Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit si...Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.展开更多
Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in ...Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.展开更多
Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in...Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods(data mining,machine learning and artificial intelligence).To achieve this objective,the study employs a predictive methodology using descriptive and exploratory statistics and frequency,frequency&monetary(RFM)classification techniques,differentiating between SMEs and large businesses via cluster analysis and supervised models.A dataset of 221,292 card records from a Latin American merchant payment gateway for the year 2022 is used.For fraud alerts,the classification model has been selected for small and medium–sized merchants,and the multilayer perceptron(MLP)neural network has been selected for large merchants.Random forest or Gini decision tree models have been selected as backup models for retraining.For the detection of punctual fraud patterns,the K-means and partitioning around medoids(PAM)models have been selected,depending on the type of trade.The results revealed that the application of the identified models would have prevented between 48 and 85%of fraud transactions,depending on the trade size.Despite the promising results,continuous updating is recommended,as fraudsters frequently implement new fraud techniques.展开更多
In 2021,12 fraudulent cases were identified in the Chinese carbon market.As a critical component of this emerging market,China’s carbon-credit scheme in the automotive sector faces several shortcomings,including info...In 2021,12 fraudulent cases were identified in the Chinese carbon market.As a critical component of this emerging market,China’s carbon-credit scheme in the automotive sector faces several shortcomings,including informational opacity and operational inefficiency,which affect market functionality and fairness.This study develops an information system that integrates blockchain technology and the Internet of Things to manage a carbon-credit scheme.Specifically,we attached carbon credits to each vehicle with radio frequency identification electronic tags and a chained data structure to ensure the traceability and reliability of information flow.We use the distributed ledger technology and establish five distinct types of smart contracts for decentralized operations to ensure that all procedures of the Chinese carboncredit scheme are standardized and under public scrutiny.The proposed infrastructure has the potential to significantly enhance the transparency and efficiency of China’s carbon-credit schemes.展开更多
In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin...In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.展开更多
Taking China’s 2018 value-added tax(VAT)credit refund reform as an exogenous shock to improve VAT neutrality,we use a difference-in-differences approach to explore how the reform affected corporate social responsibil...Taking China’s 2018 value-added tax(VAT)credit refund reform as an exogenous shock to improve VAT neutrality,we use a difference-in-differences approach to explore how the reform affected corporate social responsibility(CSR).We find that the reform motivated firms to improve CSR performance.The reform has a“resource”effect,increasing internal funds and reducing financing costs,thereby enhancing firms’ability to undertake CSR.The reform also has a“reputation”effect,stimulating firms’willingness to engage in CSR to improve their reputations.CSR following the reform increases firm values and reduces bankruptcy risk.Our study provides fresh insights into VAT neutrality theory and is a reference for tax reform in emerging economies.展开更多
The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account ...The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account for a significant share of employment and GDP globally.However,the traditional bank credit model has long failed to effectively meet the financing needs of SMEs due to issues such as information asymmetry,high cost,and difficulty in risk assessment,resulting in serious financing constraints.Digital financial inclusion,through technological innovation and big data analysis,has significantly reduced credit costs,alleviated information asymmetry,and provided SMEs with more flexible and efficient financing channels.Research shows that digital financial inclusion can not only ease the financing constraints of SMEs,but also promote their innovation and growth,providing important support for building a more inclusive and sustainable financial ecosystem.展开更多
