To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with mini...To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs.This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory mechanisms.The study considers the credit of various economic agents within the social credit system such as countries(or governments),corporations,and individuals and their credit variations in online markets(i.e.,network credit).A historical review of the theoretical(or model)development of economic agents is presented together with significant works and future research directions.Some interesting conclusions are summarized from the literature review.(1)Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms.(2)The most popular credit scoring methods include expert systems,econometric models,artificial intelligence(AI)techniques,and their hybrid forms.Evaluation indexes should vary across different target agents.(3)The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.展开更多
To solve the high-dimensionality issue and improve its accuracy in credit risk assessment,a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection.The proposed p...To solve the high-dimensionality issue and improve its accuracy in credit risk assessment,a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection.The proposed paradigm consists of three main stages:categorization of high dimensional data,high-dimensionality-trait-driven feature extraction,and high-dimensionality-trait-driven classifier selection.In the first stage,according to the definition of high-dimensionality and the relationship between sample size and feature dimensions,the high-dimensionality traits of credit dataset are further categorized into two types:100<feature dimensions<sample size,and feature dimensions≥sample size.In the second stage,some typical feature extraction methods are tested regarding the two categories of high dimensionality.In the final stage,four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits.For the purpose of illustration and verification,credit classification experiments are performed on two publicly available credit risk datasets,and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.展开更多
Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting for...Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.展开更多
Blockchain has attracted much attention in recent years with the development of cryptocurrency and digital assets.As the underlying technology of cryptocurrency,blockchain has numerous benefits,such as decentralizatio...Blockchain has attracted much attention in recent years with the development of cryptocurrency and digital assets.As the underlying technology of cryptocurrency,blockchain has numerous benefits,such as decentralization,collective maintenance,tamper-resistance,traceability,and anonymity.The potential of the blockchain technology(BT)is widely recognized in the financial field.Although some scholars have proposed the combination of blockchain and supply chain finance(SCF),the details of this combination is rarely mentioned.This study first analyzes the coupling between SCF and blockchain technology.Second,the conceptual framework of blockchain-driven SCF platform(BcSCFP)is presented.Third,the operation process of three SCF models on the BcSCFP is proposed.Finally,a case study combined with actual events is conducted.This paper has a positive practical significance in the operation and management of banks and loan enterprises.展开更多
In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk e...In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.展开更多
This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively co...This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns,rare events,and an SIR.Using the model,we deduce the pricing characteristic function and pricing formula of a European option.Then,we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV.For verification purposes,we conduct time efficiency analysis,goodness of fit analysis,and jump/drift term analysis of the proposed model.In addition,we compare the pricing accuracy of the proposed model with those of the Black-Scholes and the Kou(2002)models.The empirical results show that the proposed option pricing model has high time efficiency,and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.展开更多
In communication networks （CNs）, the uncertainty is caused by the dynamic nature of the traffic demands. Therefore there is a need to incorporate the uncertainty into the network bandwidth capacity design. For this ...In communication networks （CNs）, the uncertainty is caused by the dynamic nature of the traffic demands. Therefore there is a need to incorporate the uncertainty into the network bandwidth capacity design. For this purpose, this paper developed a fuzzy methodology for network bandwidth design under demand uncertainty. This methodology is usually used for offiine traffic engineering optimization, which takes a centralized view of bandwidth design, resource utilization, and performance evaluation. In this proposed methodology, uncertain traffic demands are first handled into a fuzzy number via a fuzzification method. Then a fuzzy optimization model for the network bandwidth allocation problem is formulated with the consideration of the trade-off between resource utilization and network performance. Accordingly, the optimal network bandwidth capacity can be obtained by maximizing network revenue in CNs. Finally, an illustrative numerical example is presented for the purpose of verification.展开更多
基金supported by grants from the National Science Fund for Distinguished Young Scholars(NSFC No.71025005)the National Natural Science Foundation of China(NSFC No.71433001 and NSFC No.71301006)the National Program for Support of Top-Notch Young Professionals and the Fundamental Research Funds for the Central Universities in BUCT.
文摘To avoid credit fraud,social credit within an economic system has become an increasingly important criterion for the evaluation of economic agent activity and guaranteeing the development of a market economy with minimal supervision costs.This paper provides a comprehensive review of the social credit literature from the perspectives of theoretical foundation,scoring methods,and regulatory mechanisms.The study considers the credit of various economic agents within the social credit system such as countries(or governments),corporations,and individuals and their credit variations in online markets(i.e.,network credit).A historical review of the theoretical(or model)development of economic agents is presented together with significant works and future research directions.Some interesting conclusions are summarized from the literature review.(1)Credit theory studies can be categorized into traditional and emerging schools both focusing on the economic explanation of social credit in conjunction with creation and evolution mechanisms.(2)The most popular credit scoring methods include expert systems,econometric models,artificial intelligence(AI)techniques,and their hybrid forms.Evaluation indexes should vary across different target agents.(3)The most pressing task for regulatory mechanisms that supervise social credit to avoid credit fraud is the establishment of shared credit databases with consistent data standards.
