Loan Balance Research Articles

Research on Optimization of Rural Financial Policies Based on Econometrics

Rural finance is vital for rural economic development. This study uses extensive empirical data and econometric models to analyze factors influencing rural finance development in China. It employs multiple linear regression, panel data, and time series models, optimizing them through feature selection, non-linear processing, and cross-validation. Scenarios such as baseline, fiscal support, interest rate preferences, and inclusive finance are simulated to assess impacts on rural loan balances and resident income levels. The findings highlight that comprehensive measures yield better policy synergies, offering policy optimization suggestions for decision-makers.

Factors influencing the pricing of credit risk mitigation warrants in corporate bond financing

AbstractIn 2022, various Chinese regulatory authorities, including the China Securities Regulatory Commission (CSRC) and the State‐owned Assets Supervision and Administration Commission (SASAC), jointly issued documents and convened seminars at the Shanghai and Shenzhen Stock Exchanges to highlight the importance of credit protection tools, particularly credit risk mitigation warrants (CRMWs), to bolster corporate bond market financing. However, empirical research on the factors influencing CRMW prices in the Chinese market remains scarce. This study examines the factors affecting CRMW creation prices using a sample of CRMWs issued between 2018 and 2023 and analyzes the macroeconomic factors, characteristics of the underlying issuers, and related financial instruments. This study employs Shapley decomposition to evaluate the contribution of each variable. The findings reveal that, on the macroeconomic front, the year‐on‐year GDP growth rate from the previous quarter and the loan balance of financial institutions significantly and negatively impact CRMW prices. Regarding the underlying issuers, the issuer's credit rating and return on net assets significantly negatively affect CRMW prices, whereas the asset–liability ratio and issuer age have a significant positive impact. Additionally, related financial instruments such as bond interest rates and the risk mitigation certificate term positively contribute to the coverage of the underlying bond term and have a significant positive impact on CRMW prices.

Driving A-loan: Automobile debt, neighborhood race, and the COVID-19 pandemic

COVID-19 altered travel patterns in the U.S. Studies have analyzed the effect of the pandemic on travel mode, including working from home, but few have focused on automobile ownership—a relationship with potentially long-term consequences for accessibility, household budgets and debt, and policy efforts to meet climate goals.To understand the association between the pandemic and automobile ownership, we rely on a unique credit panel dataset from Experian and examine three different automobile loan-related outcome measures: annualized growth rate of new automobile loan balances, average new loan size, and the number of new loans. We focus specifically on changes across loans in neighborhoods by race/ethnicity, hypothesizing larger increases in automobile debt in Black and Latino/a neighborhoods, where workers are less likely to be able to telework. The annualized growth rate of new automobile loans increased during the pandemic across all neighborhoods by race/ethnicity, increasing most rapidly in Latino/a neighborhoods. Controlling for other factors, loan size increased similarly across neighborhoods by race/ethnicity. The increase in automobile lending in Latino/a neighborhoods, therefore, likely was explained by a significant uptick in the number of new loans.The growth in automobile lending during the pandemic was potentially prompted by pandemic-induced changes in the need for automobiles and facilitated by an expanded social safety net. As the pandemic and its various forms of public financial assistance recede, the findings underscore the importance of ongoing assistance in enabling automobile ownership or shared access among households with limited means whose livelihoods depend on the access that vehicles provide.

The impact of green finance and technology on the commercial banks’ profit and risk

This paper investigates the impact of green credit on the performance and risk of Chinese commercial banks, and also explores the impact of green technology on this mechanism. The results show that for all banks, green loan balances have a significantly negative impact on net profit. While for large, small and medium sized banks, it also reduces and increases bank insolvency risk respectively. Therefore, our findings suggest that the standard mechanism for measuring the benefits of green credit should be improved and the regulatory mechanism for green credit should be strengthened.

A mortgage valuation model: a tale of two equations

ABSTRACT In the Journal of Financial Economics, Gerardi, Willen, and Zhang (2023) examine the relationship between prepayment rates, race, and monetary policy. In this article, we identify an anomaly in GWZ‘s valuation equation that relates to the effect of prepayment rates on the ratio of market price to initial loan balance. We then show the mathematical steps to correct this anomaly.

