Management Review

Systematic Analysis for the Impacts of U.S.-China Trade Friction on China's Economy

Bao Qin, Su Danhua, Wang Shouyang

2020, 32 (7): 3-16.

Abstract ( 567 )

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The U.S.-China trade friction with upgrading tariff will directly influence China's international trade and indirectly influence the whole economic system. In this paper, the methodology of Meta-synthesis in systems science is applied to study the impacts of U.S.-China trade friction on China's economy. Based on the qualitative analysis of the transmission mechanism and the core parameters, a multi-sector computable general equilibrium model of China is built to study the quantitative impacts of U.S.-China trade friction on China's economy. Two scenarios for the U.S.-China trade friction are designed and five scenarios for the China's economic system are assumed. In the view of systems science, by applying the properties of repeated comparison and successive approximation of Meta-synthesis, the core parameters are adjusted to study the impacts of U.S.-China trade friction. The results indicate that the additional tariff imposed by the U.S. will have minor effects on China's macro economy; however, China's international trade will be negatively shocked with exports of high-end manufacturing industries significantly hurt. Imposing higher tariff by the U.S. on China's exports will further increase the negative shocks. Reducing capital-labor substitution elasticity or price elasticity of import and export will alleviate the negative impacts on China's economy. The managed floating regime on RMB exchange rate can help achieve international balance and reduce the negative shock. This paper provides policy suggestions to coping with U.S.-China trade friction.

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Theoretical and Empirical Research on"Four Rings" Research Framework of Global Container Port System: Based on TEI@I Methodology

Xu Lizhi, Yan Xuyang

2020, 32 (7): 17-28.

Abstract ( 204 )

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This paper firstly introduces a "four rings" research framework of global container port system, which is proposed by Professor Shouyang Wang in 2009. In empirical studies, the Pearl River Delta ports, as the "one ring" port system in the "four rings" research framework of global container port system, is used as an illustration. Empirical studies show that the "four rings" research framework, based on the economic and trade, geographical multi-dimensional analysis, provides an effective analytical framework to understand and generalize the attributes, matching patterns and evolution paths of container port system of PRD region in China. Accordingly, the key quantitative indicators affecting its container cargo demand, could be effectively identified. By matching the key quantitative indicators with the specific methods and technologies of TEI@I methodology, an integrated forecasting model for specific container port system analysis is constructed. The theoretical and empirical studies indicate that the proposed "four rings" research framework of global container port system provides the research paradigm for the complex world's major container ports system.

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Measuring the Interaction between Uncertainty and Crude Oil Market: A Multiscale Methodology Based on Synthetic Integration

Feng Yuyao, Liu Chang, Sun Xiaolei

2020, 32 (7): 29-40.

Abstract ( 245 )

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As a typical complex system, the energy system is highly susceptible to a variety of uncertainty factors from the external environment during its development. Based on the thought of complex systems management, this paper incorporates different levels of uncertainty, including economic policy uncertainty, market panic and geopolitical risk into the same framework according to the financial attributes, commodity attributes and political attributes of crude oil. And a multi-scale complex system research methodology based on meta-synthesis is constructed to study the influence rule and mechanism of the complex system formed by uncertainties and crude oil market at different time, frequency and price levels. In this paper, six uncertainty indicators and crude oil futures price for a total of 270 months from 1997 to 2019 are selected. With the help of wavelet analysis and quantile regression method, the study finds that the interaction between uncertainties and the crude oil market varies between different indicators, time-frequency scales and different price levels. This helps understand the complex interaction mechanism between the crude oil market and the uncertainty system, and has important reference value for the reasonable decision-making of stakeholders.

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The Simulation Analysis of System Dynamics about the Effect of Value Network on the Business Model Innovation of High-tech Start-ups——From the Perspective of System Management and CET@I Methodology

Guo Tao, Ding Xiaozhou, Qiao Han, Zhang Chunyu

2020, 32 (7): 41-53.

Abstract ( 276 )

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Under the background of digital economy, value network is an important external environment that affects the business model innovation of high-tech start-ups and its dynamic influence mechanism is one of the hot research topics at present. From the perspective of system management, based on business model iceberg theory and value network theory, and by use of the analysis framework of CET@I methodology, this paper constructs the system dynamics model of value network influencing business model innovation of high-tech start-ups, and uses the Vensim PLE software to conduct simulation and sensitivity analysis. The simulation results show that the institutional environment, structural characteristics and resource conditions of value network affect the business model innovation by influencing the value proposition, creation and realization. When the main parameters in the value network change, the influence mechanism and degree of effect on business model innovation are different. The research results further enrich the research category of business model iceberg theory and value network theory, having a significant guidance for high-tech start-ups to deepen the cognition of the role of value network and to promote the business model innovation.