The rapid development of digital finance is profoundly changing the structure and management mode of bank credit.Through mobile banking,artificial intelligence,big data,cloud computing,and online lending platforms,ban...The rapid development of digital finance is profoundly changing the structure and management mode of bank credit.Through mobile banking,artificial intelligence,big data,cloud computing,and online lending platforms,banks are able to optimize credit services,increase efficiency,and improve access to credit[1].This evolution began in the late 20th century and accelerated after the 2008 global financial crisis.Through automated approval,precise risk assessment,and real-time monitoring,digital finance has improved credit efficiency,reduced costs,promoted financial inclusion,and enabled groups not covered by traditional financial services to gain support.However,the popularity of digital finance has also brought new challenges,such as consumer protection,cybersecurity,and fraud risks,and there is an urgent need to update the regulatory framework to address these issues.Nonetheless,the technological spillover effects of digital finance have promoted bank credit innovation and improved market competitiveness.This paper analyzes the role of digital finance in credit efficiency,cost,risk management,and financial inclusion,and puts forward policy recommendations to deal with potential risks and ensure the stability and sustainable development of the financial system.展开更多
This study examines the causal relationship between financial technology startup venture capital(VC)financing and its deals with domestic credit provided by the banking sector and equity market movement.Despite the ri...This study examines the causal relationship between financial technology startup venture capital(VC)financing and its deals with domestic credit provided by the banking sector and equity market movement.Despite the rise of alternative finance,such as fintech venture capital(it is the fund that venture capital firms put into young,promising fintech companies so that they can help them expand and scale quickly),which is yet underexplored,borrowers still heavily rely on banks and the stock market for financing.We use panel data from 57 countries from 2010 to 2020 and an advanced econometric method called the cross-sectional autoregressive distributed lag model(CS-ARDL)to determine how the size and number of fintech equity funds dealt with by venture capital firms,banking sector credit,and stock market returns are interrelated at the global level and across regional,income,and economic levels.Our results reveal a cointegrating relationship between fintech venture capital funding and deals with bank loans and equity market returns.However,this relationship varies across the regions studied and between developed and developing economies.Our findings provide crucial guidelines for policymakers to create policies that support balanced financial development by highlighting the global interaction of equity market movements,banking credit,and fintech venture capital investment and lay the groundwork for internationally aligned policies to guarantee the optimal distribution of financial capital and improve economic stability and adaptability by illustrating how these links differ across geographical locations and economic conditions.展开更多
The green credit policy(GCP)is an essential financial policy tool for solving the problem of environmental pollution,and urban energy conservation is an effective way to achieve the goal of carbon neutrality.However,e...The green credit policy(GCP)is an essential financial policy tool for solving the problem of environmental pollution,and urban energy conservation is an effective way to achieve the goal of carbon neutrality.However,existing research has not verified the energy-saving effects of green credit(GC)at the city level.Based on panel data from 283 cities in China,this study aims to investigate whether GC can effectively reduce urban energy intensity(EI),which is an important complement to existing research.In terms of research methods,to better evaluate the effect of the policy and exclude the influence of other relevant factors,this study considers the promulgation of the Green Credit Guideline(GCG)in 2012 as the basic event,uses the difference-in-differences(DID)model to investigate the impact of GC on EI,and discusses the main impact mechanism.The key results are follows.(1)GC can effectively reduce urban EI.(2)Public environmental demand positively regulates the negative correlation between GC and EI.(3)GC reduces EI through three main channels:government support,capital investment,and technological innovation;however,the mechanism of industrial structure has no significant effect.(4)The effect of GC is more significant in areas with large urban scales,low environmental regulation intensity,and high industrial agglomeration.Based on the above results,this study presents puts forward targeted policy recommendations to strengthen the role of GC in urban sustainable development.展开更多
Under the impetus of the“Dual Credit”policy,traditional fuel vehicle manufacturers are confronted with significant pressure to meet new energy vehicle credit requirements.To address this challenge,these manufacturer...Under the impetus of the“Dual Credit”policy,traditional fuel vehicle manufacturers are confronted with significant pressure to meet new energy vehicle credit requirements.To address this challenge,these manufacturers are increasingly adopting the Original Design Manufacturer(ODM)strategy to collaborate with new energy vehicle enterprises,thereby acquiring credits and expanding their market presence.However,this strategic approach not only intensifies competition between new energy and traditional fuel vehicle markets but also reshapes the profit distribution between the two types of firms.Drawing upon the framework of the Dual Credit policy,this study establishes a Cournot game model to examine the strategic interactions between traditional fuel vehicle manufacturers and new energy vehicle producers.It further investigates the optimal production decisions under the ODM strategy and evaluates their implications for market dynamics and corporate profitability.The findings reveal that,although the ODM strategy heightens market competition,it leads to substantial profit improvements for both types of manufacturers compared to the alternative of directly purchasing credits,while also fostering the growth of the new energy vehicle sector.Moreover,the Case study demonstrates micro-level impact of the dual credit policy on enterprises’response strategies,offering valuable insights for policymakers and industry decision-makers.展开更多
Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predict...Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predictive performance.Additionally,data collected over a long time span may cause differences in the data distribution,and classic supervised learning methods may exhibit poor predictive abilities under such conditions.To address these issues,a domain-adaptation-based multistage ensemble learning paradigm(DAMEL)is proposed in this study to evaluate the credit risk of SMEs in SCF.In this methodology,a bagging resampling algorithm is first used to generate a dataset to address data scarcity.Subsequently,a random subspace is applied to integrate various features and reduce feature redundancy.Additionally,a domain adaptation approach is utilized to reduce the data distribution discrepancy in the cross-domain.Finally,dynamic model selection is developed to improve the generalization ability of the model in the fourth stage.A real-world credit dataset from the Chinese securities market was used to validate the effectiveness and feasibility of the multistage ensemble learning paradigm.The experimental results demonstrated that the proposed domain-adaptation-based multistage ensemble learning paradigm is superior to principal component analysis,joint distribution adaptation,random forest,and other ensemble and transfer learning methods.Moreover,dynamic model selection can improve the model generalization performance and prediction precision of minority samples.This can be considered a promising solution for evaluating the credit risk of SMEs in SCF for financial institutions.展开更多
To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with mini...To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs.This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory mechanisms.The study considers the credit of various economic agents within the social credit system such as countries(or governments),corporations,and individuals and their credit variations in online markets(i.e.,network credit).A historical review of the theoretical(or model)development of economic agents is presented together with significant works and future research directions.Some interesting conclusions are summarized from the literature review.(1)Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms.(2)The most popular credit scoring methods include expert systems,econometric models,artificial intelligence(AI)techniques,and their hybrid forms.Evaluation indexes should vary across different target agents.(3)The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.展开更多
Background:This study examines the access to credit,credit investment,and credit fungibility for small-holder farmers and medium-and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh,Pa...Background:This study examines the access to credit,credit investment,and credit fungibility for small-holder farmers and medium-and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh,Pakistan.Methods:A standardized questionnaire was used to collect data from 87 farmers in the Shikarpur District.We investigated the availability of credit and the use of credit fungibility by farmers with small-,medium-,and large-scale holdings by applying a credit fungibility ratio and an ANOVA technique.The factors that influence the farmers’access to agricultural credit were analyzed using a probit regression model.Results:The results revealed that farmers in both study groups used some amount of their agricultural credit for non-agricultural activities.Further,the results of the probit regression analysis showed that formal education,farming experience,household size,and farm size had a positive and significant influence on the farmers’access to agricultural credit.Conclusion:Based on these findings,our study suggests that a strong monitoring of farmers is needed in the study area.展开更多
In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-&...In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-à-vis conflict of interest and reputation.A novel distribution dynamics approach is employed to compute the probability distribution and,hence,the downgrade and upgrade probabilities of a credit rating assigned by these two CRAs of different compensation systems based on the dataset of 750 U.S.issuers between 2011 and 2018,that is,after the passage of the Dodd–Frank Act.It is found that investor-paid ratings are more likely to be downgraded than issuerpaid ratings only in the lower rating grades,which is consistent with the argument that investor-paid agencies have harsher attitudes toward potentially defaulting issuers to protect their reputation.We do not find evidence that issuer-paid CRAs provide overly favorable treatments to issuers with threshold ratings,implying that reputation concerns and the Dodd–Frank regulation mitigate the conflict of interests,while issuerpaid CRAs are more concerned about providing accurate ratings.展开更多