High Risk, Constrained Return: Impact of Student Loans on Agricultural Real Estate

A farming household’s decision to continue producing agricultural commodities within the United States is influenced by a multitude of factors. Thus, this study seeks to examine whether the outstanding student loan balance of any member within a farming household may explain why the total number of acres devoted to the production of agriculture in the United States continues to decline. Panel data from the 2007–2009 Survey of Consumer Finances are analyzed via a fixed effect model to estimate the effect of outstanding student loan balances on farmland acreage owned, controlling for other factors like farm income, debt, and land prices. The results suggest that for each additional dollar of outstanding student loan debt, there is an associated decrease of 0.0064 acres in total farmland ownership. This suggests that student loan debt may also be a factor in the decline in real estate devoted to agriculture production. The estimated effect is both economically and statistically significant. This study contributes to the literature on the risks and constraints associated with farming households that own or seek to procure additional acres of agricultural producing real estate.

Analysis of the impact of agricultural credits on agricultural mechanization in Türkiye

Agricultural mechanization is one of the high-cost important inputs used in agricultural production. Thanks to agricultural mechanization, time and manpower are saved greatly in the agricultural production process. Agricultural loans, one of the most important agricultural financing instruments, are considered necessary for agricultural enterprises to invest in agricultural mechanization. This study, which adopts a quantitative research method approach, aimed to analyze the impact of agricultural loans on agricultural mechanization in Türkiye using time series analysis. Within the scope of the purpose, annual data covering the period 1981-2022 were gathered from data sources related to loans and agricultural mechanization. A log-log model was constructed with loans as the independent variable and agricultural mechanization as the dependent variable, and ARDL bounds testing method was applied in the estimation of the model. According to the findings obtained from the estimation of the model, there was a long-term, positive, and statistically significant co-integration relationship between loans and agricultural mechanization variables. A 1% increase in the loan balance was found to increase agricultural mechanization by approximately 0.035%. On the other hand, the short-term relationship between the variables was found to be negative and statistically significant. Short-term deviations from the long-term equilibrium caused by shocks were shown to vanish by approximately 86% after 1 period. In this context, it is clear that the adaptation process between the variables is very rapid.

Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression

With the deepening linkage between housing and finance, the financial attributes of housing have been increasing. Thus, housing financialization has become a worldwide phenomenon that is gradually emerging in China's real estate market and thus cannot be ignored. The amount of urban capital is an important manifestation of financialization, but only a few studies have considered the spatial heterogeneity of impact of urban capital amount—represented by loan balances (LOAN) on housing prices. To fill this gap, this study builds a dataset of housing prices and influencing factors for county-level units using 2109 counties in China and analyzes the spatial scope and heterogeneity of housing financialization. Results show that globally, LOAN has a significant positive effect on housing prices, and the impact direction is in line with theoretical expectations. Locally, spatial heterogeneity exists for the impact of LOAN on housing prices, and the phenomenon of housing financialization is mainly observed in China's eastern coastal area. This study can help enhance the understanding of the spatial constraints on the impact of LOAN on housing prices and the spatial heterogeneity of housing financialization in China. Moreover, it provides a theoretical basis for policymakers to formulate spatially differentiated housing policies.

Effects of Transformation on Outreach Performance of Microfinance NGOs in Kenya

This research examines how transformation affects the outreach performance of microfinance NGOs in Kenya. Specifically, it analyzes the impact of various transformation indicators on outreach performance. Utilizing a quantitative research approach, the study utilizes unbalanced panel data spanning 19 years (1997 to 2015) obtained from the Microfinance Information Exchange (Mix) Market databank, focusing on six surveyed transformed microfinance NGOs in Kenya. Panel data regression models and instrumental variables estimation methods are employed for model specification. The findings reveal that outreach, measured by the average loan balance per borrower, is significantly and negatively influenced by the debt-to-asset ratio but not significantly affected by the debt-to-equity ratio or deposits-to-total assets ratio. The percentage of female borrowers is significantly influenced by the debt-to-equity ratio and debt-to-asset ratio, while the number of active borrowers is significantly influenced by the debt-to-asset ratio, deposits-to-total assets ratio, institutional size, and institutional age. These results suggest a necessity for microfinance NGOs facing capital funding challenges to devise policies that facilitate the utilization of commercial capital sources to expand outreach to impoverished individuals. Nonetheless, the management of transformed microfinance NGOs must implement safeguards ensuring that the pursuit of commercial funding options and public deposits does not compromise their mission of serving the most vulnerable populations.