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Study on System Management Method of Foreign Exchange Reserves Risk

Yu Mei, Zhang Kun

2020, 32 (7): 54-65.

Abstract ( 256 )

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As an important part of financial security, foreign exchange reserves risk management is a typical complex system management issue. This paper regards foreign exchange reserves risk management as a complex system, fully considering the change and mutual influence of various macro variables in the system. On the basis, this paper proposes a risk early warning model and constructs risk warning indicators to achieve the goal of monitoring foreign exchange reserves risk. Firstly, this paper selects effective indicators based on signal analysis method, then calculates the probability of foreign exchange reserves crisis that occurs within the next 24 months, and finally forecasts foreign exchange reserves risk in the next two years based on probability value and the change trend. This paper uses the data of China and Argentina to conduct an empirical test, and the results show that the model has a better predictive ability. At present, China's foreign exchange reserves risk is low, but since 2014, the risk has shown a slow upward trend and needs real-time monitoring to avoid crisis.

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Group Decision Making Based on Trust Relationship, Prospect Theory and Hesitant Fuzzy Preference from the Perspective of WSR Methodology

Zhou Xiaoyang, Wang Liqin, Feng Pingping, Hu Zhongquan, Wen Haoyu

2020, 32 (7): 66-75.

Abstract ( 281 )

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Based on the Wuli-Shili-Renli (WSR) methodology, this paper defines and analyzes "Wuli" (objective uncertain information), "Shili" (process of group decision making) and "Renli" (human psychological behavior and the trust relationships among people) in group decision making, and introduces corresponding methods to quantify the above three factors. A group decision-making method considering trust relationship and prospect theory with hesitant fuzzy sets is proposed by considering "Wuli", "Shili" and "Renli". A numerical example is presented to verify the validity of the proposed method. Comparison with traditional group decision making method is also given to illustrate the advantages of the proposed method.

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The Effectiveness of TEI@I Forecasting: Evidence from a Five-year Project-based Public Prediction for Pearl River Delta Port Logistics

Tian Xin, Wang Haoqing, Zhu Jiayi, E Erjiang

2020, 32 (7): 76-88.

Abstract ( 285 )

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Existing literatures that assess the performance of forecasting methods typically based on historical data are partially subjective. We propose a brand new framework for evaluating and tracking the effectiveness of forecasting methods with data set from predicting project under real scenarios. This paper empirically examines the effectiveness of TEI@I methodology using public project-based data for Pearl River Delta Port logistics during the period from 2009 to 2013. Our analyses show that TEI@I model has both excellent prediction accuracy and robustness, which decreases with the increase of prediction duration. This paper also demonstrates that future development tendency of the complex system can be effectively predicted.

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Integrated Data Characteristic Driven Forecasting Research on Real Estate Market

Cui Mingming, Liu Xiaoting, Li Xiuting, Dong Jichang

2020, 32 (7): 89-101.

Abstract ( 207 )

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Housing market is a complex system, and housing prices are results of various factors. The prediction accuracy of traditional single forecasting model is not enough to well support economic decision-making. Based on the TEI@I idea, an integrated prediction model is created to predict the direction and level of change in the real estate market, using key predictive indicators based on the dynamic circulation system of the real estate market. Firstly, the changing direction of the real estate market is predicted by boom analysis. Then, based on the divide-and-rule idea, integrated data characteristic driven forecasting model of the real estate market is established. The model quantitatively predicts the real estate market and demonstrates the effectiveness of different forecasting methods by comparing data decomposition and data feature-driven basic model selection. This paper concludes that applying the appropriate base model according to the data feature can predict the investment, demand and price of the real estate market more accurately than the single prediction model. This research enriches the theory and method of real estate market forecasting and predicts the trend of the real estate market more accurately. Moreover, it provides recommendations for the government to design policies and make decisions, for real estate developers to invest and for residents to purchase houses.

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Dependent Structure and Extreme Risk Spillover of Global Stock Markets: Financial Complexity Analysis Based on Vine-Copula

He Minyuan, Li Hongquan

2020, 32 (7): 102-110.

