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This study uses the mutual information method to study economic dependence among the provinces in the Yellow River Economic Belt, constructs the core dependence structure through the maximum spanning tree method, and uses the rolling window method to observe the changes in the dependence structure from a dynamic point of view. It has been found that there are extensive economic links among the nine provinces in the Yellow River Economic Belt, but that the degree of economic dependence varies greatly in different time periods. When economic development and the capital market are overheated, the interregional dependence is stronger, while the dependence decreases when economic development is in a state of contraction or when the total demand is relatively reduced. In addition, the phenomenon of geographical clustering of economic dependence is not obvious among provinces in the Yellow River Economic Belt. Most of the provinces maintain strong economic dependence with the economically developed provinces, and the economically developed provinces also maintain strong economic ties with one another. Finally, the implementation of the Yellow River Economic Belt strategy strengthens the economic links between the less developed provinces and the other provinces in the region, and promotes coordinated and sustainable development in the region.
Xianbo Wu; Xiaofeng Hui. Economic Dependence Relationship and the Coordinated & Sustainable Development among the Provinces in the Yellow River Economic Belt of China. Sustainability 2021, 13, 5448 .
AMA StyleXianbo Wu, Xiaofeng Hui. Economic Dependence Relationship and the Coordinated & Sustainable Development among the Provinces in the Yellow River Economic Belt of China. Sustainability. 2021; 13 (10):5448.
Chicago/Turabian StyleXianbo Wu; Xiaofeng Hui. 2021. "Economic Dependence Relationship and the Coordinated & Sustainable Development among the Provinces in the Yellow River Economic Belt of China." Sustainability 13, no. 10: 5448.
Since the international financial crisis in 2008, to achieve the political goal of financial stability, academic circles, financial industry, and regulatory authorities worldwide have deeply reflected on the current economic regulatory theories and policy adjustment tools through introducing the macroprudential policy. The dynamic provisioning system is a counter-cyclical policy tool in the macro-prudential adjustment framework widely used in the world. This paper uses the binary Gaussian Copula function to combine the measurement method of the default distance in the contingent claims analysis method with the risk warning idea based on the Probit model and proposes the contingent claims analysis (CCA)–Probit–Copula dynamic provisioning model based on nine forward-looking indicators. Based on China’s actual conditions, this model solves present problems faced by the current dynamic provisioning system in China, such as insufficient historical credit data reserves of commercial banks, excessive reliance on subjective judgments, and conflicts with the current accounting system. Moreover, this model can put forward corresponding counter-cyclical provisioning requirements according to the influence degree of macro-cyclical factors to different commercial banks’ own default risk, which not only takes into account the security and liquidity of commercial banks, but also ensures their profitability and competitiveness. Based on the empirical test of historical data from listed commercial banks in China, it proves that the dynamic provisioning requirements proposed in this model can effectively adjust the overall credit scale of the banking industry in counter-cyclical ways, thereby achieving the policy goals of counter-cyclical adjustment under the macro-prudential framework and maintaining the security of China’s financial system and the sustainable development of the macroeconomy.
Xiaofeng Hui; Aoran Zhang. Construction and Empirical Research on the Dynamic Provisioning Model of China’s Banking Sector under the Macro-Prudential Framework. Sustainability 2020, 12, 8527 .
AMA StyleXiaofeng Hui, Aoran Zhang. Construction and Empirical Research on the Dynamic Provisioning Model of China’s Banking Sector under the Macro-Prudential Framework. Sustainability. 2020; 12 (20):8527.
Chicago/Turabian StyleXiaofeng Hui; Aoran Zhang. 2020. "Construction and Empirical Research on the Dynamic Provisioning Model of China’s Banking Sector under the Macro-Prudential Framework." Sustainability 12, no. 20: 8527.
This paper applies effective transfer entropy to research the information transfer in the Chinese stock market around its crash in 2015. According to the market states, the entire period is divided into four sub-phases: the tranquil, bull, crash, and post-crash periods. Kernel density estimation is used to calculate the effective transfer entropy. Then, the information transfer network is constructed. Nodes’ centralities and the directed maximum spanning trees of the networks are analyzed. The results show that, in the tranquil period, the information transfer is weak in the market. In the bull period, the strength and scope of the information transfer increases. The utility sector outputs a great deal of information and is the hub node for the information flow. In the crash period, the information transfer grows further. The market efficiency in this period is worse than that in the other three sub-periods. The information technology sector is the biggest information source, while the consumer staples sector receives the most information. The interactions of the sectors become more direct. In the post-crash period, information transfer declines but is still stronger than the tranquil time. The financial sector receives the largest amount of information and is the pivot node.
