The Impact of“Fintech+Financial Regulation”Matching Level on Financial Risk:Evidence from China

Preventing financial risk is an important topic that academic circles and the government have paid attention to for a long time.The development of fintech and the improvement of financial regulation will affect the level of financial risk.The relationship between the degree of matching between fintech and financial regulation and financial risk is explored,which is crucial for reducing financial risk.Panel data from 31 provinces in China from 2011 to 2020 is used to explore the impact of fintech and financial regulatory matching levels on financial risk.The study finds that the improved matching level between fintech and financial regulation helps reduce financial risk.The degree of matching between fintech and financial regulation affects financial risk through financial efficiency.

Financial Risk Evaluation System in the Context of Great Intelligence Movement Cloud: A Hierarchical Analysis Approach

This paper examines the financial risk evaluation system in the context of the“Great Intelligence Movement Cloud”and employs the hierarchical analysis method as the primary research tool.With the rapid advancement of“Great Intelligence Movement Cloud”technology,enterprise financial risks have expanded from the offline domain to the information domain,encompassing a broader scope and more diverse channels.The traditional approach to risk identification,relying solely on single financial indicators,no longer meets current demands.Therefore,it is essential to integrate a non-financial early warning indicator system and adopt a combination of quantitative and qualitative analysis methods.Hierarchical analysis enables the decomposition of complex problems into multiple components and organizes them into a hierarchical structure based on their relationships,facilitating a more accurate assessment of financial risk.This study seeks to establish a comprehensive financial risk evaluation system suited to the“Great Intelligence Movement Cloud”context,offering enterprises more precise risk assessments to better address financial risks and achieve steady development.

Digital Transformation and Corporate Financial Risk:Evidence from Listed Companies in

As a core issue in enterprise operation management,corporate financial risk is directly related to the survival and development of enterprises,and digital transformation has brought new challenges to the control of corporate financial risk.Based on the data of The Shanghai Stock Exchange(SSE)and Shenzhen Stock Exchange(SZSE)from 2009 to 2022,this paper analyzes the impact of digital transformation on corporate financial risk and the impact mechanism.The empirical study finds that digital transformation significantly increases the financial risk of enterprises,and shows differences among different regions,different risk factors,and enterprise natures,and substantially increases the corporate financial risk in the East and West,non-state-owned enterprises,high-risk and low-risk enterprises.The mechanism analysis found that digital transformation would affect the financial risk of enterprises by increasing their R&D investment and reducing their debt level.The conclusion improves insights and guidance for analyzing and managing financial risks in enterprises under digital transformation.

Research on Financial Risk Management in the S Water Conservancy Project and Its Implications for Similar Projects

As a vital infrastructure initiative with significant impacts on national development and public welfare,water conservancy projects play an essential role in advancing agricultural development and mitigating losses caused by flooding.However,these projects often require substantial investments and extended construction periods,leading to the inevitable emergence of various financial risks during implementation.This paper aims to explore the current challenges in financial risk management of the S water conservancy project,offering practical management strategies and solutions to address these financial difficulties.Through a detailed assessment and analysis of the S water conservancy project,the study seeks to provide valuable references and insights for similar projects to better manage financial risks,ensuring their smooth progression and successful completion.The proposed control measures are designed not only to effectively mitigate the financial risks specific to the S water conservancy project but also to offer actionable lessons for other similar projects.By promoting and applying these measures,the overall financial management capabilities of the industry can be enhanced,providing robust support for the sustainable development of water conservancy projects.