The study is focused on agricultural credit sources and determinants of credit acquisition by farmers in ldemili local government area of Anambra State, Nigeria with specific objectives to： describe socio-economic ch...The study is focused on agricultural credit sources and determinants of credit acquisition by farmers in ldemili local government area of Anambra State, Nigeria with specific objectives to： describe socio-economic characteristics of rural farmers; identify sources of agricultural credit available to rural farmers; determine socio-economic factors that influence agricultural credit acquisition of farmers; ascertain reasons for any credit misappropriation and identify problems that constrain farmers from agricultural credit acquisition. Ninety farmers were randomly selected by multi stage random sampling technique. Semi-structured questionnaire was used to elicit data for the study. Descriptive statistics and multiple regression model were used in achieving the objectives. Results indicated that 74.44% of respondents were males with a mean age of 45 years. Majority （76.67%） were married with large house hold sizes. Majority （93.33%） received different level of education, with sources of credit from friends/relatives （30.00%）, cooperative societies （43.33%）, money lenders （14.44%）, and cumulatively from formal sources （（12.22%）. The result of the multiple regression analysis revealed age, household size, membership of cooperative societies, marital status, education level, farm size and amount of loan repaid at varied signs and levels as significant predictors of amount of agricultural credit acquired by farmers. The most common reason given among the respondents （55.89%） of those who misappropriated acquired agricultural credit, was meeting nonfood needs of the household. The farmers encountered problems of high interest rate （78.89%）, lack of collateral （75.56%）, long distance from source of credit （50.00%）, poor harvest （37.78%）, moratorium （33.33%） and delay in loan approval/disbursement （44.44%） as constraints to acquire credit. The study recommends that the state government should pass policies aimed at providing free educative seminars to all illiterate farmers to teach them possible ways and methods of acquiring credit. To ensure mass attendance to such seminars, little incentives should be given to farmer participants.展开更多
The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has r...The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.展开更多
Credit scoring models are quantitative models used commonly by financial institutions in the measurement and forecasting of credit risk, having a consolidated use in the process of credit concession of these instituti...Credit scoring models are quantitative models used commonly by financial institutions in the measurement and forecasting of credit risk, having a consolidated use in the process of credit concession of these institutions. The purpose of this paper was to evaluate the possibility of application of credit scoring models in a microcredit institution named Fundo Rotativo de A~~o da Cidadania--Cred Cidadania （Revolving Fund of Citizenship Action--Cred Cidadania）, located in Recife （Brazil）. In order to do this, data related to a sample of clients of the Cred Cidadania was collected, and this data was used to develop two types of credit scoring models： one is credit approval, another is called behavioral scoring. The statistical technique applied in the construction of the models was logistic regression. The results of the study demonstrated that the credit scoring models obtain satisfactory performance when used in the analysis of credit risk in the Cred Cidadania microcredit institution, reaching a correct client classification percentile of about 80%. The results also indicate that the use of credit scoring models supplies subsidies to the institution, aiding it in the prevention and reduction of insolvency and in the decrease of its operational costs, two problems that affect their financial sustainability.展开更多
文摘Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.
基金supported by National Key R&D Program of China(2022YFA1008000)the National Natural Science Foundation of China(12571297,12101585)+1 种基金the CAS Talent Introduction Program(Category B)the Young Elite Scientist Sponsorship Program by CAST(YESS20220125).
文摘Credit risk assessment is a crucial task in bank risk management.By making lending decisions based on credit risk assessment results,banks can reduce the probability of non-performing loans.However,class imbalance in bank credit default datasets limits the predictive performance of traditional machine learning and deep learning models.To address this issue,this study employs the conditional variational autoencoder-Wasserstein generative adversarial network with gradient penalty(CVAE-WGAN-gp)model for oversampling,generating samples similar to the original default customer data to enhance model prediction performance.To evaluate the quality of the data generated by the CVAE-WGAN-gp model,we selected several bank loan datasets for experimentation.The experimental results demonstrate that using the CVAE-WGAN-gp model for oversampling can significantly improve the predictive performance in credit risk assessment problems.
