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Although the port industry is very important in the Korean economy, there are few literature studies that shed light on the macroeconomic implications of the business cycles in this industry. This paper examines the roles played by foreign economic factors in the business cycle in the Korean port industry. Specifically, it aims to estimate the impulse response of the seaborne freight volume in the port industry due to the shocks of the foreign economy and analyze the contributions of each shock considered on the variation in the freight volume. The structural FAVAR (SFAVAR) model was employed to extract the unobserved foreign economic factors. This paper estimates the four foreign economic factors and the parameters of the model using the one-step Bayesian Gibbs sampling method. The findings of this study show that foreign economic activity statistically affects the freight volume of the Korean ports. Specifically, the shocks to the foreign real economic activity increased freight transportation for nearly one year. Following the world inflation shocks, the freight transportation was enhanced. However, this impact disappeared after a year. Similarly, the rise in global liquidity was shown to encourage transportation activity; nevertheless, this activity declined after five quarters. Moreover, the increase in the world interest rates exerted a negative effect on the volume of transportation. Furthermore, the variance decomposition analysis shows that 49.2% of the variation in the transportation volume could be attributed to foreign economic activity. This analysis can contribute to drawing useful implications in establishing the port industry policy in response to the change in the economic environment such as the foreign economy. [ABSTRACT FROM AUTHOR]

Poverty reduction is an important driving force for the global realization of the United Nations 2030 Sustainable Development Goals and the building of a global community with a shared future for mankind. This paper takes the Belt and Road Initiative (BRI) as a quasi-natural experiment. Based on the panel data of 134 countries in the world from 2000 to 2018, this paper uses the difference-in-difference (DID) model to examine the impact of the BRI on poverty reduction in countries along the route and to study its internal mechanism from the dual perspectives of trade openness and investment openness. The results show that (1) the BRI can significantly reduce the incidence of poverty with the impact coefficient stable at −0.26, and the empirical conclusion has passed the parallel test, placebo test, and instrumental variable test; (2) the mediating effect test model shows that the BRI can achieve poverty reduction through the dual openness of trade and investment, and the intermediary effect of trade openness is greater than investment openness; and (3) from the perspective of heterogeneity analysis, geographically, the BRI has a slightly higher role in promoting poverty reduction in landlocked countries than in coastal countries. In terms of economic location, the effect of poverty reduction has a certain "pro-poverty" characteristic, that is, the BRI promotes poverty reduction in low- and middle-income countries far more than other types of income countries. Therefore, we believe that the continuous deepening of the BRI high-quality construction and the strengthening of cooperation among countries along the route will play a key role in promoting the international cycle of trade, investment, and other factors, as well as the cause of poverty reduction in the region and the world. [ABSTRACT FROM AUTHOR]

In view of the problems of poor correlation and poor calculation accuracy of the existing factors affecting economic fluctuations, this paper designs a new method for analyzing the factors affecting economic fluctuations based on the characteristics of the era of big data. First, after determining the factors influencing the economic fluctuations in the era of big data, the calculation method of key impact indicators is clarified. Then, the entropy law method is used to assign a weight value to the index, and the set is formed after the index is gathered. Then, the EM algorithm is used to assign the expected value to the data in the set. Finally, the set of influencing factors of the economic fluctuations is taken as input information, and the correlation evaluation model of influencing factors of economic fluctuations is constructed by the Monte Carlo method to complete the analysis of the influencing factors. The experimental results show that the correlation coefficient of influencing factors of economic fluctuations in the era of big data analyzed in this paper is high and the calculation accuracy of influencing factors is high, which proves the feasibility of this method. [ABSTRACT FROM AUTHOR]

In order to explore the impact of the Internet of Things technology on economic market fluctuations and the analysis effect of the Internet of Things technology on economic market fluctuations, this paper uses the Internet of Things algorithm to improve the economic fluctuation model. Moreover, this paper uses the Internet of Things algorithm to locate economic transactions and performs data processing to optimize the intelligent network system to improve the operating effect of the economic system. In addition, this paper improves the sensor node algorithm and proposes to use the weighted value of network node density to balance the positioning problem caused by the unbalanced distribution of network nodes in the detection area. Finally, this paper analyzes the market economy volatility model through the Internet of Things technology, combined with simulation experiments to explore the application of the Internet of Things technology in the economic market volatility model. Through experimental research, it can be known that economic market fluctuation models based on Internet of Things technology can play an important role in market economic analysis. [ABSTRACT FROM AUTHOR]