Abstract ( 176 )

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With the development of economic globalization and financial integration, the correlation between international financial markets is increasing, and risk contagion has become a focus of attention for all parties. This paper aims to examine the dependent structure and the extreme risk spillover in global stock markets by using the method of Vine Copula. Specifically, this paper uses the Vine Copula to describe the overall dependent structure of the international stock markets, and then examines the extreme risk spillover among the stock markets by tail dependence measure. The conclusions are as follows:(1) The dependent structure of international stock markets presents some characteristics of economic agglomeration, and the dependent structure is slightly different with different Vine Copula models; (2) There is an asymmetric tail dependence between international stock markets, and the lower tail dependence is generally higher than that of the upper tail; (3) The average value of tail dependence in mature markets is relatively high, while that in emerging markets is low, indicating that the financial markets of developed countries still dominate the international financial markets. This paper reveals the important nodes and main relationships of the global financial market correlation map, which has important guiding value for both macro-prudential supervision and micro-investment decision-making.

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Analysis of Multi-scale Volatility Features of Interest Rate in China Based on TEI@I Methodology

Cui Xiao, Guo Kun, Jin Zhenni, Yang Gan, Liao Zhewen

2020, 32 (7): 111-122.

Abstract ( 222 )

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The interest rate reflects the supply and demand relationship of capital market. It also has an important impact on economic growth, the operation of real enterprises, financial products pricing and policy regulation. The fluctuation characteristics of interest rate has been a research focus in both academic area and market area. From the perspective of system management, this paper makes a deep research on various factors which have important impact on interest rate in both money supply and demand system and systematically sorts out theoretical basis of interest rate decision from both macro and micro aspects. The return of 10-year CDB bond is selected as the proxy index of the market-oriented long-term interest rate. Based on the methodology of TEI@I, the EMD model is used to decompose the sequence of interest rate. The results show that, the interest rate has multi-scale features, and each component has different fluctuation characteristics. The long-term and middle long-term trends are mainly affected by the opening policy of Chinese financial markets. The low-frequency cycle is mainly affected by the factors of capital demand side, while the high-frequency cycle is mainly affected by the supply side. Further, the over-expected random disturbances are mostly accompanied by the impact of unexpected events.

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Systemic Dependence Analysis between Chinese Art and Stock Markets

Li Ping, Xu Jianing

2020, 32 (7): 123-137.

Abstract ( 233 )

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With the integration of the culture and finance, the art market has become another important investment area in addition to the stock and real estate markets, and the risk fluctuations in the capital market have an impact on the art market. The complexity of the two systems in terms of relevance, synergy and volatility is increasing, which expands the transmission effect of risks between different systems. We can investigate the correlation between the systems, from the internal structures and the impact, thus revealing the evolution of the two markets and the forming-mechanism of risk. This paper analyzes the development trend and presents situation of Chinese art and stock markets. In conclusion, the two markets are closely related to the macro economy; there are competitive effects and wealth spillover effects between the two markets; the interactions of the two effects affect the degree of correlation between the two markets. Based on this, it can be concluded that there is an obvious dependence between China's stock and art markets. Subsequently, we combine Copula and GARCH-t to construct four models in static or dynamic form thereby studying the correlation between Chinese art and stock markets. The empirical results show that during 2000-2018 the correlations between Shanghai Composite Index and Art Market Trading Index are generally low, but increase when the market fluctuates greatly, indicating that there is some tail dependence. Time-varying Copula models are more accurate in describing the correlation between the two economic systems than static copula. Indices of the two market mainly have a weak positive correlation. In the end, we put forward some suggestions on the art investment.

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Research on Timeliness of Dynamic Reserve Reliefs after Typhoon Based on TEI@I Methodology

Qu Chongchong, Wang Jing, Zhou Yongshen, He Mingke, Zhang Jingmin

2020, 32 (7): 138-149,190.

Abstract ( 270 )

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The importance of the disaster information forecast is growing in the process of disaster relief. This paper presents an integrated forecasting model based on the TEI@I methodology for typhoon disaster relief materials reserving with the example of super typhoon Lekima. The empirical results reveal that TEI@I methodology integrated model can significantly improve the prediction performance over other simulation algorithms models presented in this study, especially the nonlinear prediction results adjusted NGA model of operation time reduced from 200s to 99.92s based on TEI@I methodology. The connotation of "decomposition before integration" in TEI@I methodology introduces the analysis and prediction of non-linear demand. TEI@I methodology can not only analyze the impact of the timeliness requirement of the external environment on the demand for relief materials, but also integrate the time series after analysis to improve the prediction accuracy of the model.

