Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology,the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management.Particularly within the context of economic globalization,informatization,and digital transformation,enterprises find themselves navigating a rapidly evolving market environment by intensifying competition.To enhance efficiency and competitiveness,many enterprises have embraced the financial sharing model to streamline financial management processes,curtail costs,and bolster the execution of corporate strategies.This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management.Drawing upon this analysis and aligning with the needs of enterprise financial management,the article proposes ideas for optimizing management practices,aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks.This contribution seeks to provide valuable insights for practitioners in the field.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

The aggregate and sectoral time‑varying market efficiency during crisis periods in Turkey:a comparative analysis with COVID‑19 outbreak and the global financial crisis

This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.

Analysis of Enterprise Marketing Strategy Optimization from the Perspective of New Media

With the advent of the economic era of“Internet+,”new media has become a new means of enterprise marketing by virtue of its own advantages,including fast communication speed,diversified communication channels,low cost,and novel content.Enterprises should actively integrate into the new media era,constantly improve their cultural soft power and new media marketing ability,build new marketing systems,set up professional new media marketing teams,and further improve their marketing ability;innovate new media marketing content,attract consumers’attention,and expand the audience group;open up new media marketing channels,carry out diversified marketing,comprehensively enhance their marketing ability,and succeed in the fierce market competition.

Research on the Optimization and Upgrading Path of Enterprise Financial Management from the Perspective of the Digital Economy

At present,China has entered the era of the digital economy,the business environment faced by enterprses has changed significantly,and the traditional financial management model is no longer adaptable due to market demand.Hence,enterprises need to study the characteristics of the digital economy and adopt ffective financial management oplimization and upgrading paths.This article summarizes the characteristics of the digital economy,concludes and analyzes the opportunities and challenges faced by corporate financial management from the perspective of the digital economy,investigates the necessity ol optimizing and upgrading corporate financial management,and examines the efective optimization and upgrading paths,hoping to provide reference information for corporate financial managers.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.

China Continues to Be World’s Second Largest Insurance Market

Allianz Group recently released the Allianz Global Insurance Report,predicting that China will consolidate its position as the world’s second largest insurance market in the next decade.The report analyzes the business performance of the global insurance market in 2023 and forecasts the development direction and trends of the global insurance industry in the next decade.2023:A year of significant growth According to the report,in 2023,the global insurance industry grew by an impressive 7.5%,which is the fastest rate since the pre-Global Financial Crisis(GFC)era.

Analysis of Porter’s Five Forces Competitive Model in China’s Property Rights Trading Market

In recent years,China’s property rights trading market has been extremely competitive,but there are also new contradictions and challenges.This paper aims to analyze the external and internal competitive market of the property rights market through Porter’s five forces competition model,then find out the problems and defects in the development,thereby promoting the better development and progress of China’s property rights trading market.

Copula-EVT Based Tail Dependence Structure of Financial Markets in China

Tail dependence structure model based on Copula theory and extreme value theory （EVT） is constructed to picture the tail correlation of financial time series more exact. The empirical research results show that the Gumbel Copula can fit the upper and lower tail dependence structures of Shanghai A share index and Shenzhen A share index, and correlation of upper tails of both indices is stronger than that of lower-tails.

Will high-frequency trading practices transform the financial markets in the Asia Pacific Region?

High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.

Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Pricing,management and decision‑making of financial markets with artificial intelligence:introduction to the issue

While the advancement of Internet Technology generates massive amounts of data that can facilitate decision makings of financial markets,it also arouses new challenges to financial activities,such as the acquisition,processing and analysis of multiple information resources.In addition,the external environment of the financial market is full of uncertainties,such as the occurrence of COVID-19 pandemic and government intervention,which makes the asset pricing and management much more challenging.In recent years,artificial intelligence technology has developed rapidly and has been widely used in various fields,including the financial markets.Due to its capability of mining patterns from big data,artificial intelligence is regarded as an efficient tool to address the abovementioned challenges.

Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market

This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry.

