Search Results (22)

In this paper, we analyzed the role of credit consortia in supporting SMEs of the Italian region of Sardinia around and during the SARS-CoV-2 pandemic crisis. Credit consortia (or credit guarantee schemes) are financial companies whose institutional role is to support small firms needing bank lending who are individually weak in the bank–firm relationship. Credit consortia are particularly relevant in Italy, where they mitigate credit restrictions for SMEs by supplying guarantees to the bank, allowing for partial coverage of potential losses, providing peer-monitoring activity, and collectively negotiating more favorable interest rates and other conditions with banks. During the SARS-CoV-2 pandemic, credit consortia had a crucial role in supporting Sardinian SMEs with guarantees and obtaining government financial support. The evolution of Sardinian companies’ financial structures during the SARS-CoV-2 pandemic shows that the confidi-supported firms have low capitalization and are financially fragile yet capable of good returns. The liquidity provided by the government during the pandemic loosened these constraints, boosting the available liquidity, which translated, in short, into higher investment and higher sales. The demographics of Sardinian companies in 2019–2022 and the volumes of loans and savings showed a strengthening of debt capital payments, increased collections, and a progressive improvement of the Sardinian companies’ net financial positions. Full article

Despite the MFI insurgency, agricultural financing remains critically low, even though microcredit is widely accepted as both a substitute and compliment to formal credit. Zimbabwe is an agro-based economy and very little is known about the determinants of microcredit demand and microcredit size in smallholder resettled sugarcane farmers. Research is concentrated in short-term agriculture activities. Thus, this study aims to fill the unattended gap in lagged returns agriculture activities such as sugarcane production which takes at least a year to mature, hence, the greater need for agriculture financing alternatives such as microfinance. The study examined the determinants of both microcredit demand and loan size (magnitude of microcredit participation) by smallholder resettled A2 sugarcane farmers in Chiredzi. Primary data from 370 smallholder resettled sugarcane farmers (214 borrower participants and 156 non-borrower participants) were used. Probit and Tobit regression models were used for data analysis in STATA. Operational costs, interest rate, grace period, and land size significantly affect both the demand for microcredit and microcredit size, while education, household farming assets, extension services, and payback period significantly affect microfinance demand, and risk attitude/perception additionally determine microcredit size. Special microfinance schemes best suitable for the agriculture sector and crop/plant-specific agriculture financing schemes, currency, and macroeconomic stability are the major policy recommendations. Full article

In the face of global climate change and the collision of consumer preferences towards green and low-carbon, businesses need to accelerate the transition to sustainable development to achieve long-term growth. Companies must raise significant funds to support this transition and manage high market risk. The existing research on green innovation within supply chains often overlooks market risks, particularly those associated with incomplete information. Hence, this paper considers a two-echelon supply chain system composed of a manufacturer and a retailer. Manufacturers are willing to carry out green innovation and make a single product for sale in the consumer market with green preferences. However, innovation is risky due to the uncertainty in the sales volume of green products. In addition, the manufacturer may lack internal capital to invest in the innovation activities and may seek external financial resources, e.g., bank loans or retail prepayment financing. Hence, the manufacturer and retailer must decide which financial option to adopt. The results show that when the market risk is high, the supply chain members tend to make conservative decisions, no matter which financial modes they choose. However, with the robust optimization approach, the manufacturer and the retailer may earn a higher profit when the market risk is high. When the prepayment rate and bank loan interest rate are equal, regardless of the market risk, the manufacturer’s optimal decision is to choose prepayment financing from the retailer. However, when the prepayment rate is higher than the bank loan interest rate, there is no dominant strategy for the manufacturer to choose. Full article

