Optimal Loan Pricing for Agricultural Supply Chains from a Green Credit Perspective

In recent years, many countries have proposed various sustainable development strategies around environmental issues. The implementation of green supply chain management is an effective sustainable development approach that combines “environmental awareness” and “economic development.” Therefore, introducing the concept of “green” effectively is the main direction for the sustainable development of agriculture in the future. The impacts of green credit policies on agricultural supply chains have rarely been discussed before. Therefore, we focus on the incentive mechanism of green credit policies in the agricultural supply chain. We use the Stackelberg Leadership Model to construct a pricing model which adds the interest subsidy and required reserve ratio (RRR) cuts, and determines the pricing rules of bank loans and production decisions of the farmer in the agricultural supply chain under the incentive policy of green credit by quantifying the optimization problems of the bank and the farmer. The result shows that optimal decisions exist for both farmer and bank in the supply chain game framework. The implementation of the green credit policies contributes to both of their profits. Additionally, the green credit policies give the bank room to reduce interest rates so that the overall utility level of the supply chain could be improved.

Monetary Policy Pass-through, Ownership and Crisis: How Robust is the Indian Evidence?

Employing disaggregated data for 2001–2016, this study investigates the lending and loan pricing behaviour of state-owned and domestic private banks in response to monetary policy. Three major findings emerge. First, although both the interest rate and the bank lending channels are relevant for monetary pass-through, there is a trade-off: the impact of the former is much higher than the latter, although it occurs with a significant lag. Second, domestic private banks have a far greater response to a monetary policy shock under the interest rate channel, whereas state-owned banks display a greater response under the bank-lending channel. And finally, state-owned banks cut back lending during periods of crises, although no such response is manifest in domestic private banks. JEL Codes: C23, D4, E43, E52, G21, L10

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

Small- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

Confidentiality in loan credit agreements

PurposeThis study has two primary objectives. First, it analyzes the information content of confidentiality strictness in corporate loan credit agreements. Second, it examines how confidentiality strictness impacts covenant design, lending syndicate structure and loan pricing.Design/methodology/approachUsing a sample of 6,327 loan credit agreements originated by US public firms in the period of 1996–2017, this study measures the confidentiality strictness in loan contracts using textual analyses that capture the appearance of confidentiality-related words and the length of confidentiality provision. All regressions include relevant loan characteristics, firm-specific accounting variables, industry and year fixed effects. To address the endogeneity concern, the paper uses borrowing firms' rival cash holdings and R&D expenditures to instrument for confidentiality strictness in two-staged least square regressions.FindingsBorrowers which have higher R&D and operate in more competitive product markets have tighter confidentiality policies. Furthermore, this study reveals that confidentiality strictness is negatively associated with the imposition of financial covenants, especially performance covenants. Loan contracts for borrowers with stricter confidentiality on average have more relaxed covenant intensity, measured by the number of covenants. The study also shows that stricter confidentiality attracts finance companies, which have strong expertise in product markets of their parent firms, into the lending syndicate. However, confidentiality-conscious borrowers with higher degree of information asymmetry are subject to higher loan spreads.Originality/valueThis study provides the first examination of confidentiality policies in loan contracts and supports the idea that loan provisions are not simply made of “boilerplate” language. The results suggest that, for confidentiality-sensitive borrowers, the greater exposure to product market competition helps control managerial slack and substitute monitoring from financial markets.

Is Economic Uncertainty a Risk Factor in Bank Loan Pricing Decisions? International Evidence

Uncertainty in economic environment leads economic agents to act cautiously. In this paper, we postulate that such uncertainty leads banks to charge higher interest rate on loans. Measuring aggregate country-level economic uncertainty with the World Uncertainty Index (WUI) and using a bank-level dataset from 88 countries over the period 1998–2017, we find that heightened economic uncertainty increases bank loan interest rates. Specifically, bank loan interest rates rise by 20.67 basis points with a one standard deviation increase in WUI. Our results are robust when we use alternative proxy of uncertainty, include additional controls in the model, and extend the sample size. We also observe that WUI index is better at measuring local economic uncertainty as compared to the Economic Policy Uncertainty (EPU) index. Overall, this study provides evidence that bank price in economic uncertainty is an important risk while setting interest rates on bank loans.