Under the strategic goal of building a domestic economic cycle, the development quality of social e-commerce in my country needs to be improved urgently. The main reason for the low quality of social e-commerce development in my country is that the natural advantages of social e-commerce, such as high-efficiency dissemination of information and increased consumer utility due to improper sales model design, cannot play a role. However, the lack of research on social e-commerce systems makes it difficult for retailers to obtain effective theoretical guidance for their sales model design. Based on this, this paper takes the three most important and important social e-commerce sales models in my country at present—recommended reward model, online group purchase model, and online crowdfunding model as the research objects, and systematically analyzes the consumer recommendation behaviors in the three sales models. By analyzing and market equilibrium, while improving the theoretical research of social e-commerce, it also provides theoretical basis and policy guidance for the optimal mode design of social e-commerce under different sales modes. This paper considers the consumer recommendation behavior in social networks and analyzes the main characteristics of the three sales models in view of the development status and specific problems of social e-commerce in my country, and then constructs retailer-existing consumer-potential The three-agent dynamic Stackelberg game model of consumers analyzes the influence mechanism and market equilibrium of consumer recommendation behavior under three sales models. [ABSTRACT FROM AUTHOR]

As a large developing country, China's outward foreign direct investment (OFDI) and its relationship to trade have always been subjects of extensive scrutiny in academic circles. With the continuous advancement of the "Belt and Road" initiative (BRI), the pace of overseas expansion by Chinese firms has accelerated. This paper theoretically constructs a firm heterogeneity model of China's OFDI and bilateral trade, and conducts research based on panel data of China's OFDI in countries along the BRI from 2003 to 2019. The results show that China's OFDI can substantially enhance the potential for bilateral trade, and this conclusion remains robust after overcoming endogeneity and economic fluctuations. In a heterogeneity test, China's OFDI in countries along the "Land Silk Road" is shown to have a greater effect on trade potential than in countries along the "Maritime Silk Road." In the controlled test, China's OFDI to BRI countries has a greater role in upgrading trade potential than investment in other trading powerhouses. China's OFDI can promote the economic development of host countries along the BRI by strengthening the potential for bilateral trade. The results of this study are not only conducive to the "going global" of Chinese enterprises but also of practical significance for host countries to achieve high-quality economic development. [ABSTRACT FROM AUTHOR]

The factors of industry environment risks to enterprise financial activities are considered; seven kinds of industry environment risks influencing enterprise financial activities are chosen as state variables. The eighth paper is "Nonlinear Effect Analysis of Electricity Price on Household Electricity Consumption" by Zhang and Wen; they investigated the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. Accurate energy forecasting is essential to achieve greater efficiency and reliability in power system operation and security, energy pricing problems, and scheduling and planning of energy supply systems, among other areas. [Extracted from the article]

As the global semiconductor industry has entered a new round of rapid growth, it has also entered a golden cycle of economic growth. Semiconductor companies increase their intrinsic value through financing, industry mergers and acquisitions, and venture capital searches. At the same time, market investors pay more attention to the intrinsic value of companies when looking for good investment targets. Therefore, the systematic risk assessment of the global semiconductor market has become a common concern of market investors and corporate management. In this context, this paper found a method that can assess the systemic risk of the semiconductor global market, which is to use the K-means algorithm based on deep feature fusion. This paper analyzed the algorithm in depth, analyzed the quantum space of tensors, and used the definition of cluster fusion to obtain the relationship between the projection matrices U and V. Experiments were carried out on the improved algorithm, and market research was conducted on a multinational semiconductor company A, which mainly included the basic statistics of the rate of return and the ACF and PACF coefficients of the rate of return series. Finally, the stock risk comparison of company A and company B in the same period was carried out. The experimental results showed that comparing the three items of compound growth rate, coefficient of variation, and active rate coefficient, the highest compound growth rate was 0.41, which came from Category 2, the highest variation coefficient was 2.31, which came from Category 10, and the highest active rate coefficient was 1.78, which came from Category 9. The experimental content was completed well. [ABSTRACT FROM AUTHOR]