The Innovation Research of Financial Early-Warning Index Measurement

The period economic fluctuation is vital for an enterprise to exist and further develop, it directly affect the enterprise financial health. So, it is significant to build up financial early-warning index and measure the warning condition that the enterprise faces and take the effective measures to eliminate. We criticize Altman’sZ calculating model and build up some new indexes for enterprise financial early-warning condition measuring and making sound decision.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Performance-based seismic financial risk assessment of reinforced concrete frame structures

Engineering facilities subjected to natural hazards(such as winds and earthquakes) will result in risk when any designed system(i.e.capacity) will not be able to meet the performance required(i.e.demand).Risk might be expressed either as a likelihood of damage or potential financial loss.Engineers tend to make use of the former(i.e.damage).Nevertheless,other non-technical stakeholders cannot get useful information from damage.However,if financial risk is expressed on the basis of probable monetary loss,it will be easily understood by all.Therefore,it is necessary to develop methodologies which communicate the system capacity and demand to financial risk,Incremental dynamic analysis(IDA) was applied in a performance-based earthquake engineering context to do hazard analysis,structural analysis,damage analysis and loss analysis of a reinforced concrete(RC) frame structure.And the financial implications of risk were expressed by expected annual loss(EAL).The quantitative risk analysis proposed is applicable to any engineering facilities and any natural hazards.It is shown that the results from the IDA can be used to assess the overall financial risk exposure to earthquake hazard for a given constructed facility.The computational IDA-EAL method will enable engineers to take into account the long-term financial implications in addition to the construction cost.Consequently,it will help stakeholders make decisions.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Chaotic dynamic behavior analysis and control for a financial risk system

According to the risk management process of financial markets,a financial risk dynamic system is constructed in this paper.Through analyzing the basic dynamic properties,we obtain the conditions for stability and bifurcation of the system based on Hopf bifurcation theory of nonlinear dynamic systems.In order to make the system＇s chaos disappear,we select the feedback gain matrix to design a class of chaotic controller.Numerical simulations are performed to reveal the change process of financial market risk.It is shown that,when the parameter of risk transmission rate changes,the system gradually comes into chaos from the asymptotically stable state through bifurcation.The controller can then control the chaos effectively.

Systemic risk management and investment analysis with financial network analytics:research opportunities and challenges

Recent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic,social,and technical mechanisms of the increasingly interconnected global financial system.Such a system largely relies on the interconnectedness of various financial entities such as banks,firms,and investors through complex financial relationships such as interbank payment networks,investment relations,or supply chains.A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories,as well as develop business applications.Moreover,with the advance of big data related technologies,and the availability of huge amounts of financial and economic network data,advanced computing technologies and data analytics that can comprehend such big data are also needed.We referred this approach as financial network analytics.We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk,as well as developing intelligent business applications like banking advisory systems.In this paper,we review the existing research about financial network analytics and then discuss its main research challenges from the economic,social,and technological perspectives.

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

The Effect of the 2007/2008 Financial Crisis on Enterprise Risk Management Disclosure of Top US Banks

We document the effect of the 2007/2008 financial crisis on the volume and the quality of enterprise risk management （ERM） disclosure in the annual reports of the largest US banks, and analyze its determinants. Using a content analysis approach of the annual reports form 10-K for the years 2006, 2007, 2008, and 2009, we find that the ERM disclosure is significantly and positively associated with the crisis, bank size, board independence, duality and significantly and negatively associated with profitability, leverage, and board size. This paper seeks to fill a gap in the literature by investigating the effect of the crisis on ERM disclosure in the US banking sector context, and gives an insight into the factors affecting risk disclosure practices during the financial crisis.

Research on Financial Management of Internet Financial Enterprises Based on Big Data

In the context of the rapid development of big data technology,the financial management model of Internet financial enterprises is undergoing a profound transformation.This paper analyzes the key aspects of applying big data technology in Internet finance,including its basic concepts,characteristics,and current state of development in the field.It examines the current situation and primary challenges faced by financial management in Internet financial enterprises,such as risk management,cost control,and data integration.To address these challenges,optimization strategies based on big data are proposed,focusing on areas such as risk control and cost optimization.By constructing a financial data analysis model,this study provides an in-depth analysis of relevant data,demonstrating the role of big data technology in improving financial management.Finally,through a case study,the effectiveness of big data applications in financial management is verified,and future development directions are discussed.