Financial and Tax Planning Strategy to Optimize the Financial Performance of PT Kaltim Daya Mandiri

PT Kaltim Daya Mandiri is a utility provider company located in Bontang, East Kalimantan, Indonesia, currently planning to invest in a new project investment by constructing a nitrogen plant in Bontang and a utility center plant in West Papua to support the business expansion of PT Pupuk Kalimantan Timur in the eastern region of Indonesia. To execute the project, the company must ensure the adequacy of cash flow to support the existing operational and project activities needs; therefore, it requires cash flow management and the right financing strategy to ensure the company’s financial performance remains stable. In this study, the analytical method used to generate a strategic formulation to optimize financial performance during projects is using quantitative and qualitative analysis. The strategic formulation comprises a financial strategy, specifically the option of project financing and a tax planning strategy related to tax refund and tax exemption. From the exploration of several strategies, there are two options for a combination of strategies, namely Scheme 1: Tax planning strategies and financial strategies through utilizing banking products (investment loan and short-term loan) and Scheme 2: Tax planning strategies and financial strategies through utilizing debt securities (corporate bonds and medium-term note). The simulation shows that in terms of the efficiency aspect towards interest cost imposed during the period, impact on the company’s cash flow during the project development period, and the proforma of financial performance, Scheme 1 is more favorable compared to Scheme 2. Using Scheme 1, the bank loan’s interest will be imposed on the cash flow statement and profit and loss statement after the project is declared commercial. During the project construction, the company will only be exposed to an interest construction, which will be an additional loan balance after the project commercial. In addition, instalment payments (principal and interest portion) have an impact on reducing the loan balance, and the future interest expense will decrease. Whereas, by using Scheme 2, the company will be directly burdened by interest coupons that need to be paid quarterly and constantly, which can affect cash flow and profit and loss statements directly.

Credit supply shocks, home purchase volume, and borrowing behavior

AbstractWe exploit the 2007 private label securitization (PLS) freeze as a quasi‐experiment to study the impact of a negative credit supply shock on home purchases and borrowing behavior. Using a difference‐in‐differences estimator, we show that a negative supply shock to first‐lien mortgages has little impact on the volume of purchases financed with a mortgage, but significantly reduces the average first‐lien loan balance. Much of this reduction in loan balances is the result of increased bunching at the conforming loan limit that is achieved through a combination of greater second mortgage utilization and larger downpayments. Importantly, we find significant heterogeneity in the response to the mortgage supply shock across borrower characteristics and house price levels. Home purchase volume does decline after the shock for less creditworthy borrowers and in expensive locations. The reduction in first‐lien balances is fairly uniform across borrower types, however, the effect is slightly more acute in less expensive areas. Our results suggest that financial market frictions (e.g., downpayment constraints, imperfect credit) play an important role in determining how credit supply shocks impact housing purchases and borrowing behavior.

Macroeconomic Determinants of Non-Performing Loans in Malaysia

This study re-examines the macroeconomic variables those explaining the outstanding balance of non-performing loans (NPL) in Malaysia, namely interest rate, unemployment rate, output, and price level. It covers 11 financing sectors between commercial and Islamic banks, for examples, primary agriculture, electricity, gas and water supply, manufacturing, household sectors, and so on. The data covers monthly observations for the period 2019-2021. Given non-stationarity property in nature for the variables, the ordinary least square (OLS) estimator applies for the first-differenced variables. The empirical results show that higher output reduces overall NPL including of Islamic banks but is inelastic. The past NPL explains the recent outstanding balance. The macroeconomic variables explain the most for primary agriculture, construction, mining & quarrying, transport, storage & communication, and other sector. This study also provides some relevant policy implications.