Under the development of social economy and the gradual improvement and optimization of the financial and economic system, China's financial structure is constantly changing and evolving. Financial institutions are the interrelations and connections between the various parts of the economy as a whole. They represent the role and achievements of the financial economy. Research shows that the transformation and evolution of financial structure promotes economic development and management efficiency. Therefore, under the background of the continuous evolution of financial structure, this article explores the factors of the evolution of financial structure on economic growth and management efficiency. The results of the experiment are as follows: (1) this article investigates developments of the evolution of financial structure, economic development, and management efficiency, determines the research direction of the experiment, provides background support for the subject research of this paper, and explores the influencing factors according to the impact mechanism on economic development and management efficiency and (2) put the evolution background of the financial structure in the degree of economic development and management efficiency. By using the economic growth calculation algorithm and management efficiency calculation algorithm, calculate the change trend of the range of economic growth and the fluctuation of the degree of management efficiency under the background of the evolution of the financial structure. The experimental results show that under the background of the evolution of the financial structure, the total development of the national economy has been effectively broken through, and the management mode of enterprises has been improved to effectively improve the management efficiency. [ABSTRACT FROM AUTHOR]

This paper adopts the method of the random matrix to conduct an in-depth study and analysis of the management of macroeconomic multivariate statistics and regionalization, and to design its management strategy based on a random matrix. A combination of theoretical and applied research, combined with the experience of other cities, is used to study regional economic management using literature analysis, comparative analysis, and deductive induction by summarizing the results achieved in community management, studying its experience, exploring the significance of community management in ETDZs for social development, analyzing the current obstacles and contradictions affecting community management in Suqian ETDZ, and proposing relevant countermeasures for the problems arising therein to enrich the relevant theories and thus promote the level of community management in ETDZs to a higher level. Macroeconomic uncertainty refers to the changes that cannot be accurately observed, analyzed, and foreseen, i.e., the deviation of the expected value of the economy from the actual value. Not all macroeconomic fluctuations belong to the category of macroeconomic uncertainty, only unpredictable economic fluctuations are uncertain. It can be seen from the above that there is a corresponding relationship between the financial income correlation matrix and the random correlation matrix, and both are affected by the same random factors. The importance of uncertainty for macroeconomic policy making is undeniable, and its quantification is a key step. In this paper, we propose an econometric model to eliminate expectations and compare the advantages and disadvantages of different measurement models; we measure a system of fourteen macroeconomic indicators to reflect macroeconomic uncertainty and provide quantitative measurement results for the correct understanding of macroeconomic uncertainty and economic policy making. Factor analysis is used to analyze these six comprehensive aspects to obtain the six-dimensional comprehensive scores of each province and city, and the scores are normalized and visualized. Based on a certain understanding of the economic equilibrium structure of each province and city in China, and a certain control of the overall development of each region itself at this stage, the future economic development trend is studied. [ABSTRACT FROM AUTHOR]

In order to improve the analysis and forecasting effect of economic fluctuations, this paper combines the high-frequency financial sequence algorithm to conduct measured analysis of economic fluctuations. Under the continuous jump-diffusion price model, this paper considers the jump part and the continuous part in the process of asset pricing. Moreover, for the jump part, this paper uses the wavelet method to analyze the observation data, and obtains the estimator of the second-order jump covariation difference matrix and its convergence speed. For the continuous part, this paper adopts a two-scale realized volatility model under the continuous price model. In addition, this paper verifies the effect of the intelligent model proposed in this paper through simulation experiments. The simulation data shows that the economic fluctuation analysis system based on high-frequency financial time series proposed in this paper has good economic analysis and economic forecasting effects. [ABSTRACT FROM AUTHOR]

