The Impact of the Digital Economy on Financial Regulatory Costs: An Empirical Analysis Based on the Solow Growth Model

As a novel economic form,the digital economy is reshaping the financial regulatory landscape and significantly impacting regulatory costs.This paper incorporates the digital economy and financial regulatory costs into the classic Solow growth model,uncovering an inverted U-shaped relationship between them.A subsequent mechanism analysis explains the rationale behind this relationship.To empirically examine this relationship in China,the paper utilizes inter-provincial panel data from 2013 to 2021 and employs methodologies such as the two-way fixed effects and moderating effects models.These analyses have important implications for the sound and sustainable development of China’s financial industry.The findings indicate:(a)As China’s digital economy develops,its impact on financial regulatory costs follows an inverted U-shaped pattern,initially increasing and then declining.This conclusion remains valid after robustness tests.(b)The influence of the digital economy on regulatory costs depends on favorable external conditions.Specifically,the impact is more pronounced in regions and periods with better digital infrastructure and more abundant human capital.(c)Additionally,redundant resources moderate this impact,which can weaken the inverted U-shaped relationship.Our findings not only provide a theoretical foundation for understanding the impact of the digital economy on financial regulatory costs but also offer valuable policy insights for optimizing financial regulation in China.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

Moderating effect of corporate financialization on the impact of climate policy on corporate green innovation:Evidence from China

With intensifying global climate change,humanity is confronted with unparalleled environmental challenges and risks.This study employs the staggered difference-in-difference model to examine the relationship between climate policy and green innovation in the corporate financialization context.Using Chinese-listed company data from 2008 to 2020,our analysis reveals a favorable correlation between China’s carbon emission trading policy(CCTP)and advancements in green innovation.Furthermore,we find that the level of corporate financialization moderates this correlation,diminishing the driving effect of CCTP on green innovation.Additionally,results of heterogeneity analysis show that this moderating consequence is more evident in non-state owned and low-digitization enterprises compared with state-owned and high-digitization ones.Our findings contribute to the existing literature by clarifying the interaction between CCTP,green innovation,and corporate financialization.Our research provides valuable insights for policymakers and stakeholders seeking to strengthen climate policies and encourages green innovation in different types of businesses.

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure.The stochastic procedures mainly depend on the combination of the artificial neural network(ANNs)along with the Levenberg-Marquardt Backpropagation(LMB)i.e.,ANNs-LMB technique.The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional orderα.The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1.The data proportion is applied as 73%,15%,and 12%for training,testing,and certification to solve the chaotic fractional system.The acquired results are verified through the comparison of the reference solution,which indicates the proposed technique is efficient and robust.The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error(MSE).To authenticate the exactness,and consistency of the technique,the obtained performances are plotted in the figures of correlation measures,error histograms,and regressions.From these figures,it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

APPLICATION OF FUZZY OPTIMIZATION MODEL IN ECOLOGICAL SECURITY PRE-WARNING

Ecological security is a vital problem that people all over the world today have to face and solve, and the situation of ecological security is getting more and more severe and has begun to impede heavily the sustainable development of social economy. Ecological environment pre-warning has become a hotspot for the modern environment science. This paper introduces the theories of ecological security pre-warning and tries to constitute a pre-warning model of ecological security. In terms of pressure-state-response model, the pre-warning guide line of ecological security is constructed while the pre-warning degree judging model of ecological security is established based on fuzzy optimization. As a case, the model is used to assess the present condition pre-warning of the ecological security of Anhui Province. The result is in correspondence with the real condition: the ecological security situations of 8 cities are dangerous and 9 cities are secure. The result shows that this model is scientific and effective for regional ecological security pre-warning.

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

The impact and heterogeneity analysis of digital financial inclusion on non-farm employment of rural labor

This study examines the effects of digital financial inclusion on non-farm employment of rural labor and the mediating mechanism of innovation and entrepreneurship activity using China Family Panel Studies(CFPS)and the provincial index of digital financial inclusion.Through the empirical test of the Probit model and mediation effect model,we found that:firstly,digital financial inclusion can promote non-farm employment of rural labor,with the level of digitalization having the most significant impact;secondly,by encouraging innovation and entrepreneurial activity,digital financial inclusion can promote non-farm employment of rural labor;thirdly,the driving effect of digital financial inclusion on non-farm employment of rural labor is more pronounced among the unmarried,eastern region,and male labor.Therefore,we should improve the construction of rural digital infrastructure and accelerate the development of inclusive rural finance to promote more diversified non-farm employment options for“disadvantaged groups”;encourage and support innovation at the government level,and create a favorable atmosphere for innovation and entrepreneurship.Simultaneously,farmers’digital literacy and financial knowledge should be improved so that more can adopt and take advantage of digital financial inclusion.

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.

Modelling and Analysis on Noisy Financial Time Series

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular problem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively.