基金supported by project Finance for all(F4A),funded by the"Institución Gran Duque de Alba"and"Diputación provincial deávila"under the grant 3364/2022.
文摘Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods(data mining,machine learning and artificial intelligence).To achieve this objective,the study employs a predictive methodology using descriptive and exploratory statistics and frequency,frequency&monetary(RFM)classification techniques,differentiating between SMEs and large businesses via cluster analysis and supervised models.A dataset of 221,292 card records from a Latin American merchant payment gateway for the year 2022 is used.For fraud alerts,the classification model has been selected for small and medium–sized merchants,and the multilayer perceptron(MLP)neural network has been selected for large merchants.Random forest or Gini decision tree models have been selected as backup models for retraining.For the detection of punctual fraud patterns,the K-means and partitioning around medoids(PAM)models have been selected,depending on the type of trade.The results revealed that the application of the identified models would have prevented between 48 and 85%of fraud transactions,depending on the trade size.Despite the promising results,continuous updating is recommended,as fraudsters frequently implement new fraud techniques.
基金Financial support from the National Natural Science Foundation of China(under grants numbers:72271249 and 72432005)from Guangdong Basic and Applied Basic Research Foundation(under grant number:2023B1515040001)are highly appreciated.
文摘In 2021,12 fraudulent cases were identified in the Chinese carbon market.As a critical component of this emerging market,China’s carbon-credit scheme in the automotive sector faces several shortcomings,including informational opacity and operational inefficiency,which affect market functionality and fairness.This study develops an information system that integrates blockchain technology and the Internet of Things to manage a carbon-credit scheme.Specifically,we attached carbon credits to each vehicle with radio frequency identification electronic tags and a chained data structure to ensure the traceability and reliability of information flow.We use the distributed ledger technology and establish five distinct types of smart contracts for decentralized operations to ensure that all procedures of the Chinese carboncredit scheme are standardized and under public scrutiny.The proposed infrastructure has the potential to significantly enhance the transparency and efficiency of China’s carbon-credit schemes.
文摘In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.
基金Scientific Research Project of Higher Education Institutions in Hebei Province in 2025“Research on Government Procurement-Driven Green Governance of Hebei’s Manufacturing Industry”(Project No.:QN2025662)Social Science Fund of Hebei Province in 2024“Research on Informal Environmental Regulation Promoting Green Development of Hebei’s Manufacturing Industry”(Project No.:HB24GL036)Hebei Provincial Social Science Development Research Project,“Study on the Constraints and Implementation Paths of the Transformation from Dual Control of Energy Consumption to Dual Control of Carbon Emissions in Hebei Province”(Project No.:HBSKFZ25QN199)。
文摘Taking China’s 2018 value-added tax(VAT)credit refund reform as an exogenous shock to improve VAT neutrality,we use a difference-in-differences approach to explore how the reform affected corporate social responsibility(CSR).We find that the reform motivated firms to improve CSR performance.The reform has a“resource”effect,increasing internal funds and reducing financing costs,thereby enhancing firms’ability to undertake CSR.The reform also has a“reputation”effect,stimulating firms’willingness to engage in CSR to improve their reputations.CSR following the reform increases firm values and reduces bankruptcy risk.Our study provides fresh insights into VAT neutrality theory and is a reference for tax reform in emerging economies.
文摘The rapid development of digital financial inclusion is profoundly changing the financing environment for small and medium-sized enterprises(SMEs).As an important driver of economic growth and innovation,SMEs account for a significant share of employment and GDP globally.However,the traditional bank credit model has long failed to effectively meet the financing needs of SMEs due to issues such as information asymmetry,high cost,and difficulty in risk assessment,resulting in serious financing constraints.Digital financial inclusion,through technological innovation and big data analysis,has significantly reduced credit costs,alleviated information asymmetry,and provided SMEs with more flexible and efficient financing channels.Research shows that digital financial inclusion can not only ease the financing constraints of SMEs,but also promote their innovation and growth,providing important support for building a more inclusive and sustainable financial ecosystem.