Does Direct Monetary Policy Affect the Supply of Bank Credit to Small and Medium-Sized Enterprises? An Analysis Based on Chinese Data

In order to develop the real economy and solve the problems of enterprise financing and lending, banks should increase their support for SMEs (small and medium-sized enterprises). The People’s Bank of China introduced two direct monetary policy tools in June 2020, which are important for alleviating the financing problems of SMEs, improving the construction of financial support for the real economy and promoting the recovery of economic development. This paper manually collects annual data on the loan balances of SMEs from listed commercial banks in China from 2011 to 2021, and it empirically tests the implementation effects of the direct monetary policy tools using the double-difference model and the moderating effect model. The results of the study indicate that the implementation of the direct monetary policy tools can increase the credit supply of commercial banks to SMEs. The moderating effect of digital inclusive finance on the impact of direct monetary policy on the credit supply of SMEs is not statistically significant for the time being. Therefore, it is necessary to improve the transmission mechanism of structural monetary policy, establish a sustainable development mechanism for digital inclusive finance, and guide commercial banks to improve their capital strength and profitability so that structural monetary policy tools can be most effective.

Pricing tenure payment reverse mortgages with optimal exercised prepayment options by accounting for house prices, interest rates, and mortality risk

Prepayment options can be exercised to terminate reverse mortgages (RM hereafter) early and receive house prices, minus loan balances, at the expense of future annuity proceeds. Prior RM evaluation studies use probability or intensity models to calibrate option exercise policies with historical prepayment records and may not apply to countries without sufficient historical records. In addition, these models may fail to capture time-varying policies due to changing market conditions. Accordingly, insurers may run the risk of undervaluing option premiums and overestimating fair annuity rates. To find optimal exercise policies that maximize option premiums and establish the most conservative annuity rates, we propose a three-dimensional tree for modeling stochastic house prices, interest rates, and mortality risks. We analyze the gain and loss to exercise the option in each scenario to determine the optimal policy. Fair annuity rates are evaluated to ensure that expected insurer losses (i.e. loan balances exceeding house values) equal gains (i.e. insurance premiums plus house values exceeding loan balances). We find that such non-optimal exercise policies undervalue option premiums and overestimate fair annuity rates. Increasing upfront premiums, insurance premium rates, and early redemption charges reduce prepayment incentives and increase fair annuity rates. We also analyze influences from factors such as the policyholder's age and volatilities of house prices and interest rates.

Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 ‘problem areas’ were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (−0.126) had a negative impact. Among the natural indicators, soil organic matter (−0.212) and temperature (−0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.

Sistem Pendukung Keputusan Pemberian Pinjaman Karyawan PTBA UPO dengan Metode Simple Additive Weighting (SAW)

The PTBA UPO Employee Cooperative is a cooperative specifically designed for PTBA UPO employees, including both active and inactive employees. To facilitate the decision-making process regarding the priority loan recipients from the PTBA UPO cooperative, a system is needed to help make decisions more quickly, effectively, and efficiently. A decision support system is a method that aims to assist individuals or companies in making accurate, fast, and precise decisions. The Simple Additive Weighting (SAW) is a decision-making method that uses weighted summation. Several criteria have been established to determine the granting of loans at the PTBA UPO Cooperative, including membership status, needs, loan order, income amount, and previous loan balance. Members of the PTBA UPO cooperative who meet the predetermined criteria and have a preference final score become the priority loan recipients. This decision support system makes it easier for Savings and Loans Managers and PMs (Assistant Managers) to quickly determine which members have the highest priority in receiving loans.