基金This work is partially supported by grants from the Key Program of National Natural Science Foundation of China(NSFC Nos.71631005 and 71731009)the Major Program of the National Social Science Foundation of China(No.19ZDA103).
文摘To solve the high-dimensionality issue and improve its accuracy in credit risk assessment,a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection.The proposed paradigm consists of three main stages:categorization of high dimensional data,high-dimensionality-trait-driven feature extraction,and high-dimensionality-trait-driven classifier selection.In the first stage,according to the definition of high-dimensionality and the relationship between sample size and feature dimensions,the high-dimensionality traits of credit dataset are further categorized into two types:100<feature dimensions<sample size,and feature dimensions≥sample size.In the second stage,some typical feature extraction methods are tested regarding the two categories of high dimensionality.In the final stage,four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits.For the purpose of illustration and verification,credit classification experiments are performed on two publicly available credit risk datasets,and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.
基金the National Natural Science Foundation of China under Grant Nos.70601029 and 70221001the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant Nos.3547600,3046540,and 3047540the Strategy Research Grant of City University of Hong Kong under Grant No.7001806
文摘Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.
基金the National Natural Science Foundation of China(Grant Nos.71932002 and 71571010)Great Wall Scholar Training Program of Beijing Municipality(CIT&TCD20180305).
文摘Blockchain has attracted much attention in recent years with the development of cryptocurrency and digital assets.As the underlying technology of cryptocurrency,blockchain has numerous benefits,such as decentralization,collective maintenance,tamper-resistance,traceability,and anonymity.The potential of the blockchain technology(BT)is widely recognized in the financial field.Although some scholars have proposed the combination of blockchain and supply chain finance(SCF),the details of this combination is rarely mentioned.This study first analyzes the coupling between SCF and blockchain technology.Second,the conceptual framework of blockchain-driven SCF platform(BcSCFP)is presented.Third,the operation process of three SCF models on the BcSCFP is proposed.Finally,a case study combined with actual events is conducted.This paper has a positive practical significance in the operation and management of banks and loan enterprises.
基金This research was partially supported by the National Natural Science Foundation of China under Grant Nos.70221001,70701035the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant Nos.3547600,3046540,3047540+1 种基金the Key Research Institute of Philosophies and Social Sciences in Hunan Universitiesthe National Natural Science Foundation of China/Research Grants Council (RGC) of Hong Kong Joint Research Scheme under Grant No.N_CityU110/07.
文摘In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.
基金supported by the grants from the National Natural Science Foundation of China(NSFC No.71471161)the Key Programs of the National Natural Science Foundation of China(NSFC Nos.71631005 and 71433001)+1 种基金the National Natural Science Foundation of China(NSFC No.71703142)Zhejiang College StudentsʹScience Innovation Project(Xin Miao Project)on“Research on Integrated Risk Measurement of Structured Financial Products Based on Affine Jump Diffusion Process”(No.2016R414069).
文摘This paper proposes an efficient option pricing model that incorporates stochastic interest rate(SIR),stochastic volatility(SV),and double exponential jump into the jump-diffusion settings.The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns,rare events,and an SIR.Using the model,we deduce the pricing characteristic function and pricing formula of a European option.Then,we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV.For verification purposes,we conduct time efficiency analysis,goodness of fit analysis,and jump/drift term analysis of the proposed model.In addition,we compare the pricing accuracy of the proposed model with those of the Black-Scholes and the Kou(2002)models.The empirical results show that the proposed option pricing model has high time efficiency,and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.
基金partially supported by the grants from the National Natural Science Foundation of Chinathe Knowledge Innovation Program of the Chinese Academy of Sciences+1 种基金the GRANT-IN-AID FOR SCIEN-TIFIC RESEARCH (No. 19500070)MEXT.ORC (2004-2008), Japan
文摘In communication networks （CNs）, the uncertainty is caused by the dynamic nature of the traffic demands. Therefore there is a need to incorporate the uncertainty into the network bandwidth capacity design. For this purpose, this paper developed a fuzzy methodology for network bandwidth design under demand uncertainty. This methodology is usually used for offiine traffic engineering optimization, which takes a centralized view of bandwidth design, resource utilization, and performance evaluation. In this proposed methodology, uncertain traffic demands are first handled into a fuzzy number via a fuzzification method. Then a fuzzy optimization model for the network bandwidth allocation problem is formulated with the consideration of the trade-off between resource utilization and network performance. Accordingly, the optimal network bandwidth capacity can be obtained by maximizing network revenue in CNs. Finally, an illustrative numerical example is presented for the purpose of verification.
