Raft Consensus Algorithm Based on Credit Model in Consortium Blockchain

As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and efficiency.As a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and fork.However,Raft can only be used in a non-byzantine environment with a small network size.In order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model CRaft.In the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is constructed.Then the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network environment.Finally,the common node is synchronized to the consensus node to update the blockchain of the entire network.Experiments show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).

Empirical Study on Credit Risk of Our Listed Company Based on KMV Model

KMV model is one of the most important credit risk evaluation models in the world. It uses B-S option pricing and Morton formula based on the market value and volatility of the company’s equity, debt maturities, risk-free interest rates and the book value of liabilities to estimate the market value of the company’s assets and the volatility of the asset value. In this paper, based on the theory of KMV model, we can derive the listed company’s default rate, and assess credit risk. And the result is reasonable.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

Small-and medium-sized enterprises(SMEs)have a crucial influence on the economic development of every nation,but access to formal finance remains a barrier.Similarly,financial institutions encounter challenges in the assessment of SMEs’creditworthiness for the provision of financing.Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements.SMEs are perceived as unorganized in terms of financial data management compared to large corporations,making the assessment of credit risk based on inadequate financial data a cause for financial institutions’concern.The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions.To address the issue of limited financial record keeping,this study developed and validated a system to predict SMEs’credit risk by introducing a multicriteria credit scoring model.The model was constructed using a hybrid best–worst method(BWM)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).Initially,the BWM determines the weight criteria,and TOPSIS is applied to score SMEs.A real-life case study was examined to demonstrate the effectiveness of the proposed model,and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations.The findings indicated that SMEs’credit history,cash liquidity,and repayment period are the most crucial factors in lending,followed by return on capital,financial flexibility,and integrity.The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults.This model could assist financial institutions,providing a simple means for identifying potential SMEs to grant credit,and advance further research using alternative approaches.

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.

Research on Credit Risk Measurement Based on Uncertain KMV Model

Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s national conditions. In this paper, we will amend the model by using uncertain interest rate instead of fixed rate on the basis of existing research. Comparing the uncertain KMV model to traditional KMV model with ST-listed companies and non-ST-listed companies in Shanghai and Shenzhen stock exchange, we find that it performs slightly better as a predictor in uncertain KMV model and in out of sample forecasts.

Fuzzy-Neuro Model for Intelligent Credit Risk Management

This paper presents hybrid fuzzy logic and neural network algorithm to solve credit risk management problem. Credit risk is the risk of loss due to a debtor’s non-payment of a loan or other line of credit. A method of evaluating the credit worthiness of a customer is complex and non-linear due to the diverse combinations of risk involve. To address this problem a credit scoring method is proposed in this paper using hybrid fuzzy logic-neural network (HFNN) model. The model will be implemented, tested, and validated for individual auto loans using real life bank data. The neural network is used as the learner and the fuzzy logic is used as the implementer. The neural network will fine tune the fuzzy sets, remove redundant input variables, and extract fuzzy rules. The extracted fuzzy rules are evaluated to retain the best k number of rules that will give final and intelligent decisions. The experiment results show that the perform-ance of the proposed HFNN model is very accurate, robust, and reliable. Comparison of these results to other previous published works is also presented in this paper.

The Application of Genetic Algorithms and Multidimensional Distinguishing Model in Forecasting and Evaluating Credits for Mobile Clients

To solve the arrearage problem that puzzled most of the mobile corporations, we propose an approach to forecast and evaluate the credits for mobile clients, devising a method that is of the coalescence of genetic algorithm and multidimensional distinguishing model. In the end of this paper, a result of a testing application in Zhuhai Branch, GMCC was provided. The precision of the forecasting and evaluation of the client’s credit is near 90%. This study is very significant to the mobile communication corporation at all levels. The popularization of the techniques and the result would produce great benefits of both society and economy.

Research on the Influencing Factors of Personal Credit Based on a Risk Management Model in the Background of Big Data

Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.

Valuing Credit Default Swap under a double exponential jump diffusion model

This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.

Relationship Between Agricultural Credits and Agricultural Economy Based on Error Correct Model in Heilongjiang Province

Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.

社会信用治理模型选择:由风险预测制迈向行为积分制

模型是对实践的抽象化表达,内含实践的认知理念和价值判断。不同信用模型内含不同的信用治理理念并会产生不同的社会效果。风险预测制模型用于风险预测和秩序控制,在实际社会信用治理中产生过度收集数据、模型黑箱化、难以有效预测等问题。究其原因,在于技术设计过程未能有效反映治理需求,反而消解了人的主体性地位。社会信用治理需要从行为改变出发,迈向行为导向,转换治理范式。行为积分制模型以行为理论为支撑,通过将社区治理过程中的日常事项转化为积分激励事项,推动社区居民积极参与到社区经济、政治、社会、文化、生态等建设中,以共建推动共同治理,将共治行为转化为积分,进而实现治理成果的共享,提供了民主治理的新范式。在推动信用体系建设过程中,应更多考虑行为积分制模型的应用和推广。

Optimal Policy for EOQ Model with Two Level of Trade Credits in One Replenishment Cycle

In general, a supplier/retailer frequently offer trade credit to stimulate their respective sales. The main purpose of this paper is to investigate the optimal supplier/retailer’s replenishment decisions under two levels of trade credit policy within the economic order quantity (EOQ) framework. This paper deals with the supplier/retailer’s inventory replenishment problem under two levels of trade credit in one replenishment cycle. A different approach of two levels of trade credit is used, which give more freedom to the supplier/retailer in business. In addition, the easy-to-use procedure is developed to efficiently find the optimal cycle time for the retailer under minimizing annual total relevant cost. Finally, a numerical example is given to illustrate these results.