Similar financial models： Are they free lunches？

In recent years, the academic and practical circles have paid so much attention to similar financial models because they generated a miracle in GOME （Gome Electrical Appliances Holding Limited） and SUNING （Suning Appliance Co., Ltd.） respectively. But what＇s the influence of such a model on corporate profitability and risk？ Are similar financial models free lunches？ To seek for the answers to the above questions, in this paper, we take Gree （GREE Electric Appliances, Inc. of Zhuhai）, Midea （Guangdong Midea Electric Appliances Co. Ltd. Stores） and GOME for examples to carry out a comprehensive and in depth financial analysis. The conclusions of this paper are： The company with similar financial characteristics has higher profitability and risk level; Only the companies which meet special requirements need or can implement such a model and the identification and control of hidden risk can guarantee its success.

High-rise building fire pre-warning model based on the support vector regression

Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.

An Early Warning Model of Financial Distress Prediction Based on Logistic-AHP-BP Neural Network Model

Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies in China has never exceeded 1% each year.The number of delisted companies in the security market is far less than the number of companies with financial distress.The capital market lacks a good delisting system and investors lack risk identification capabilities.Financial risk is directly related to delisting risk.Therefore,an early warning model of financial distress prediction for China.s stock market can provide guidance to stakeholders such as listed companies and capital markets.This paper first explains the immature delisting system of China.s capital market and the overall high risk of listed companies.financial distress.Then,the paper further elaborates previous research on financial distress prediction model of listed companies and analyzes the advantages and disadvantages of different models.This paper chooses the Analytic Hierarchy Process(AHP)to screen out the main factors that affect the risk of financial distress.The main factors are included in Logistic regression model and BP neural network model for predicting financial distress of listed companies.The overall effect of two models are assessed and compared.Finally,this paper proposes policy implications according to empirical results.

A Multifractal Detrended Fluctuation Analysis of the Ising Financial Markets Model with Small World Topology

We present a multifractal detrended fluctuation analysis （MFDFA） of the time series of return generated by our recently-proposed Ising financial market model with underlying small world topology. The result of the MFDFA shows that there exists obvious multifractal scaling behavior in produced time series. We compare the MFDFA results for original time series with those for shuffled series, and find that its multifractal nature is due to two factors： broadness of probability density function of the series and different correlations in small- and large-scale fluctuations. This may provide new insight to the problem of the origin of multifractality in financial time series.

Impact of Energy Efficiency and Financial Support on Green Upgrading in China’s Industrial Sector

The report from the 20th National Congress of China emphasizes the importance of focusing on the clean,low-carbon,and efficient use of energy,increasing financial support,and promoting green upgrading within the industrial sector.This paper,based on annual data,employs the entropy weight method to construct a comprehensive index reflecting the impact of green upgrading in industrial sectors.To delve deeper,it utilizes the DEA model to measure energy efficiency and its subdivision BCC model to break down energy efficiency into technical and scale efficiency.The financial support landscape is examined from the vantage points of both direct and indirect financing.Using a multivariate time series model,this paper thoroughly investigates the influence of energy efficiency and financial support on the green upgrading of the industrial sector.The findings reveal a significant positive impact of both energy efficiency and financial support on green upgrading in industrial industries.Notably,scale efficiency emerges as the primary driver of energy efficiency.Moreover,indirect financing proves to be more effective in promoting financial support than direct financing.The empirical results retain their robustness even after substituting explanatory variables.The study concludes by contextualizing the research findings within the current real-world scenario,offering practical insights,and proposing specific recommendations.

Financial Boundaries Condition Analyzing Model for Non-equity Corporate Merger and Acquisition Decisions

The financial boundaries of negotiation for the acquirer and acquiree of a non-equity corporate Merger & Acquisition (hereafter M&A) is defined. A new analyzing model to determine financial boundary for thc M&A decision is developed. The negotiation range of transaction price and condition for the acquirer and acquiree by quantitative analyzing is put forward.

Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.

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.

What Is the “China Model” of Financial Reform?

Based on a comparative study of China’s three-decade financial system reform and the financial reform model of Russia and selected Eastern European countries, we found noticeable discrepancies between China and other transitional countries in respect to objective setting, path and sequence selection, power sources and advancement strategy. We conclude that a "China model" of nancial system reform does exist. The formation and evolution of the "China model" is closely related to China’s special political and economic environment. More importantly, it is significantly influenced by China’s overall economic reform model as well.

A Nonlinear Dynamic Model of the Financial Crises Contagions

Employing the Differential Dynamics Method, a nonlinear dynamic model is set up to describe the international financial crises contagion within a short time between two countries. The two countries’ control force depending on the timely financial assistance, the positive attitude and actions to rescue other infected countries, and investor confidence aggregation, and the immunity ability of the infected country are considered as the major reasons to drive the nonlinear fluctuations of the stock return rates in both countries during the crisis. According to the Ordinary Differential Equations Qualitative Theory, we found that there are three cases of financial crises contagion within a brief time between two countries: weak contagion with instability but inhibition, contagion with limit and controllable oscillation, and strong contagion without control in a brief time.

Intelligent Networks for Chaotic Fractional-Order Nonlinear Financial Model

The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented system.The method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the ANNs-LMB.This technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference solution.Fifteen numbers neurons have been used to solve the fractional-order chaotic financial model.The selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for certification.The results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and precise.The obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).