COVID‑19 pandemic and the crude oil market risk:hedging options with non‑energy financial innovations

This study examines the hedging effectiveness of financial innovations against crude oil investment risks,both before and during the COVID-19 pandemic.We focus on the non-energy exchange traded funds(ETFs)as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies.We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios.Results show evidence of hedging effectiveness for the financial innovations against oil market risks,with higher hedging performance observed during the pandemic.Overall,we show that sectoral financial innovations provide resilient investment options.Therefore,we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns,especially in similar financial crisis as witnessed during the pandemic.In essence,our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions.Moreover,by exploring the role of structural breaks in the multivariate volatility framework,our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

Volatility spillover effect between financial markets:evidence since the reform of the RMB exchange rate mechanism

The volatility spillover effect between the foreign exchange and stock markets has been a major issue in economic and financial studies.In this paper,GC-MSV model was used to study the spillover effect between the foreign exchange market and the stock market after the reform of the RMB exchange rate mechanism.The empirical results show that there is a negative correlation of dynamic price spillovers between the foreign exchange and stock markets.There are asymmetric volatility spillover effects between these two markets for both RMB stages—continued RMB appreciation or constant RMB shock(a significant reduction in appreciation).However,this has been reduced over time.In conclusion,The RMB exchange rate is a key variable that can affect the internal and external equilibrium of the national economy in an open economic environment,and the stock market is capable of quickly reflecting subtle changes in the real economy.In order to keep the stability of the financial markets and the healthy and rapid development of national economy,some suggestions were proposed.

Introduction to the special issue on Impact of COVID‑19 and cryptocurrencies on the global financial market

In the last year,the global outbreak of COVID-19 and the sharply rising price of Bitcoin greatly influence the financial market.Such influences include but are not limited to the trading strategies between different cryptocurrencies,portfolio diversification,foreign exchange markets,and macroeconomic policy.In this 26th issue of Financial Innovation(FIN),Volume 7,No.2(2021),22 researchers from 7 countries have used both traditional statistical methods and the newly developed machine learning techniques to analyze how COVID-19 affects the global financial market and how cryptocurrencies affect portfolio diversification.The research results could be beneficial to governments in making macroeconomic policies and financial investors for making better investment decisions.

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.

Asymmetric and symmetric meta-correlations in financial markets

In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.

Financial Derivatives Market and Storage Management

It’s known to all that under ideal condition the s to rage cost is kept in lower level when storage management be arranged by Economic Order Quantity(EOQ).Does this mean that any companies should set up their own storing system in proportion to the scale of the commodities’ producing or sell ing Furthermore, even if they manage storage in EOQ, because of different oper ation scale, geographical condition or ability borrowing money from financial ma rket, different companies pay unequal cost in storing the same commodity.In thi s paper, except for supplying commodities from our own storage system, the autho rs have analyzed other two supplying ways without whole storage system, they are forward contracts and futures contracts.The authors have discussed variable su pply cost for above different supply measures.According to the cost of each sup ply way, the managers can choose the most economical way in supplying the commod ity and predict the price of futures from storage management arranged by EOQ.Th e summary content is as follow: 1. The comparing of supply cost between forward contracts and storing system a rranged by EOQ. (1) The supply cost from forward contracts (2) The supply cost from storage system arranged by Economic Order Quantity (3) The application example for comparing cost in different supply way 2.The comparing of supply cost between futures going physical and storing syst em arranged by Economic Order Quantity. (1) The supply cost from futures going physical (2) The correlation between futures contracts and storage management arranged b y EOQ (3) The application example for comparing cost in different supply way 3.How does storing system of scale economic affect the price of forward and fu tures contracts (1) How does the price of forward and futures contracts fluctuate (2) How do we calculate the price of a commodity at future point from the cost of scale economic storing (3) How do we operate efficiently in derivatives market by using the cost of sc ale economic storing (4) The application example for analyzing the price of futures 4.The correlation among storage managementforward contracts and futures mark et.

A Fast LDL-factorization Approach for Large Sparse Positive Definite System and Its Application to One-to-one Marketing Optimization Computation

LDL-factorization is an efficient way of solving Ax = b for a large symmetric positive definite sparse matrix A. This paper presents a new method that further improves the efficiency of LDL-factorization. It is based on the theory of elimination trees for the factorization factor. It breaks the computations involved in LDL-factorization down into two stages： 1） the pattern of nonzero entries of the factor is predicted, and 2） the numerical values of the nonzero entries of the factor are computed. The factor is stored using the form of an elimination tree so as to reduce memory usage and avoid unnecessary numerical operations. The calculation results for some typical numerical examples demonstrate that this method provides a significantly higher calculation efficiency for the one-to-one marketing optimization algorithm.