Peer-to-peer (P2P) lending has gained significant traction in the financial landscape, particularly in developing economies such as Indonesia, where access to traditional banking services remains a challenge for many. The aim of this study is to investigate the application of value engineering as a decision support system for choosing and evaluating P2P lending platforms, using Indonesia as a case study. P2P lending is a relatively new service in the digital economy for lending money to individuals through online financial intermediaries, where borrowers and lenders often have no prior relationship. Value engineering, a systematic approach to improving the value of a product or service, can be a valuable tool in the context of P2P lending. Through applying value engineering principles, P2P lending platforms can identify and prioritize the key factors that influence lending decisions, such as risk, return, and data privacy, to enhance the overall value proposition for both borrowers and lenders. Both value engineering and P2P lending are technoeconomic systems that aim to enhance the overall value and efficiency of a system or process, albeit through different approaches. This study presents a comprehensive framework for applying value engineering as a decision support system for P2P lending in Indonesia. The findings reveal that the value engineering index developed in this study effectively differentiates between P2P lending platforms based on their performance. Specifically, platforms with a high-value index were found to offer competitive interest rates, lower fees, and superior risk management, as evidenced by their non-performing loan (NPL) rates. In contrast, platforms with a low-value index were associated with higher NPLs and less favorable terms for stakeholders. These insights provide practical guidance for P2P lending platforms, regulators, and consumers; highlight the importance of a value engineering approach in optimizing platform selection; and enhance the P2P lending ecosystem’s sustainability in Indonesia. Full article

We examine the nonlinear relationship between interest rates on bank risk-taking behavior in South Africa between 2008:q1 and 2022:q3 using nonlinear autoregressive distributive lag (NARDL) and quantile autoregressive distributive lag (QARDL) models. Whilst the preliminary estimates from linear ARDL produce results adhering to conventional theory, the NARDL and QARDL analysis shows that the relationship between the variables is more complex. On one hand, the NARDL model shows that the phase of monetary policy (cyclical asymmetries) is important in determining the pass-through effects of interest rates on bank risk behavior. We find that both contractionary and expansionary monetary policy increases long-term risk through decreased liquidity for the former and increased non-performing loans for the latter. On the other hand, the QARDL model shows that the level of bank risk behavior (location asymmetries) is also important in determining the impact of interest rates on bank risk behavior. We find that interest rates affect bank risk behavior in ‘medium-to-high risk environments’ for unsecured loans and lending and in ‘medium-to-low risk environments’ for liquidity. Overall, these results enable us to recommend ways in which the SARB can strengthen its monitoring mechanisms given the multifaceted impact of interest rates on bank risk-taking. Full article

Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%. Full article

As the main demand side of rural financial services, farmers’ cooperatives are an important part of China’s rural finance. However, due to the lack of effective collateral, farmers’ cooperatives have problems such as difficulty in obtaining loans or expensive loans, which not only hinder the high-quality development of farmers’ cooperatives, but also limit the development of regional rural finance. Digital credit as a new financing model can effectively alleviate the problems of difficult and expensive loans and has received wide attention from the government and academia. Based on this, this paper analyzes the digital credit behavior of farmers’ cooperatives in detail by applying the “6C” family model to the grounded theory, and constructs a theoretical analysis model of farmers’ cooperatives’ digital credit behavior. The findings are as follows: The motivation for the digital credit of farmers’ cooperatives is that the credit procedures are simple, the loan period is short, and the loan interest rate is low; the condition is the farmers’ cooperative reputation advantage and government policy support,; the main form is the participation of cooperatives in short- and long-cycle digital credit; and the consequence is reflected in increasing the income of cooperative members, improving the availability of cooperative loans, promoting cooperative credit building, and achieving sustainable agricultural development. Different participation motivations have different effects on the form of credit. When motivated by simple credit procedures and short loan periods, farmers’ cooperatives choose “Huinong e-loan”; when motivated by simple procedures and low loan interest rates, farmers’ cooperatives choose “Funong Loan”. Different forms of credit will produce different performances. Farmers’ cooperatives choosing “Huinong e-loan” will produce economic performance; farmers’ cooperatives choosing “Funong Loan” will produce economic performance and social performance. In order to deal with the problem of digital credit of farmers’ cooperatives, the government needs to improve the relevant policies and regulations, reduce credit risks, and establish a sound credit system to provide credit guarantees for cooperatives and farmers. Financial institutions need to improve their financial services and innovate financial products and services to meet the multi-level credit needs of cooperatives. Full article

