This paper is the first attempt to present the results form a pilot experimental research the authors conducted with high school students to examine their worldviews with regard to the implementation of the“credition...This paper is the first attempt to present the results form a pilot experimental research the authors conducted with high school students to examine their worldviews with regard to the implementation of the“credition”model in Religious Education.It was implemented in the 2nd grade of high school in selected topics.The research was held in the lessons of Religious Education during the whole school year.The authors examined the impact of the model on the students’worldviews when it is integrated with a worksheet,which was used as questionnaire(research tool).The topics to work with were selected from the expected learning results of the new curricula for Religious Education.The experimental research was qualitative semi-structured interview and aimed to examine,when the teacher implemented the“credition”model into her teaching,how this influenced the students and helped them realize their emotions,their strength and how they subsequently changed their attitudes and life choices and future orientation.The teaching with the model was implemented in the students of two different classrooms,one who had received explanations and guidelines beforehand and the other who had not received any explanation.The results showed that the students of the classroom who had received explanations realized better their emotions than the students of the other classroom who had not.The results were encouraging to make the authors repeat the research again this year to the 3rd grade students and proceed to the forming of a teaching model for working with the model in Religious Education.展开更多
This study explores the housing distribution effect of the Housing Provident Fund(HPF)system on households.Utilizing data from the China Household Finance Survey conducted in 2013,2015,2017,and 2019,this study empiric...This study explores the housing distribution effect of the Housing Provident Fund(HPF)system on households.Utilizing data from the China Household Finance Survey conducted in 2013,2015,2017,and 2019,this study empirically investigates this effect from two dimensions:the impact of HPF payments on household income and housing loan behavior,and the impact on the Gini coefficient of housing assets within the group.The results indicate that the HPF payment does not significantly impact household income levels.Instead,it increases their likelihood of obtaining housing loans,particularly for middle-income households.Additionally,for any group of families,the HPF payment decreased the housing Gini coefficient among households by approximately 0.11 units.This study may be the first to provide the most direct empirical evidence on the extent of HPF’s impact on housing inequality within a group.It also questions the inference in the literature that“HPF will aggravate housing inequality within the group.”Based on this study’s findings,we expect that housing inequality will continue to be alleviated with continued HPF implementation.In particular,this effect will be more significant if the credit support for lowincome families to purchase houses through the HPF can be further enhanced.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
The recognition and transformation of learning outcomes is a key step in building a lifelong learning pathway to meet the personalized and diverse learning and development needs of individuals.In response to the probl...The recognition and transformation of learning outcomes is a key step in building a lifelong learning pathway to meet the personalized and diverse learning and development needs of individuals.In response to the problems of incomplete systems,complex processes,and inadequate quality monitoring in the recognition and conversion of learning outcomes at Guangdong Polytechnic of Science and Technology,suggestions are proposed to optimize management methods,standardize the process of recognition and conversion of learning outcomes,build an information platform for recognition and conversion of learning outcomes,and rely on vocational education groups to continuously standardize and orderly carry out recognition and conversion of learning outcomes.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
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
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
A briefing on policy was held by the State Council Information Office of China on February 20.At the briefing,Zhou Weijun,Director General of the Credit Supervision and Management Department,State Administration for M...A briefing on policy was held by the State Council Information Office of China on February 20.At the briefing,Zhou Weijun,Director General of the Credit Supervision and Management Department,State Administration for Market Regulation(SAMR),expounded the policy on the participation of foreign-owned enterprises in the development and revision of standards for the large-scale equipment upgrade and consumer goods trade-in programs.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
Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international e...Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international economic and trade situation,commercial banks in China's mainland have entered a stage of low interest rates and narrow interest margins.Coupled with the continuous exposure of risks in retail customer groups and small and micro enterprises,many commercial banks have chosen to phase in expanding and strengthening their corporate business segments to smoothly navigate economic cycles and enhance operational resilience and sustainability.How the corporate business segment optimizes its asset-liability structure through asset allocation to achieve high-quality development is a major issue worthy of consideration by the entire industry.From the perspective of a medium-sized national commercial bank,this paper explores and proposes four key basic customer groups,six asset allocation models,and fourteen key industries for layout,for reference,and research.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
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 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
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
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
In the era of the digital economy,traditional supply chain finance models face challenges such as information fragmentation,inefficient processes,and insufficient credit transmission,necessitating digital transformati...In the era of the digital economy,traditional supply chain finance models face challenges such as information fragmentation,inefficient processes,and insufficient credit transmission,necessitating digital transformation.This study focuses on Kweichow Moutai Group,systematically analyzing its innovative practices in supply chain finance and examining the mechanisms through which digital technologies enhance core enterprise credit empowerment,improve supply chain collaboration efficiency,and optimize risk management.The research reveals that Moutai Group has transformed supply chain finance from unilateral credit granting to ecosystem-based credit sharing by establishing an IoT-enabled asset verification platform,developing smart contract-driven bill financing systems,and building a blockchain-based multi-party credit alliance.This model significantly lowers financing barriers for small and medium-sized suppliers while creating new value chain growth points through data assetization strategies.Finally,the study proposes further improvements from the perspectives of technical standardization and adaptive regulatory frameworks.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
This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existenc...This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.展开更多
文摘This paper is the first attempt to present the results form a pilot experimental research the authors conducted with high school students to examine their worldviews with regard to the implementation of the“credition”model in Religious Education.It was implemented in the 2nd grade of high school in selected topics.The research was held in the lessons of Religious Education during the whole school year.The authors examined the impact of the model on the students’worldviews when it is integrated with a worksheet,which was used as questionnaire(research tool).The topics to work with were selected from the expected learning results of the new curricula for Religious Education.The experimental research was qualitative semi-structured interview and aimed to examine,when the teacher implemented the“credition”model into her teaching,how this influenced the students and helped them realize their emotions,their strength and how they subsequently changed their attitudes and life choices and future orientation.The teaching with the model was implemented in the students of two different classrooms,one who had received explanations and guidelines beforehand and the other who had not received any explanation.The results showed that the students of the classroom who had received explanations realized better their emotions than the students of the other classroom who had not.The results were encouraging to make the authors repeat the research again this year to the 3rd grade students and proceed to the forming of a teaching model for working with the model in Religious Education.