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Research on Financial Crisis Relief Policy Based on System Engineering Method

Tong Xin, Pang Jiaqi, Dong Zhi

2020, 32 (7): 150-165.

Abstract ( 178 )

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The globalization of trade leads to an increase in economic dependence among countries. All economies constitute an interactive global economic system. Many internal channels of crisis transmission directly or indirectly affect economies within the same system. It is therefore very meaningful to study the crisis relief policy (CRF). In order to further understand the effectiveness of the economies' various CRFs under the background of the financial crisis, the system engineering model is adopted to establish a system dynamics model for financial crisis relief in a single economy, and international coordination and rescue of financial crisis in the US, Japan, Europe and China under the background of the Fed currency swap agreement. The effectiveness of the long-term rescue effect of traditional monetary policy and non-traditional monetary policy is evaluated by the system dynamics model of single economy rescue, and the long-term rescue effect of currency swap agreement on the four major economies is evaluated by the system dynamics model of international coordination rescue.

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Forecasting Premium Income of China's Insurance Industry Based on TEI@I Methodology

Zhou Hua, Lu Zhiyuan, Zheng Min

2020, 32 (7): 166-179.

Abstract ( 272 )

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Premium income is the most important basic indicator reflecting the development level of an insurance industry in a country or region. It reflects the insurance demand of residents and the overall size of the insurance market in the country or region. At the same time, premium income is also a key variable of insurance density and insurance penetration. Based on TEI@I methodology, within the integrated frameworks of econometric model, text mining and machine learning, this paper constructs a model for forecasting premium income of China's insurance industry. In this model, this paper first uses the season-adjusted model, SARIMA, to fit the main trend of premium income. Then, this paper uses the support vector regression, which is a method in machine learning, to fit the residual of SARIMA. Under the guidance of TEI@I methodology, in order to improve the fitness of the model, this paper adds the related Baidu index through the text mining technology into explanatory variables. At last, this paper uses the support vector regression again to integrate the fitting results, so as to obtain an integrated forecasting model of premium income with a higher precision. Through the model comparison and based on the data of China's premium income, this paper verifies the effectiveness and robustness of the TEI@I methodology in China's premium income forecasting research.

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Air Passenger Demand Forecasting Model Based on TEI@I Methodology

Liang Xiaozhen, Zhang Qianwen, Yang Mingge

2020, 32 (7): 180-190.

Abstract ( 222 )

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Based on TEI@I methodology, this paper proposes a forecasting framework on air passenger demand. First, ensemble empirical mode decomposition (EEMD) is applied to decompose the original air passenger demand data into a number of relatively simple modes, reducing the complexity of the data. Second, the extracted modes are thoroughly analyzed to capture hidden data characteristics, including complexity, stationarity and long-range correlation properties. These characteristics are then used to determine appropriate forecasting models for each mode (econometric models or artificial intelligence models). After that, the impacts of irregular and the infrequent future factors on air passenger demand are explored using expert systems techniques. Finally, the components above are predicted independently and these prediction results are combined as an aggregated output. The empirical results indicate that the proposed model based on TEI@I methodology has a good prediction performance on air passenger demand.

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Information Disclosure of Rational Expectation Equilibrium and Dynamical Dynamics in Financial Market

Zhang Qiang, Gu Tao, Li Xiang, Liu Shancun

2020, 32 (7): 191-204.

Abstract ( 285 )

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Based on complexity of assets pricing, we analyze the implications of information disclosure and investigate the trading strategies and the market dynamics based on rational expectation equilibrium (REE) framework with Bayesian updating. Our results show that information disclosure weakens the information advantage of homologous informed traders and reduces the uncertainty faced by heterogeneous informed traders, which triggers the interaction of the trading intensity of two types of informed traders. Specifically, the direct "uncertainty reduction" effect leads to strategic complementary and the indirect "inference augmentation" effect leads to strategic substitutability. The information disclosure increases the threshold of strategic complementary, thereby reducing the adverse selection risk of the market and improving the stability of the financial system. Due to the self-organization phenomenon in financial markets, we also analyze the impact of public disclosure on market liquidity, price informativeness and the benefits of traders.