An Empirical Study on How Financial Development Influences Carbon Emissions in Yangtze River Delta

Financial development influences economic development to some extent. China is in the important stage of economic restructuring and upgradation, while facing both the foreign and the domestic pressure of developing low-carbon economy and promoting energy conservation and emissions reduction, requiring people to understand the relationship between financial development and carbon emissions. This paper examines how financial development influences the carbon emissions using the panel data composed by 37 prefecture-level cities in Yangtze River delta during the 2008-2017 period. This paper build the multiple regression model based on modified environmental impact assessment model (EIA) and measure financial development level using the percentage of loan balance of financial institutions in GDP and the percentage of saving balance of urban residents in GDP. The result shows a negative association between financial development and carbon emissions among 37 cities in Yangtze River Delta. Therefore, the policymakers should try to maximize the supporting of financial institutions to local economies, develop green finance and promote the regional information sharing and cooperation.

Practical Applications of Primer on Agency Mortgage-Backed Securities Specified Pools and Their Convexity Profiles

In <b>Primer on Agency Mortgage-Backed Securities Specified Pools and Their Convexity Profiles</b> from the Spring 2022 issue of <b><i>The Journal of Fixed Income</i></b>, authors <b>Glenn M. Schultz</b> (of <b>MUFG Securities</b>) and <b>Frank J. Fabozzi</b> (of <b>Johns Hopkins University</b> and <b>EDHEC Risk Institute</b>) describe the various sectors of the market for agency mortgage-backed securities (MBS) specified pools. While most MBS are traded on a generic basis—without identifying the underlying pool of mortgage loans—some trades specify the particular underlying pool of the security that must be delivered to clear the trade. The first type of trade is called a to-be-announced (TBA) trade, and the second type is called a specified-pool trade. When market participants trade agency MBS on a specified pool basis, they generally do so because they expect the loans in the underlying pools to display atypical prepayment behavior, which gives the related MBS atypical convexity properties. Specified pools are categorized into sectors, based on loan balance, loan-to-value (LTV) ratio, and geographical location, plus whether they consist of refinanced mortgages or mortgages for investment properties, relocating employees, or people with suboptimal credit scores. The authors describe each specified-pool sector and its convexity profile. Most have positive convexity relative to generic pools and trade at a premium, but jumbo and relocation mortgage pools have negative convexity relative to generic pools and trade at a concession. Investors can manage risk by allocating assets to customize the convexity profiles of their MBS portfolios.

Prediction of Factors Influencing Social Performance of Indian MFIs using Machine Learning Approach

The aim of the current work is to predict the impact of MFI specific internal factors on the social performance of Indian microfinance institutions (MFIs) by using machine learning techniques. Social performance index (SPI) is designed by taking data of 73 Indian MFIs for 10 years with the help of an indexing technique where six different factors (operational self sufficiency, number of women borrowers, number of rural borrowers, gross loan portfolio, average loan balance per borrower / GNI per capita and cost per borrower) representing different dimensions of functioning of MFIs are considered. The data is taken from MIX data repository. Pooled OLS regression model is used for analyzing impact of various MFI specific factors on SPI. For predicting the SPI, Artificial Neural Networks (ANN) machine learning model is considered that takes all independent variables as input. The results of regression model indicate that size, legal status, outreach and service provisions significantly affect SPI. ANN analysis result indicates that social performance of MFIs gets determined by MFI specific internal factors. The experimental result indicates that the proposed ANN prediction model is providing better result for predicting the SPI. The findings suggest that MFIs can contribute for development of the society by adopting suitable policies keeping in view certain internal factors.

Financial Development, Real Estate Investment and Economic Growth

ABSTRACT In recent years, China’s economic growth has shown a continuous downward trend, but the financial industry’s added value as a percentage of GDP and the financial development level have continued to rise, and real estate investment still maintains a relatively high proportion of total fixed asset investment in society. Taking financial development as the starting point, which is measured by the balance of loans from financial institutions as a percentage of GDP, this paper selects China’s provincial panel data from 2003 to 2017, and uses the system generalized method of moments to empirically study how financial development affects real estate investment and economic growth. The results reveal that financial development pushes up the proportion of real estate investment in fixed asset investment but has a negative impact on economic growth. The increase in the proportion of real estate investment promotes economic growth and plays a masking effect on the suppression of economic growth by financial development. Further analysis shows that financial development affects the growth of the real economy by restraining the development of the secondary industry and increasing real estate investment is conducive to alleviating the restraining effect of financial development on the growth of the real economy.