Affected by the fluctuation of the market and economic environment during the epidemic period, the capital pressure of companies has increased sharply, which increases the possibility of risk transmission in the same industry and poses new challenges to the operation and financing of companies. From the perspective of preventive motivation of corporate cash holding, we creatively use the industry working capital shortfall as the explanatory variable to construct an Extended Cash Holding Model and a Cash Holding Value Regression Model. Taking the panel data of A-share listed companies in Shanghai and Shenzhen as samples, this paper uses the Classical Linear Regression Model and Fixed Effects Regression Model to study the relationship between industry working capital shortfall and cash holdings in the same industry, as well as the relationship between industry working capital shortfall and cash holding value. The empirical results show that industry working capital shortfall has an important impact on the cash holding level within the same industry, and the cash holding level is significantly and positively correlated with industry working capital shortfall. Moreover, this study also reveals that the industry working capital shortfall has a dual impact on cash holdings. Specifically, the higher the risk of industry working capital shortfall, the lower the value of company cash holdings. The conclusions of this paper not only extend the research on cash holding but also provide support and reference for companies to optimize the cash holding value. [ABSTRACT FROM AUTHOR]

At this stage, with the development of society, China's modernization level has also been greatly improved. Under the background of artificial intelligence and the impact of economic globalization, China's external economic environment is changing with each passing day. Chinese enterprises are looking for new development ways to maintain their own development vitality under the condition of artificial intelligence. State-owned enterprises are controlled by the central or local governments. On this basis, this paper mainly discusses the innovative methods of state-owned enterprise management under the background of artificial intelligence, so as to provide reference for the innovation of state-owned enterprise management. [ABSTRACT FROM AUTHOR]

Economic crises occur when the economy experiences drastic fluctuations, which in turn cause heavy losses to society and the economy. Economic crises happen when there are significant losses to society and the economy as a result of the economy experiencing abrupt changes or deep recessions. The term "economic cycle early warning mechanism" refers to a collection of theories and techniques for keeping track of, assessing, forecasting, and choosing policies regarding finances in light of the particular economic phenomenon known as economic cycle fluctuations. The traditional early warning mechanism has been unable to keep up with the demands of economic cycle early warning due to the growth of economic globalisation, and the phenomenon of incomplete early warning and poor early warning accuracy frequently occurs. A genetic algorithm simulates the natural selection and genetic mechanisms of Darwin's theory of biological evolution. It is a computational model of the biological evolution process. It is a technique for looking for the best answer by simulating the course of natural evolution. Genetic algorithms have advanced incredibly quickly in recent years. It has been widely applied in machine learning, neural networks, control system optimization, and the social sciences as an effective, useful, and reliable optimization technique. In order to optimize the early warning mechanism and increase the thoroughness and accuracy of early warning, this paper investigates the early warning mechanism of financial and economic cycles by combining genetic algorithms. A mathematical model of the economic cycle was built during the experiment and tested in accordance with the genetic algorithm's basic operating principle. The findings demonstrate that the genetic algorithm-based early warning system for the financial economic cycle is more complete and accurate, with a 6.4% increase in accuracy. [ABSTRACT FROM AUTHOR]

The transformation of the real economy to the virtual economy is one of the necessary ways for social and economic development. By analyzing the definition of real economy and virtual economy, the relationship between them, and the relationship between finance and virtual economy, a feasibility analysis model for the transition from real economy to virtual economy is designed based on association rule algorithm, and provide feasibility analysis for "from entity to virtual." At the same time, based on the scale measurement theory of virtual economic cycle, this paper establishes the rule-constrained Apriori algorithm model. Then, the input-output structure decomposition method is used to analyze the feasibility of the transformation from the real economy to the virtual economy. Through the analysis, we know that the multiplier value in the real economy is higher than that in the virtual economy, which promotes the substantial growth of its own output. The total amount of virtual economy is growing rapidly, but economic transformation is incomplete. The expansion of the real economy has increased the total output value of virtual laser machines. Therefore, the feasibility of transforming from a real economy to a virtual economy has a good relationship. [ABSTRACT FROM AUTHOR]