基金funded by the Humanities and Social Sciences Research Project of the Ministry of Education of China(Project No.23XJC790007).
文摘This study explores the housing distribution effect of the Housing Provident Fund(HPF)system on households.Utilizing data from the China Household Finance Survey conducted in 2013,2015,2017,and 2019,this study empirically investigates this effect from two dimensions:the impact of HPF payments on household income and housing loan behavior,and the impact on the Gini coefficient of housing assets within the group.The results indicate that the HPF payment does not significantly impact household income levels.Instead,it increases their likelihood of obtaining housing loans,particularly for middle-income households.Additionally,for any group of families,the HPF payment decreased the housing Gini coefficient among households by approximately 0.11 units.This study may be the first to provide the most direct empirical evidence on the extent of HPF’s impact on housing inequality within a group.It also questions the inference in the literature that“HPF will aggravate housing inequality within the group.”Based on this study’s findings,we expect that housing inequality will continue to be alleviated with continued HPF implementation.In particular,this effect will be more significant if the credit support for lowincome families to purchase houses through the HPF can be further enhanced.
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Guangdong Provincial Education Science Planning Project:Strategic Research on the Implementation Path of“Optimizing the Positioning of Vocational Education Types”in Guangdong Province in the New Era(2023GXJK738)Research and Practice Project on Education and Teaching Reform:Exploration of Collaborative Innovation and Development of Curriculum,Resources,and Textbooks in Higher Vocational Colleges。
文摘The recognition and transformation of learning outcomes is a key step in building a lifelong learning pathway to meet the personalized and diverse learning and development needs of individuals.In response to the problems of incomplete systems,complex processes,and inadequate quality monitoring in the recognition and conversion of learning outcomes at Guangdong Polytechnic of Science and Technology,suggestions are proposed to optimize management methods,standardize the process of recognition and conversion of learning outcomes,build an information platform for recognition and conversion of learning outcomes,and rely on vocational education groups to continuously standardize and orderly carry out recognition and conversion of learning outcomes.
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.
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A briefing on policy was held by the State Council Information Office of China on February 20.At the briefing,Zhou Weijun,Director General of the Credit Supervision and Management Department,State Administration for Market Regulation(SAMR),expounded the policy on the participation of foreign-owned enterprises in the development and revision of standards for the large-scale equipment upgrade and consumer goods trade-in programs.
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.
文摘Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international economic and trade situation,commercial banks in China's mainland have entered a stage of low interest rates and narrow interest margins.Coupled with the continuous exposure of risks in retail customer groups and small and micro enterprises,many commercial banks have chosen to phase in expanding and strengthening their corporate business segments to smoothly navigate economic cycles and enhance operational resilience and sustainability.How the corporate business segment optimizes its asset-liability structure through asset allocation to achieve high-quality development is a major issue worthy of consideration by the entire industry.From the perspective of a medium-sized national commercial bank,this paper explores and proposes four key basic customer groups,six asset allocation models,and fourteen key industries for layout,for reference,and research.
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.
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 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.
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In the era of the digital economy,traditional supply chain finance models face challenges such as information fragmentation,inefficient processes,and insufficient credit transmission,necessitating digital transformation.This study focuses on Kweichow Moutai Group,systematically analyzing its innovative practices in supply chain finance and examining the mechanisms through which digital technologies enhance core enterprise credit empowerment,improve supply chain collaboration efficiency,and optimize risk management.The research reveals that Moutai Group has transformed supply chain finance from unilateral credit granting to ecosystem-based credit sharing by establishing an IoT-enabled asset verification platform,developing smart contract-driven bill financing systems,and building a blockchain-based multi-party credit alliance.This model significantly lowers financing barriers for small and medium-sized suppliers while creating new value chain growth points through data assetization strategies.Finally,the study proposes further improvements from the perspectives of technical standardization and adaptive regulatory frameworks.
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.
文摘This study investigates the determinants that drive the volatility of the credit default swaps(CDS)of BRICIT(Brazil,Russia,India,China,Indonesia,and Turkey)nations as a proxy measure for sovereign risk.On the existence of cointegration,an unrestricted error correction model integrated with the autoregressive distributed lag(ARDL)model is applied to measure the short-run and long-run dynamics empirically.The study utilizes the Bayesian global vector autoregression methodology for cross-border spillover estimation.The study also suggests a strategy for policymakers for quadrant categorization to mitigate risk arising from cross-border spillover.The result of ARDL indicates that the global macroeconomic variables affect the BRICIT CDS more than domestic macroeconomic determinants,with Indian CDS being the most sensitive to Fed tapering.Notably,China’s CDS is the most sensitive to shocks,with the CDS volatility primarily driven by China’s geopolitical risk.Russian CDS is more sensitive to real effective exchange rates due to severe ruble depreciation than crude oil,despite Russia being a major oil exporter.The quadrant categorization indicates that the Indonesian stock market index is most interconnected with BRICIT CDS,while the Turkish long-term interest rates send the highest intensity spillover across BRICIT nations.