我国公司债违约的影响因素研究:基于产权和行业的视角

自从2014年我国第一家公司债违约,我国公司债打破刚性兑付,公司债违约事件逐步增加。债券违约风险的管理与预测已成为亟待解决的重要问题。本文以我国上市公司2008—2020年发行的公司债为样本,从产权属性和所在行业的角度出发,控制公司财务与债券特征因素,研究我国公司债违约风险的影响因素并进行预测检验。Logit回归与平均边际效应检验结果表明,民营企业与制造业上市公司具有较高的违约概率,上市公司净资产增长率和所在地区经济增长水平均与违约概率呈显著负相关,而信用评级和到期期限与违约无显著关联。ROC曲线检验结果表明,本文模型能够有效预测违约风险。

数字普惠金融背景下信用风险溢价对实际利率的影响——基于多重门槛模型的分析与行为经济学的解释

金融市场活动容易受到人们感觉认知的影响。在数字普惠金融快速发展的背景下,数字普惠金融发展状况成为人们在信贷市场上能够动态感知的、越来越重要的信号。根据信号的同时对比与继时对比,人们有可能改变经济决策与行为。研究结果表明:不仅数字普惠金融能够通过信用传导机制及多重门槛效应对实际利率施加显著影响,而且门槛变量表现出显著的时滞效应。在信贷市场上,人们关于数字普惠金融的体验极有可能具备感觉阈限与感觉适应的特征。数字普惠金融发展能减弱利率市场对于信用风险溢价的反应。随着数字普惠金融处于越来越快的发展阶段,人们在面对信用风险时表现得越来越乐观。

绿色信贷政策是否有效降低了重污染企业信贷风险

绿色信贷政策承担着引导金融资源流入绿色发展领域、推进企业转型升级的双重任务,信贷风险管理是其实现任务的重要环节。文章根据2012年1月原银监会出台的《绿色信贷指引》,运用KMV模型测度重污染企业信贷风险即违约距离(DD),并构建双重差分模型(DID)实证分析绿色信贷政策是否有效降低重污染企业信贷风险以及验证其影响机制。实证结果显示,重污染行业间违约距离差异不显著,行业内违约距离总体上呈增加趋势,即信贷风险降低;《绿色信贷指引》的实施通过调整债务期限结构、提升环境信息披露质量显著降低重污染企业信贷风险,且国有企业、金融发展程度较高的地区企业对其更敏感。推动绿色信贷政策规范化、制度化及长效化实施,对增强银行信贷风险管控能力、推动企业高质量发展具有一定参考意义。

绿色信贷政策促进企业绿色技术创新了吗?

我国现代化建设进入了生态文明发展新阶段,企业绿色技术创新是“减污降碳协同增效”的关键动力,绿色信贷政策是否以及在多大程度上促进了企业绿色技术创新需要进行量化衡量,旨为绿色信贷政策更有效地促进企业绿色技术创新。在分析绿色信贷政策对企业绿色技术创新作用机理、归纳近些年绿色信贷政策与企业绿色技术创新的典型事实后,基于2007~2019年间A股上市工业企业样本数据,以受政策支持的绿色企业为实验组,未受支持的非绿色企业为控制组,利用PSM-DID模型开展准自然实验,得出绿色信贷政策对企业绿色技术创新具有显著的正向促进作用;并通过中介效应模型检验了促进作用是通过信贷规模与融资成本两种渠道缓解了企业融资约束;且通过异质性分析分别细化了政策因企业区位、企业所有权、企业规模的促进作用程度差异;最后,基于研究结论提出相应的对策建议。

基于KMV模型的新能源汽车行业信用风险研究

随着环境问题越来越受到关注,新能源汽车产业逐渐成为全球关注的焦点。我国政府对新能源汽车产业的发展高度重视,对其优化产业结构、促进能源转型、改善环境质量方面具有重要意义。文章拟从信用风险的角度出发,选取我国具有代表性的20家新能源汽车上市公司以及近5年的财务数据和股票数据,运用KMV模型对新能源汽车上市企业的信用风险水平进行评估,计算其违约距离。根据实证结果分析新能源汽车行业的信用风险状况,并提出改善风险防控的建议。

“留抵退税”新政对企业经营绩效分析与政策建议——以医疗制造业上市公司为例

受多种因素的影响,企业作为增值税一般纳税人,由于增值税税制的链条性设计,增值税的进项留抵税额具有客观性和普遍性。为遵循国际通行的做法,2018年以来我国通过实施并扩围增值税增量留抵退税制度,以减少待抵扣进项税额占用企业自有资金问题,2022年又开始实施存量留抵退税政策。为验证该政策对企业经营绩效的实施效果,采用双重差分模型(di d),对92家医疗制造业上市公司的若干主要财务指标进行了实证研究。研究发现,增值税留抵退税对企业的经营业绩有明显地提升作用。因此,留抵退税新政应进一步扩大其实施范围,进而不断提升企业经营绩效,助力实现高质量发展。

培养创新创业能力的职业教育模式研究

如何更加高效地培养学生创新创业能力,探索出既不烦琐又可操作且广泛适用的职业教育模式,是现阶段各大高职院校亟须解决的重要课题。为进一步促进高职院校培养创新创业能力的职业教育模式改革与发展,对现有高职创新创业教育模式存在的弊端进行分析,提出“厚植学生创新创业观、完善高职院校创新创业教育体系、优化高职院校创新创业实践体系、建立健全创新创业能力培养评价机制”等策略,助力职业教育高质量发展,培养出理念先进、技术扎实、素养过硬的新时代复合型人才。