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Dynamic Pricing Decision of Distributed Power Generation System Based on System Engineering Methodology

Dai Yeming, Gao Hongwei, Wang Baohui, Li Lu

2020, 32 (7): 205-216.

Abstract ( 192 )

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The sale of surplus power in distributed photovoltaic power generation system plays a good complementary role in optimizing power supply and demand and ensuring the stability of power grid. For studying the internal dynamic pricing mechanism of photovoltaic power generation group in distributed power generation system, based on the analysis of system engineering methodology and the system decision method, an internal dynamic pricing game decision method in distributed photovoltaic user group is proposed from the perspective of system management. By introducing a penalty function, a distributed algorithm is designed to solve the game model. Finally the system simulation and sensitivity analysis is carried out. The simulation results show that the network space distance, resource constraints and user preference of distributed power generation system will directly affect the decision result of internal pricing of photovoltaic user group. At the same time, the production capacity of photovoltaic users and the buyback price of main power network are the sensitive factors that affect the direct dynamic pricing of photovoltaic users. The research results enrich the scope of pricing mechanism in power system, especially distributed power system, and provide feasible exploration and guidance for the grid connection of renewable energy.

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Risk Analysis for Urban Transit——An Empirical Study on the Beijing Rail Transit System

Liu Fuze, Li Juan, Fan Bosong, Wang Jue

2020, 32 (7): 217-225.

Abstract ( 256 )

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Rail transit is a transportation system that provides urban public passenger services. Its risk management is an arduous and complicated system engineering. In order to ensure the safety of urban rail transit system operation and improve the ability of management departments to respond to emergencies, it is necessary to evaluate the risk status of rail transit operations and formulate corresponding control measures from the aspects of comprehensive application of technology and scientific decision-making. Based on the TEI@I methodology, this paper proposes a delay duration prediction model. The delay time is predicted by establishing a Bayesian network model. The statistical distribution model such as lognormal, Weibull and Gamma distribution is used to verify the prediction result of delay time. Based on the results, a prediction model of subway delay time is constructed. This statistical analysis method is used to calculate the probability of occurrence of the risk event, so as to analyze the risk status of the rail transit system. An empirical study on the Beijing rail transit system shows that the urban rail transit system in Beijing has a good operational status and the possibility of risk events is small. Among all types of risk events, driving accidents are most likely to occur, usually caused by a variety of factors, and the relevant departments should pay enough attention.

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Enterprise Financial Risk Early Warning Model Based on TEI@I Methodology

Xiao Yi, Xiong Kailun, Zhang Xi

2020, 32 (7): 226-235.

Abstract ( 306 )

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With great economic downward pressure, many companies are facing performance thunder frequently. How to identify and guard against ‘traps’, reshape investors' confidence and prevent financial crisis has become a practical and urgent task. With the theoretical framework of TEI@I methodology, this paper integrates text mining and deep learning to construct a prediction model of enterprise financial crisis. From a new perspective, a dynamic modeling method for financial distress prediction which is based on convolutional neural networks and long-term and short-term memory networks is proposed. The empirical research is carried out with listed Chinese companies which in the information service industry as samples. With the effective considering of financial factors and non-financial factors that affect financial crisis of enterprises, the forecasting model has much better forecasting effect than others. This method can be applied and promoted easily, and has positive value for government and investors if they need to grasp the business dynamics, reduce investment risks and prevent financial crisis.

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Semi-supervised Key Feature Selection of Customers Based on Hall for Workshop of Meta-synthetic Engineering

Xie Ling, Chen Wenting, Cao Hanwen, Xaio Jin

2020, 32 (7): 236-245.

Abstract ( 223 )

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Customer classification has always been one of the most important issues in customer relationship management (CRM). Therefore, it is very important to select key features of customers. In the era of big data, unbalanced classification distribution, high dimension, and a large number of samples without label have made this more complex and become a complex systemic decision issue. In order to address this issue, this study proposes the semi-supervised key feature selection model of customers based on hall for workshop of meta-synthetic engineering (SFS-HWME). The model invites five experts in related fields to identify research difficulties, find alternatives through qualitative analysis, obtain a total solution through comprehensive integration and get a quantitative analysis model. The quantitative analysis model uses semi-supervised learning (SSL). Firstly, it uses the data set L with category tags to train the Adaboost integration model to predict the categories of samples in the data set U with unclassified tags; secondly, the data set U is clustered by the self-organization map (SOM) algorithm and the samples are selectively tagged; thirdly, these samples are added to the data set L along with the tagged category tags; finally, the re-sampling technique is used to balance the class distribution of the new training set L, and the group method of data handling (GMDH) deep learning network is trained to pick out the optimal feature subset. The research invites 5 experts to select the most reasonable features. The empirical analysis on four customer classification data sets shows that the proposed SFS-HWME model has better key feature selection performance than some existing models.