文摘The rapid development of digital finance is profoundly changing the structure and management mode of bank credit.Through mobile banking,artificial intelligence,big data,cloud computing,and online lending platforms,banks are able to optimize credit services,increase efficiency,and improve access to credit[1].This evolution began in the late 20th century and accelerated after the 2008 global financial crisis.Through automated approval,precise risk assessment,and real-time monitoring,digital finance has improved credit efficiency,reduced costs,promoted financial inclusion,and enabled groups not covered by traditional financial services to gain support.However,the popularity of digital finance has also brought new challenges,such as consumer protection,cybersecurity,and fraud risks,and there is an urgent need to update the regulatory framework to address these issues.Nonetheless,the technological spillover effects of digital finance have promoted bank credit innovation and improved market competitiveness.This paper analyzes the role of digital finance in credit efficiency,cost,risk management,and financial inclusion,and puts forward policy recommendations to deal with potential risks and ensure the stability and sustainable development of the financial system.
基金financially supported by Doctoral Fellowship of the University Grants Commission,Bangladesh.
文摘This study examines the causal relationship between financial technology startup venture capital(VC)financing and its deals with domestic credit provided by the banking sector and equity market movement.Despite the rise of alternative finance,such as fintech venture capital(it is the fund that venture capital firms put into young,promising fintech companies so that they can help them expand and scale quickly),which is yet underexplored,borrowers still heavily rely on banks and the stock market for financing.We use panel data from 57 countries from 2010 to 2020 and an advanced econometric method called the cross-sectional autoregressive distributed lag model(CS-ARDL)to determine how the size and number of fintech equity funds dealt with by venture capital firms,banking sector credit,and stock market returns are interrelated at the global level and across regional,income,and economic levels.Our results reveal a cointegrating relationship between fintech venture capital funding and deals with bank loans and equity market returns.However,this relationship varies across the regions studied and between developed and developing economies.Our findings provide crucial guidelines for policymakers to create policies that support balanced financial development by highlighting the global interaction of equity market movements,banking credit,and fintech venture capital investment and lay the groundwork for internationally aligned policies to guarantee the optimal distribution of financial capital and improve economic stability and adaptability by illustrating how these links differ across geographical locations and economic conditions.
基金Natural Science Foundation of China(Key Program,No 72133003)Key Projects of Philosophy and Social Sciences Research,Ministry of Education,(Grant No.:22JZD008).
文摘The green credit policy(GCP)is an essential financial policy tool for solving the problem of environmental pollution,and urban energy conservation is an effective way to achieve the goal of carbon neutrality.However,existing research has not verified the energy-saving effects of green credit(GC)at the city level.Based on panel data from 283 cities in China,this study aims to investigate whether GC can effectively reduce urban energy intensity(EI),which is an important complement to existing research.In terms of research methods,to better evaluate the effect of the policy and exclude the influence of other relevant factors,this study considers the promulgation of the Green Credit Guideline(GCG)in 2012 as the basic event,uses the difference-in-differences(DID)model to investigate the impact of GC on EI,and discusses the main impact mechanism.The key results are follows.(1)GC can effectively reduce urban EI.(2)Public environmental demand positively regulates the negative correlation between GC and EI.(3)GC reduces EI through three main channels:government support,capital investment,and technological innovation;however,the mechanism of industrial structure has no significant effect.(4)The effect of GC is more significant in areas with large urban scales,low environmental regulation intensity,and high industrial agglomeration.Based on the above results,this study presents puts forward targeted policy recommendations to strengthen the role of GC in urban sustainable development.