In order to explore the influencing factors of economic fluctuations, this article combines data mining technology to mine the factors that affect economic fluctuations and introduces financial network theory to the transfer problem of investment strategies, which provides an effective research method for economic behavior research. Moreover, this article calculates the fund network parameters from a quantitative perspective by constructing the topological characteristic index of the financial network to illustrate the influence of the degree of network evolution and the time scale. In addition, this article constructs an economic fluctuation data mining model based on economic fluctuations and uses data to verify the effects of the method proposed in this article. The experimental results show that the data mining method proposed in this article can play an important role in the analysis of factors affecting economic fluctuations and accurately mine the relevant factors that affect the economy. [ABSTRACT FROM AUTHOR]

In order to analyze the relationship between energy consumption and economic growth dynamics, the author proposes a dynamic analysis technology of energy consumption and economic growth based on the PVAR model. This technology uses the PVAR method to compare and quantitatively describe the relationship between economic growth and energy consumption in developed and developing countries from 1990 to 2009, using impulse response functions and variance decomposition analysis methods; study the similarities and differences between total energy consumption and various fossil energy consumption and the dynamic impact of economic development, and finally establish a PVAR model for analysis. Experimental results show that: in the forecast of variance analysis, the impact explanatory power of the total energy consumption of developing countries on the fluctuation of economic growth reaches 8.85070. However, the contribution of total energy consumption in developed countries to economic growth is insufficient. The explanatory power of economic growth in developed countries to total energy consumption is 15.58070, while that in developing country is 29.28070, both of which are greater than the explanatory power of their respective total energy consumption on economic growth. Conclusion. The technology based on the PVAR model can effectively meet the needs of analyzing the dynamic relationship between energy consumption and economic growth. [ABSTRACT FROM AUTHOR]

Economic security is a core theoretical issue in economics. In modern economic conditions, the ups and downs caused by economic instability in any economic system will affect the stability of the financial market, bring huge losses to the economy, and affect the development of the whole national economy. Therefore, research on the regularity of economic security and economic fluctuations is one of the important contents to ensure economic stability and scientific development. Accurate monitoring and forecasting of economic security are an indispensable link in economic system regulation, and it is also an important reference factor for any economic organization to make decisions. This article focuses on the construction of an economic security early warning system as the main research content. It integrates cloud computing and data mining technologies and is supported by CNN-SVM algorithm and designs an early warning model that can adaptively evaluate and warn the economic security state. Experiments show that when the CNN network in the model uses ReLU activation function and SVM uses RBF function, the prediction accuracy can reach 0.98, and the prediction effect is the best. The data set is verified, and the output Q province's 2018 economic security early warning comprehensive index is 0.893. The 2019 economic security early warning index is 0.829, which is consistent with the actual situation. [ABSTRACT FROM AUTHOR]

With the fast development of intelligent data-mining technologies, some advanced artificial intelligence approaches are widely developed and employed to help the decision-making of enterprises and government. The application of advanced and intelligent approaches successfully helps the enterprises and government find out the valuable information hidden in the massive economic data. This study presents a novel data-driven approach to forecast the short-term business climate using the electric power consumption data of large enterprises. In addition, the climate conditions, interactions between different industries, the business cycle, and some other related variables are also considered and included in the developed forecasting model. To be specific, the business climate prediction model based on support vector machine (SVM) is proposed firstly, and the human-simulated particle swarm optimization algorithm (HSPSO) in our previous work is employed to optimize the parameters of the developed forecasting model. Secondly, a novel power-consumption-based business climate index (BCI) system is developed and comprehensively analyzed. The developed BCI system that contains the index for each separate industry (BCI-I), the index for tertiary industry (BCI-T), the index for secondary industry (BCI-S), and the index for the entire province (BCI-P) is proposed. In addition, the developed BCI system is employed to normalize the output of SVM-based forecasting model to directly indicate the business climate, which is very important to the decision-making of enterprises and government under the background of smart cities. Finally, the real data of Guangdong province in China, including the gross output values (GOV) and detailed power consumptions of more than 38000 enterprises, are employed to test the proposed approach. Experimental results show that the GOV of each industry and the whole society predicted by HSPSO-SVM matches the real data well. Moreover, the predicted BCI can directly indicate the business climate in advance, which is of great value for economic-decision and policy-making of both enterprises and government. [ABSTRACT FROM AUTHOR]