLS (ordinary least squares) regression methodology with fixed effects to control for unobserved characteristics that might affect the dependent variable. Instrument variable regression is also used to address the potential endogeneity problem.Findings: The results show that firms having higher DLR, as proxied by more short-term debt, experience lower growth rate. An increase in firms' short-term debt decreases the firms' future growth rate as evidenced by lower assets, revenue and employee growth rate. Moreover, the authors' results show that small firms or firms with more investment opportunities grow fast if the firms take higher DLR. Finally, cyclical firms with higher DLR exhibit lower growth rate during the credit tighten period. The authors' results hold for both the pre-zero lower bound (ZLB) era and ZLB period.Originality/value: To the authors' best knowledge, this is one of the earliest studies to carefully examine the effects of DLR on firms' growth rate. While prior research finds that firms with higher growth potential, measured by market-to-book (MTB) ratio, use more short-term debt, the authors' research directly addresses whether DLR affects firms' future growth rate. The authors’ findings also help explain why firms with high growth potential use more short-term debt.

This study examines racial and ethnic disparities in mortgage-lending outcomes across different lender types—large banks, fintech lenders, non-bank lenders, small banks, and credit unions—using Home Mortgage Disclosure Act (HMDA) data from 2018 to 2023. By analyzing approval rates, rate spreads, and origination charges, we evaluate how borrower outcomes vary by race and ethnicity, controlling for loan characteristics, borrower attributes, and regional factors. Our findings reveal that Black and Hispanic borrowers consistently face less favorable terms than White borrowers, with disparities differing by lender type. Large banks, operating under stringent regulatory oversight, demonstrate relatively equitable pricing but impose higher loan denial rates on minorities. Credit unions, despite offering the lowest rate spreads overall, penalize minority borrowers more severely in pricing than other lender types. Fintech lenders, while charging higher-rate spreads and fees, show smaller credit access disparities for minority borrowers. Non-bank and small banks display mixed results, with inconsistencies in their treatment of minorities across pricing and credit access. These results highlight that neither technological innovations nor alternative lending models alone suffice to eliminate systemic inequities. Achieving equitable mortgage lending requires enhanced regulatory oversight, greater transparency in algorithmic decision-making, and stricter enforcement of fair lending practices.

This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), followed closely by banks (0.857), with fintech lenders trailing (0.852). In pricing analysis, banks adjust the origination rates most sharply with borrower risk (7.20 basis points per percentage-point increase in default probability) compared to fintech (4.18 bp) and non-fintech lenders (5.43 bp). Fintechs underprice 32% of high-risk loans, highlighting limited incentive alignment under GSE securitization structures. Expanding the allowable alternative data and modest risk-retention policies could enhance fintechs’ analytical effectiveness in mortgage markets.

Cost–volume–profit (CVP) analysis is a widely used decision tool across many business disciplines. The current literature on stochastic applications of the CVP model is limited in that the model is studied under the restrictive forms of the Gaussian and Lognormal distributions. In this paper we introduce the Mellin Transform as a methodology to generalize stochastic modeling of the CVP problem. We demonstrate the versatility of using the Mellin transform to model the CVP problem, and present a generalization of the CVP model when the contribution margin and sales volume are both defined by continuous random distributions.

The authors apply machine learning techniques to Loss Given Default estimation, identifying key variables in LGD prediction and evaluating the performance of

Purpose: The unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time.Design/methodology/approach: This study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research.Findings: The authors decompose UR into two components – temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling.Originality/value: The findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.