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Competition and Cooperation Strategy of Bohai Rim Portunder the Framework of TEI@I Methodology

Lu Bo, Moon Ilkyeong, Xing Jian, Song Dongping

2020, 32 (7): 246-257.

Abstract ( 184 )

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As an open system, the internal and external factors together form the complex system operation structure of the port. This results in a random state transition of the port system. This paper combines the catastrophe theory and game theory with TEI@I as the basic theoretical framework and guiding ideology to analyze and predict the strategic choice of the Bohai Sea port in competition and cooperation. On this basis, in order to overcome the problem of overlapping information and unclear meanings of the vulnerability research in the past catastrophe theory, we apply factor rotation to further modify the catastrophe theory. In this way, we measure the vulnerability of the nine major ports in China's coastal areas and measure the impact of various factors. And use the conclusion of vulnerability measurement to select the game participants and the game strategy set. It makes up for the defects of subjectivity in the construction of existing game models. We find that in terms of port construction scale competition, whether other ports choose the strategy of upgrading depends on whether the leading port enterprises in the region can obtain excess profits; In terms of port market competition, differentiated management and characteristic management are key strategies; In terms of port cooperation, after cooperation, participation in cooperation and non-participation in cooperation ports increase monopoly profits, which is not conducive to the long-term development of regional economy. Finally, the simulation results support the conclusion of the game model. In the development of the Bohai Sea port group, the port construction scale, differentiated operation and port operation capacity construction should be increased.

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Research on the Impact of Supply-side Reform of Iron and Steel Industry on Stock Market: From the Perspective of Systemic Risk Management

Liu Xiangli, Zhang Yipeng

2020, 32 (7): 258-266.

Abstract ( 210 )

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This paper takes the stock market as the research object and studies the impact of supply-side reform on the stock market from the perspective of systemic risk management. Based on the traditional CoVaR method, we propose the CoVaR model of non-linear quantile regression which uses polynomial to measure the systemic risk contribution (CoVaR value) of iron and steel industry to the stock market index and all first class industry in China from 2008 to 2018, and compare the data before the supply-side reform with the data after the reform. We find that the systemic risk spillover of iron and steel industry to the stock market and most first class industry decreased significantly after the reform, and the supply-side reform policy played a significant role in it. The supply-side reform made a positive contribution to the stable development of the stock market system.

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Calculating Method of Borrower's Credit Quality Transfer Probability and Application in Forecasting Loss-of-Connection Probability Based on TEI@I Methodology

Pang Sulin, Hou Xianyan

2020, 32 (7): 267-279.

Abstract ( 279 )

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Based on the theoretical framework of TEI@I methodology, for the first time, we study the calculation method of the probability of borrower's credit quality and its forecasting probability prediction application. In this paper, text mining is used to deal with the instability of borrower's credit quality, credit grade and credit score are used for classification, credit quality grade is classified by fuzzy classification, and the conversion probability of credit quality is defined. The conditional probability measurement model is extended to the conditional probability of credit grade and the conditional probability of credit score. The calculation formula of conditional probability of credit quality is obtained by using the method of total probability calculation. Referring to the calculation method of S&P credit transfer matrix, and using Markov C-K equation to predict the transfer probability of borrower's credit quality grade, this paper jointly studies the probability of borrower's loss of connection. The results show that the higher the credit quality grade is, the higher the probability of timely repayment is, the lower the credit quality grade is, and the higher the probability of bad debts is; the closer the borrower is to the "lost link" grade, the greater the possibility of losing link; with the extension of the borrowing period, it becomes less likely to keep the original credit quality grade and the transition probability to the next level will gradually increase.

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Review and Prospect of System Management Prediction Technology Based on TEI@I Methodology

Chai Jian, Kou Honghong

2020, 32 (7): 280-292.