文摘Under the impetus of the“Dual Credit”policy,traditional fuel vehicle manufacturers are confronted with significant pressure to meet new energy vehicle credit requirements.To address this challenge,these manufacturers are increasingly adopting the Original Design Manufacturer(ODM)strategy to collaborate with new energy vehicle enterprises,thereby acquiring credits and expanding their market presence.However,this strategic approach not only intensifies competition between new energy and traditional fuel vehicle markets but also reshapes the profit distribution between the two types of firms.Drawing upon the framework of the Dual Credit policy,this study establishes a Cournot game model to examine the strategic interactions between traditional fuel vehicle manufacturers and new energy vehicle producers.It further investigates the optimal production decisions under the ODM strategy and evaluates their implications for market dynamics and corporate profitability.The findings reveal that,although the ODM strategy heightens market competition,it leads to substantial profit improvements for both types of manufacturers compared to the alternative of directly purchasing credits,while also fostering the growth of the new energy vehicle sector.Moreover,the Case study demonstrates micro-level impact of the dual credit policy on enterprises’response strategies,offering valuable insights for policymakers and industry decision-makers.
基金supported by grants from the National Natural Science Foundation of China(No.72361014)the Technical Field Fund of Basic Research Strengthening Program(Project No.2021-JCJQ-JJ-0003)+1 种基金the Major Program of the National Social Science Foundation of China(No.19ZDA103)the Science and Technology Project of Jiangxi Provincial Department of Education(No.GJJ2200526).
文摘Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predictive performance.Additionally,data collected over a long time span may cause differences in the data distribution,and classic supervised learning methods may exhibit poor predictive abilities under such conditions.To address these issues,a domain-adaptation-based multistage ensemble learning paradigm(DAMEL)is proposed in this study to evaluate the credit risk of SMEs in SCF.In this methodology,a bagging resampling algorithm is first used to generate a dataset to address data scarcity.Subsequently,a random subspace is applied to integrate various features and reduce feature redundancy.Additionally,a domain adaptation approach is utilized to reduce the data distribution discrepancy in the cross-domain.Finally,dynamic model selection is developed to improve the generalization ability of the model in the fourth stage.A real-world credit dataset from the Chinese securities market was used to validate the effectiveness and feasibility of the multistage ensemble learning paradigm.The experimental results demonstrated that the proposed domain-adaptation-based multistage ensemble learning paradigm is superior to principal component analysis,joint distribution adaptation,random forest,and other ensemble and transfer learning methods.Moreover,dynamic model selection can improve the model generalization performance and prediction precision of minority samples.This can be considered a promising solution for evaluating the credit risk of SMEs in SCF for financial institutions.
基金supported by grants from the National Science Fund for Distinguished Young Scholars(NSFC No.71025005)the National Natural Science Foundation of China(NSFC No.71433001 and NSFC No.71301006)the National Program for Support of Top-Notch Young Professionals and the Fundamental Research Funds for the Central Universities in BUCT.
文摘To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs.This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory mechanisms.The study considers the credit of various economic agents within the social credit system such as countries(or governments),corporations,and individuals and their credit variations in online markets(i.e.,network credit).A historical review of the theoretical(or model)development of economic agents is presented together with significant works and future research directions.Some interesting conclusions are summarized from the literature review.(1)Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms.(2)The most popular credit scoring methods include expert systems,econometric models,artificial intelligence(AI)techniques,and their hybrid forms.Evaluation indexes should vary across different target agents.(3)The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.
文摘Background:This study examines the access to credit,credit investment,and credit fungibility for small-holder farmers and medium-and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh,Pakistan.Methods:A standardized questionnaire was used to collect data from 87 farmers in the Shikarpur District.We investigated the availability of credit and the use of credit fungibility by farmers with small-,medium-,and large-scale holdings by applying a credit fungibility ratio and an ANOVA technique.The factors that influence the farmers’access to agricultural credit were analyzed using a probit regression model.Results:The results revealed that farmers in both study groups used some amount of their agricultural credit for non-agricultural activities.Further,the results of the probit regression analysis showed that formal education,farming experience,household size,and farm size had a positive and significant influence on the farmers’access to agricultural credit.Conclusion:Based on these findings,our study suggests that a strong monitoring of farmers is needed in the study area.