Hydropower as a renewable source can help many countries achieve their sustainable energy and climate goals, but large projects are challenging to finance because of their costs and risks. To fully realize the climate benefits of such projects, sponsors have recently fashioned complex financing arrangements that structure and allocate risks to reduce financing costs. This paper focuses on the blended financing approach adopted for the Nachtigal Hydropower Plant (NHP) in Cameroon. The purpose of the paper is to present a detailed systems analysis of Nachtigal’s financial arrangement to address the question of why the complex financing approach worked in practice. We accomplish this by creating a “financial simulator”—a computational model for evaluating risks and incentives embedded within the financing structure under different contract architectures and risk–event scenarios. Our simulator is a dynamic value–risk calculator that can be easily updated to study other climate-oriented projects that involve complex financial arrangements. We evaluated three aspects of the financing/contractual arrangements that made Nachtigal “bankable:” (i) guarantees that covered nonpayments, (ii) financial options on locally sourced loans; and (iii) an interest rate swap. We found: (i) the guarantees recovered project value threatened by four specific risks often associated with large hydropower investments (cost overruns, schedule delays, offtake risk, and low flow due to climate change); (ii) the mechanism significantly lowered interest rate charges; and (iii) private finance was mobilized—especially due to the options. The financial safeguards employed increased the likelihood of capturing the long-run sustainability benefits from NHP. Full article

Due to the COVID-19 pandemic, a very low interest rate policy was economically applied in Korea, and various investment activities through loans were activated. Real estate and stock prices rose rapidly, and many people became involved in stock investments because of economic instability. However, hastily started investment behavior resulted in economic loss and addictive behavior in stocks. The phenomenon of using stock investment to satisfy individual sensation seeking or addictive dependence on stocks due to lowered life satisfaction expectancy can become a serious social problem. However, the improvement of distress tolerance and the ability to endure pain despite frequent stock price fluctuations or lowered life satisfaction expectancy would be good alternatives to prevent stock addiction tendency. Therefore, the purpose of this study is to test the moderating effect of distress tolerance on the effect of adults’ sensation seeking and life satisfaction expectancy in stock addiction tendencies. The participants were 272 adults with stock investment experience. As a result, distress tolerance significantly moderated the positive effect of sensation seeking on stock addiction tendency. In addition, life satisfaction expectancy did not significantly increase in the group with high distress tolerance even if life satisfaction expectancy was lowered. These results suggest that stock addiction can be prevented by enhancing distress tolerance. Full article

This study analyzes the time-varying effect of interest rates on housing prices. As housing prices are too high for most consumers to afford with income alone, they use bank loans. Consequently, when interest rates fall, the demand for housing increases, causing prices to rise. This effect of interest rates was common in countries that implemented low-interest rates in response to the COVID-19 pandemic. Using Korean data from March 1991 to March 2022, this study examined the impact of interest rate shocks on housing prices by employing a time-varying parameter vector autoregressive model. According to the analysis, in Korea, while the impact of the interest rate shocks on housing prices was not significant before the global financial crisis, it increased dramatically afterward. Particularly, the impact of interest rate shocks was strongest relative to the past during the period of the increase in house prices from 2020 to 2021. The rise in the effects of interest rate shocks on housing prices is attributed to the increased dependence on loans for housing purchases. The results suggest that given the recent substantial increments in interest rates due to inflation, an interest rate shock would likely cause a global housing market recession. Full article

What characteristics of fintech lending platforms improve access to funding and increase financial inclusion? We build a computational model of platform lending that is used to study the endogenous loan network formation process on the platform. Given the multidimensional nature of financial inclusion, we address what factors influence the number of loans, the level of investment/debt, and how those relate to the distribution of investment/debt across agents. We find that platform scale and SME reach are essential in determining the number of loans on the platform. However, the willingness to accept risks is the main driver behind the value of those loans. We also find that increased platform scale, high-risk thresholds, and low-interest rates lead to more evenly distributed investments. Moreover, we find that large platforms help increase diversity and lead to a more evenly distributed power among peers. We conclude that digital platforms increase financial inclusion, helping to foster investment and achieve a more egalitarian allocation of resources. These results can guide new theory development about the impact of P2P lending on inequality as well as help platforms to promote financial inclusion. Full article

