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We study how capital requirements, intended as a measure to ensure security for the financial system, can create moral hazard for banks in dealing with distressed debts.

Over the period spanning from 1993 to 2019, we manually gathered data on 1953 firms, identifying a total of 2,146 distress events, with 804 instances resulting in bankruptcy fillings.

Our analyses at the loan level and the bank level consistently show that loans of distressed firms are much more likely to be extended when the lenders are closer to the capital requirement limit. Exploiting the discontinuity in the predetermined maturity date of loans, we provide causal evidence on the relationship between capital ratios and extension likelihood. Distressed loans that are due just before the report date (end of a quarter) are much more likely to be extended than loans due just after the report date, after controlling for loan and firm characteristics. Additional analyses show that the effects are stronger when external financing is more costly and when the banks are poorly capitalized.

Our paper presents the first causal evidence of capital requirements on lending distortion, contributing to our understanding of the dynamics within the banking sector and providing policy implications for promoting financial stability and regulatory efficacy.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.