Abstract ( 348 )

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With the changes in the world economy and industrial structure, the uncertainty and risk of the market economy are increasing. The predictive technology with early warning has become a research hot spot of many scholars. But with the advent of the big data, the behavior in complex systems becomes difficult to control and predict. In this paper, we review the development and application of prediction technology from the perspective of system management methodology with the help of text mining and bibliometric. Firstly, the development of system management forecasting methods is systematically summarized and evaluated based on the extensive investigation of the main forecasting technical literature. Besides, the principle and application of TEI@I methodology and its derived prediction methods in system management are summarized. Secondly, the trend of relevant literature in recent 20 years and the popular forecasting technologies are analyzed by bibliometric method. Finally, we summarize the whole paper and forecast the development of system management prediction technology under the background of big data in the future.

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Forecasting Corn Futures Prices Based on TEI@I Methodology

Wang Huijuan, Chen Hongjia, Gao Siqin, Guo Jingyi, Guan Rong

2020, 32 (7): 293-301.

Abstract ( 340 )

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The forecasting of corn futures prices is of great significance in instructing agricultural production and regulating the development of agricultural downstream markets. Based on the methodology of complex system management, this paper constructs a research framework for forecasting corn futures prices based on TEI@I methodology, and analyzes and predicts corn futures prices on the basis of actual data, because existing researches lack regularity and integrity. Our research confirms that the individual forecasting results of regression, Var and RPROP neural network prediction models have large difference and are not very stable in terms of time series. However, the forecasting results of TEI@I methodology with the Bootstrap integration outperforms the equal weight integration model.

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Decision Analysis of Supply Chain Finance Driven by Blockchain Based on Meta-synthesis

Li Jian, Zhu Shichao, Li Yongwu

2020, 32 (7): 302-314.

Abstract ( 368 )

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The majority of researchers work on the integration of supply chain finance and blockchain from the perspective of qualitative research, but there are few quantitative studies about this. In this study, the impact analysis of the blockchain in supply chain finance is upgraded from the qualitative analysis of the current literature to the quantitative analysis, taking the warehouse receipt pledge as research object. Based on the theoretical framework of the Meta-synthesis system approach, this paper studies the loan and production decisions of enterprises before and after the use of blockchain platform. In addition, the risk measurement method of VaR is used to compare and analyze the loan-to-value ratios decision of banks before and after the use of the blockchain. Finally, an analysis of numerical example is given. The results show that in this context, the use of blockchain has a positive impact on the operation management and ordering decisions of high-profit enterprises, while low-profit enterprises cannot benefit from it. Besides, the ceiling of banks' loan-to-value ratios is increased after the use of the blockchain platform at the same risk tolerance level, which means that borrowing enterprises have the opportunity to obtain more loans. This study has certain reference significance for enterprises and banks to make decisions on using the blockchain technology.

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Research on R&D Cost Management Based on WSR Methodology

Tang Zhipeng, Hua Guowei, Cheng Tai Chiu Edwin

2020, 32 (7): 315-325,336.

Abstract ( 284 )

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R&D cost management is a complex system management project, which needs a set of systematic management theory guidance. The larger the scale of R&D activities, the more systematic R&D cost management is needed. In view of the complexity and importance of R&D cost management, this paper constructs a WSR three-dimensional management framework of R&D cost management by using the "Wuli-Shili-Renli" (WSR) system methodology, and puts forward the three-dimensional management of R&D cost management. The research of R&D cost management based on WSR methodology can simplify and systematize the complex R&D cost management, improve the efficiency of R&D cost management, and provide guidance for R&D cost management.

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Evaluation of Short Selling: A State Space Approach from Financial System Engineering Perspective

Wu Lei, Bu Hui

2020, 32 (7): 326-336.

Abstract ( 209 )

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One question that has plagued regulators for a long time is that the volatility in the stock market has always been increased when short selling bans are removed. With the perspective of financial system engineering, the state space approach that takes into account the effect of behavior factors on the complex system, can help us decompose the price return into three parts:the innovation shock, the investors' reaction to the innovation shock, and the noise. This approach deepens our recognition and consideration about the question theoretically. Empirical result shows that short selling significantly increases the speed of investors' reaction to innovations and decreases the ratio of noise in the return volatility. This result indicates that the increased volatility comes from the increased speed of investors' reaction to innovations and therefore is the natural result of the development of stock market. The paper confirms the substantial improvement on price discovery efficiency induced by short selling in the Chinese stock market.

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