This study examines the relationship between politically connected independent commissioners and independent directors regarding the cost of debt. The sample is all companies listed on the Indonesia Stock Exchange for the 2010–2017 period, totaling 327 companies with a total data value of 1722 firm-year observations. We used the ordinary least squares regression model (OLS) and the Heckman 2SLS method to solve the endogeneity problem. We found that politically connected independent commissioners and politically connected independent directors negatively correlate with the cost of debt. These results indicate the importance of politically connected independent commissioners and independent directors in managing companies, especially in obtaining loans with low interest rates. In addition, our results are robust due to the use of the Heckman 2SLS test. Therefore, this research can contribute to the development of the literature related to corporate governance and political connections in public companies, so that politically connected independent commissioners and independent directors have an essential role in decision-making in companies. Full article

In this paper, we study the green credit financing equilibrium in a green supply chain (GSC) with government subsidy and supply uncertainty. The GSC system is composed of one manufacturer, two retailers, one bank, and the government. The manufacturer is subject to both supply uncertainty and limited capital. The manufacturer invests in the R&D of green products and borrows loans from the bank. The government subsidizes banks to encourage banks to provide loans to manufacturers with lower interest rates, which is termed “green credit financing”. The two retailers decide their order quantities with horizontal competition or horizontal cooperation. We first developed a Stackelberg model to investigate the green credit financing equilibriums (i.e., the interest rate of the bank, the manufacturer’s product green degree and wholesale price, and the retailers’ order quantity) under horizontal competition and horizontal cooperation, respectively. Subsequently, we analyzed how the subsidy interest rate, supply uncertainty, and supply correlation affect financing decisions regarding equilibrium green credit. We found that a high subsidy interest rate leads to a low interest rate of bank and the manufacturer can set a high level of green product and high wholesale price, while the retailers can set a high order quantity. Finally, we compared the green credit financing equilibriums under horizontal competition with those under horizontal cooperation using numerical and analytical methods. We found that, in general, the optimal decisions and profits of bank and SC members, consumer surplus, and social welfare under horizontal competition are higher than those under horizontal cooperation. The findings in this research could provide valuable insights for the management of capital-constrained GSCs with government subsidies and supply uncertainty in a competing market. Full article

This paper investigates information leakage and financing simultaneously in a supply chain (SC) consisting of one capital-constrained supplier and two retailers with private demand-forecast signals. The supplier invests in corporate social responsibility (CSR) events and displays supply uncertainty. The supplier decides whether to leak information (L) or not (N). Additionally, the supplier has two financing strategies: bank credit financing (B) and trade credit financing (T). Thus, by combining the supplier’s information leakage and financing decisions, we formulated four possible strategies (i.e., NB, NT, LB, LT) and built a game analysis model to address the interaction of information leakage and financing decisions. We first provide the SC members’ optimal operational decisions (including the order quantity, the wholesale price and CSR effort level) under four strategies. Subsequently, we compare the profits of the suppliers and retailers under four strategies by combining analytical and numerical analysis. Several interesting results were found: (1) the supplier’s optimal wholesale price, CSR effort level, and profit under information leakage were higher than those under no information leakage; (2) the supplier’s financing decisions are dependent on the loan interest rate as low supply uncertainty and low supply correlation motivate the supplier to prefer choosing trade credit financing; and (3) finally, several interesting insights in managing SCs are provided. Full article

The Housing Provident Fund System (HPFS) was established in China in the 1990s as a welfare program to offer low-cost loans and encourage the purchasing of houses. However, there has been some controversy over the income redistribution effect of HPFS. Previous studies focused on the effect of low-interest-rate loans but ignored the effects of tax exemptions and low-interest-rate deposits. This paper introduces a lifetime cash flow model which considers the effects of low-interest-rate loans, tax exemptions, and low-interest-rate deposits together. It compares the internal rate of return (IRR) for typical employees with different incomes in four situations: whether or not HPFS participation and whether or not house purchasing. We found that the IRRs of the typical low-income HPFS participants who buy houses with HPFS loans were lower than the IRRs of non-participants who buy houses with commercial mortgages without HPFS participation. For the typical middle-income employees, there is not much difference in IRR between the two situations. Only the typical high-income employees can benefit from HPFS participation, and this is mostly due to the effect of the tax exemptions, rather than the effect of low-interest-rate loans. Increasing the coverage of HPFS and HPFS loans among low-income employees will not improve the income redistribution effect of HPFS. Full article