Using Python to Analyze Financial Big Data

As technology and the internet develop,more data are generated every day.These data are in large sizes,high dimensions,and complex structures.The combination of these three features is the“Big Data”[1].Big data is revolutionizing all industries,bringing colossal impacts to them[2].Many researchers have pointed out the huge impact that big data can have on our daily lives[3].We can utilize the information we obtain and help us make decisions.Also,the conclusions we drew from the big data we analyzed can be used as a prediction for the future,helping us to make more accurate and benign decisions earlier than others.If we apply these technics in finance,for example,in stock,we can get detailed information for stocks.Moreover,we can use the analyzed data to predict certain stocks.This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level,helping to protect them from potential losses.

Credit risk constraint mechanisms in rural financial reform

To research the operating mechanisms of rural financial reform, through setting up a contract model, the constraint roles of reputation and legal intervention on the default risk arising in the operating of the credit union funds are inspected. Analysis indicates that the increase in reputation cost can reduce the probability of union member default behavior and the probability of turning to the law for the credit union funds. Meanwhile, the amount of loans and the interest rates can increase the probability of turning to the law for the credit union funds. Below the marginal values, the penalty mechanisms can reduce the balancing probabilities of member default behavior and turning to the law for the credit union funds, namely, the penalty has some "substitution effect" for turning to the law for the credit union funds.

Population Ageing,Financial Leverage and Systemic Risk

Population ageing and high financial leverage are two common problems .faced by the worM＇s major economies. The recent financial crisis proved that the two issues could lead to systemic risk if not handled properly. Based on dynamic panel data from 1980 to 2012 in 119 countries, this paper examines the impact of population ageing on financial leverage J？om an empirical point of view and finds that there is a signOqcant inverted U-shape relationship between the two. In addition, empirical studies show that after passing the ＂turning point＂ of ageing, there will be a marked increase in the probability of financial crisis during the ＂deleveraging＂ process. It can be projected from the empirical conclusions of this paper that China will likely enter the range of a turning point between 2019 and 2028. After that, population ageing, deleveraging and asset price collapse may have a ＂resonance＂ effect to severely impact the stability of the financial system. Therefore, China should shift to more proactive macro financial regulations as quickly as possible, as dynamic and robust management of financial leverage and forward-looking control of bubbles could ensure that the financial system remain flexible enough to avoid systemic risk to the greatest extent.

Research on Industrial Relevance Degree and Industrial Financial Situation Risk Matrix Construction——Based on Chinese Information Technology Industry

Based on Input-Output Table in 2010 issued by National Bureau of Statistics of China, with the help of input-output model and with the calculation of indexes of industrial relevance degree in Chinese information technology industry, the paper reveals the industrial relevance in Chinese information technology industry. The paper also selects the relevant industries which are highly associated with the development of Chinese information technology industry based on industrial relevance degree to analyze the influences of these industries on the financial situation risk fluctuation in information technology industry and to design the matrix of financial situation risk in information technology industry. Then, the paper offers countermeasures and suggestions for the development of our information technology industry.

Clear Measures Set to Control Financial Risks

China’s top financial regulators have warned about risks in complicated financial products and banks’off-balance sheet business,in a bid to prevent cross-market risk contagion,according to senior officials recently.

The risk early-warning of gas hazard in coal mine based on Rough Set-neural network

This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate： applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.

Risks in Credit Financial Analyze

This article aims to study the indicators used in the financial analysis for credit and explain them. Also it checks the impact of each indicator in credit analysis and what happens if the pointer is changed deliberately to get the loan, giving some possible ways to do it and analyzing them. It proposes a new model to evaluate the indicators and the assignment of weights in formula evaluation of each indicator, so the risks of granting credit will be smaller as well as the evaluation of the financial terms of a company will be more balanced and optimal. The scope is to equilibrate the weights of each indicator in the fmancial credit analyze not by rescoring its value but by assigning shares in the evaluation formula. Doing this, it can be considered as a double checking using the same parameters and it lowers the risks in the money recovering. As it is debated in the article anyone can do fxaud to obtain a loan by altering the documents they provide through which some can do it good and even get uncaught. The scope is not to find what they did; it is to get protected even if they do it.