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
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
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
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
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 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
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
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
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
Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approa...Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.展开更多
With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,res...With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.展开更多
In the financial sector,alternatives to traditional datasets,such as financial statements and Securities and Exchange Commission filings,can provide additional ways to describe the running status of businesses.Nontrad...In the financial sector,alternatives to traditional datasets,such as financial statements and Securities and Exchange Commission filings,can provide additional ways to describe the running status of businesses.Nontraditional data sources include individual behaviors,business processes,and various sensors.In recent years,alternative data have been leveraged by businesses and investors to adjust credit scores,mitigate financial fraud,and optimize investment portfolios because they can be used to conduct more in-depth,comprehensive,and timely evaluations of enterprises.Adopting alternative data in developing models for finance and business scenarios has become increasingly popular in academia.In this article,we first identify the advantages of alternative data compared with traditional data,such as having multiple sources,heterogeneity,flexibility,objectivity,and constant evolution.We then provide an overall investigation of emerging studies to outline the various types,emerging applications,and effects of alternative data in finance and business by reviewing over 100 papers published from 2015 to 2023.The investigation is implemented according to application scenarios,including business return prediction,business risk management,credit evaluation,investment risk prediction,and stock prediction.We discuss the roles of alternative data from the perspective of finance theory to argue that alternative data have the potential to serve as a bridge toward achieving high efficiency in financial markets.The challenges and future trends of alternative data in finance and business are also discussed.展开更多
Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.Howeve...Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.展开更多
Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients m...Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.展开更多
In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space...In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.展开更多
Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optim...Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optimal trade credit contracts when a risk-averse retailer hides its sales cost information(adverse selection)and selling effort level(moral hazard).We develop incentive models for a risk-averse supply chain when adverse selection and moral hazard coexist,which are then compared with the results under single information asymmetry(moral hazard).Moreover,we analyze the effects of private information and risk-aversion coefficient on contract parameters,selling effort level and the profit or utility of the supply chain.The study shows that when the degree of retailer’s risk aversion is within a certain range,reasonable trade credit contracts designed by the manufacturer can effectively induce the retailer to report its real sales cost and encourage it to exert appropriate effort.Furthermore,we find that the optimal trade credit period,optimal transfer payment,and retailer’s optimal sales effort level under dual information asymmetry are less than those under single information asymmetry.Numerical analysis are conducted to demonstrate the effects of the parameters on decisions and profits.展开更多
A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all...A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.展开更多
The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit an...The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.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.
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.
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 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.
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.
文摘Purpose:This study aims to evaluate the accuracy of authorship attributions in scientific publications,focusing on the fairness and precision of individual contributions within academic works.Design/methodology/approach:The study analyzes 81,823 publications from the journal PLOS ONE,covering the period from January 2018 to June 2023.It examines the authorship attributions within these publications to try and determine the prevalence of inappropriate authorship.It also investigates the demographic and professional profiles of affected authors,exploring trends and potential factors contributing to inaccuracies in authorship.Findings:Surprisingly,9.14%of articles feature at least one author with inappropriate authorship,affecting over 14,000 individuals(2.56%of the sample).Inappropriate authorship is more concentrated in Asia,Africa,and specific European countries like Italy.Established researchers with significant publication records and those affiliated with companies or nonprofits show higher instances of potential monetary authorship.Research limitations:Our findings are based on contributions as declared by the authors,which implies a degree of trust in their transparency.However,this reliance on self-reporting may introduce biases or inaccuracies into the dataset.Further research could employ additional verification methods to enhance the reliability of the findings.Practical implications:These findings have significant implications for journal publishers,Beyond authorship:Analyzing contributions in PLOS ONE and Maddi,A.,&the challenges of appropriate attribution highlighting the necessity for robust control mechanisms to ensure the integrity of authorship attributions.Moreover,researchers must exercise discernment in determining when to acknowledge a contributor and when to include them in the author list.Addressing these issues is crucial for maintaining the credibility and fairness of academic publications.Originality/value:This study contributes to an understanding of critical issues within academic authorship,shedding light on the prevalence and impact of inappropriate authorship attributions.By calling for a nuanced approach to ensure accurate credit is given where it is due,the study underscores the importance of upholding ethical standards in scholarly publishing.