基金funded by Research Grants Council,Hong Kong,Grant Number UGC/FDS14/B20/16the Hong Kong Polytechnic University,Grant Number P0030199.
文摘In this study,we compare the adjustments of credit ratings by an investor-paid credit rating agency(CRA),represented by Egan-Jones Ratings Company,and an issuer-paid CRA,represented by Moody’s Investors Service,vis-à-vis conflict of interest and reputation.A novel distribution dynamics approach is employed to compute the probability distribution and,hence,the downgrade and upgrade probabilities of a credit rating assigned by these two CRAs of different compensation systems based on the dataset of 750 U.S.issuers between 2011 and 2018,that is,after the passage of the Dodd–Frank Act.It is found that investor-paid ratings are more likely to be downgraded than issuerpaid ratings only in the lower rating grades,which is consistent with the argument that investor-paid agencies have harsher attitudes toward potentially defaulting issuers to protect their reputation.We do not find evidence that issuer-paid CRAs provide overly favorable treatments to issuers with threshold ratings,implying that reputation concerns and the Dodd–Frank regulation mitigate the conflict of interests,while issuerpaid CRAs are more concerned about providing accurate ratings.
文摘The study is focused on agricultural credit sources and determinants of credit acquisition by farmers in ldemili local government area of Anambra State, Nigeria with specific objectives to： describe socio-economic characteristics of rural farmers; identify sources of agricultural credit available to rural farmers; determine socio-economic factors that influence agricultural credit acquisition of farmers; ascertain reasons for any credit misappropriation and identify problems that constrain farmers from agricultural credit acquisition. Ninety farmers were randomly selected by multi stage random sampling technique. Semi-structured questionnaire was used to elicit data for the study. Descriptive statistics and multiple regression model were used in achieving the objectives. Results indicated that 74.44% of respondents were males with a mean age of 45 years. Majority （76.67%） were married with large house hold sizes. Majority （93.33%） received different level of education, with sources of credit from friends/relatives （30.00%）, cooperative societies （43.33%）, money lenders （14.44%）, and cumulatively from formal sources （（12.22%）. The result of the multiple regression analysis revealed age, household size, membership of cooperative societies, marital status, education level, farm size and amount of loan repaid at varied signs and levels as significant predictors of amount of agricultural credit acquired by farmers. The most common reason given among the respondents （55.89%） of those who misappropriated acquired agricultural credit, was meeting nonfood needs of the household. The farmers encountered problems of high interest rate （78.89%）, lack of collateral （75.56%）, long distance from source of credit （50.00%）, poor harvest （37.78%）, moratorium （33.33%） and delay in loan approval/disbursement （44.44%） as constraints to acquire credit. The study recommends that the state government should pass policies aimed at providing free educative seminars to all illiterate farmers to teach them possible ways and methods of acquiring credit. To ensure mass attendance to such seminars, little incentives should be given to farmer participants.
文摘The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.
文摘Credit scoring models are quantitative models used commonly by financial institutions in the measurement and forecasting of credit risk, having a consolidated use in the process of credit concession of these institutions. The purpose of this paper was to evaluate the possibility of application of credit scoring models in a microcredit institution named Fundo Rotativo de A~~o da Cidadania--Cred Cidadania （Revolving Fund of Citizenship Action--Cred Cidadania）, located in Recife （Brazil）. In order to do this, data related to a sample of clients of the Cred Cidadania was collected, and this data was used to develop two types of credit scoring models： one is credit approval, another is called behavioral scoring. The statistical technique applied in the construction of the models was logistic regression. The results of the study demonstrated that the credit scoring models obtain satisfactory performance when used in the analysis of credit risk in the Cred Cidadania microcredit institution, reaching a correct client classification percentile of about 80%. The results also indicate that the use of credit scoring models supplies subsidies to the institution, aiding it in the prevention and reduction of insolvency and in the decrease of its operational costs, two problems that affect their financial sustainability.