文摘With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.
基金sponsored by the National Natural Science Foundation of China(72371032)the National Key Research and Development Program of China(2023YFC3305401).
文摘In the financial sector,alternatives to traditional datasets,such as financial statements and Securities and Exchange Commission filings,can provide additional ways to describe the running status of businesses.Nontraditional data sources include individual behaviors,business processes,and various sensors.In recent years,alternative data have been leveraged by businesses and investors to adjust credit scores,mitigate financial fraud,and optimize investment portfolios because they can be used to conduct more in-depth,comprehensive,and timely evaluations of enterprises.Adopting alternative data in developing models for finance and business scenarios has become increasingly popular in academia.In this article,we first identify the advantages of alternative data compared with traditional data,such as having multiple sources,heterogeneity,flexibility,objectivity,and constant evolution.We then provide an overall investigation of emerging studies to outline the various types,emerging applications,and effects of alternative data in finance and business by reviewing over 100 papers published from 2015 to 2023.The investigation is implemented according to application scenarios,including business return prediction,business risk management,credit evaluation,investment risk prediction,and stock prediction.We discuss the roles of alternative data from the perspective of finance theory to argue that alternative data have the potential to serve as a bridge toward achieving high efficiency in financial markets.The challenges and future trends of alternative data in finance and business are also discussed.
基金funding from the National Natural Science Foundation of China (72303086)the Leading Scientist Project of Qinghai Province, China (2023-NK-147)+1 种基金the Consulting Project of Chinese Academy of Engineering (2023-XY-28,2022-XY-139)the Fundamental Research Funds for the Central Universities, China (lzujbky-2022-sp13)
文摘Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.
基金supported by Key Research and Development Program of China (No.2022YFC3005401)Key Research and Development Program of Yunnan Province,China (Nos.202203AA080009,202202AF080003)+1 种基金Science and Technology Achievement Transformation Program of Jiangsu Province,China (BA2021002)Fundamental Research Funds for the Central Universities (Nos.B220203006,B210203024).
文摘Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.
基金supported by the Jiangsu University Philosophy and Social Science Research Project(Grant No.2019SJA1326).
文摘In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.
基金supported by the Plan Project of Shanghai Philosophy and Social Science(2017BGL014)the National Natural Science Foundation of China(71832001)the Fundamental Research Funds for the Central Universities(2232020B-04,2232018H-07).
文摘Trade credit,as an effective tool for integrating and coordinating material,information,and financial flows in supply chain management,is becoming increasingly widespread.We explore how a manufacturer can design optimal trade credit contracts when a risk-averse retailer hides its sales cost information(adverse selection)and selling effort level(moral hazard).We develop incentive models for a risk-averse supply chain when adverse selection and moral hazard coexist,which are then compared with the results under single information asymmetry(moral hazard).Moreover,we analyze the effects of private information and risk-aversion coefficient on contract parameters,selling effort level and the profit or utility of the supply chain.The study shows that when the degree of retailer’s risk aversion is within a certain range,reasonable trade credit contracts designed by the manufacturer can effectively induce the retailer to report its real sales cost and encourage it to exert appropriate effort.Furthermore,we find that the optimal trade credit period,optimal transfer payment,and retailer’s optimal sales effort level under dual information asymmetry are less than those under single information asymmetry.Numerical analysis are conducted to demonstrate the effects of the parameters on decisions and profits.
基金supported by the Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
基金funded by the Chongqing Social Sciences Planning Project (2023NDQN22)the Social Sciences and Philosophy Project of the Chongqing Municipal Education Commission (23SKGH097)the Youth Program of Science and Technology Research of Chongqing Municipal Education Commission (KJQN202300545)。
文摘The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.