Xudong Wang; Xiaofeng Hui. Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015. Entropy 2018, 20, 663 .
AMA StyleXudong Wang, Xiaofeng Hui. Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015. Entropy. 2018; 20 (9):663.
Chicago/Turabian StyleXudong Wang; Xiaofeng Hui. 2018. "Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015." Entropy 20, no. 9: 663.
This paper proposes a double Markov model of the double continuous auction for describing intra-day price changes. The model splits intra-day price changes as the repetition of one tick price moves and assumes order arrivals are independent Poisson random processes. The dynamic process of price formation is described by a birth-death process of the double M/M/1 server queue corresponding to the best bid/ask. The initial depths of the best bid and ask are defined as different constants depending on the last price change. Thus, the price changes in the model follow a first-order Markov process. As the initial depth of the best bid/ask is originally larger than that of the opposite side when the last price is down/up, the model may explain the negative autocorrelations of the price of the best bid/ask. The estimated parameters are based on the real tick-by-tick data of the Nikkei 225 futures listed in Osaka Stock Exchanges. The authors find the model accurately predicts the returns of Osaka Stock Exchange average.
Meng Li; Xiaofeng Hui; Misao Endo; Kazuo Kishimoto. A quantitative model for intraday stock price changes based on order flows. Journal of Systems Science and Complexity 2014, 27, 208 -224.
AMA StyleMeng Li, Xiaofeng Hui, Misao Endo, Kazuo Kishimoto. A quantitative model for intraday stock price changes based on order flows. Journal of Systems Science and Complexity. 2014; 27 (1):208-224.
Chicago/Turabian StyleMeng Li; Xiaofeng Hui; Misao Endo; Kazuo Kishimoto. 2014. "A quantitative model for intraday stock price changes based on order flows." Journal of Systems Science and Complexity 27, no. 1: 208-224.
Because of the importance of companies’ financial distress prediction, this paper applies support vector machine (SVM) to the early-warning of financial distress. Taking listed companies’ three-year data before special treatment (ST) as sample data, adopting cross-validation and grid-search technique to find SVM model’s good parameters, an empirical study is carried out. By comparing the experiment result of SVM with Fisher discriminant analysis, Logistic regression and back propagation neural networks (BP-NNs), it is concluded that financial distress early-warning model based on SVM obtains a better balance among fitting ability, generalization ability and model stability than the other models.
Xiao-Feng Hui; Jie Sun. An Application of Support Vector Machine to Companies’ Financial Distress Prediction. Computer Vision 2006, 3885, 274 -282.
AMA StyleXiao-Feng Hui, Jie Sun. An Application of Support Vector Machine to Companies’ Financial Distress Prediction. Computer Vision. 2006; 3885 ():274-282.
Chicago/Turabian StyleXiao-Feng Hui; Jie Sun. 2006. "An Application of Support Vector Machine to Companies’ Financial Distress Prediction." Computer Vision 3885, no. : 274-282.
Aiming at improving the predictive ability of corporate financial distress, a method integrating decision tree and genetic algorithms is put forward to realize dynamic selection of financial ratios in the process of modeling. It uses genetic algorithms to optimize financial ratio set, so the ultimate decision tree model for financial distress prediction has a good balance between accuracy and generalization. Empirical study shows that this model's prediction accuracy for training samples and validation samples are respectively 94.67% and 93.75%. This indicates that the proposed method for financial distress prediction can dynamically optimize the financial ratio set and effectively avoid the over-fitting problem of decision tree to improve the generalization ability
Jie Sun; Xiao-Feng Hui. An Application of Decision Tree and Genetic Algorithms for Financial Ratios' Dynamic Selection and Financial Distress Prediction. 2006 International Conference on Machine Learning and Cybernetics 2006, 2413 -2418.
AMA StyleJie Sun, Xiao-Feng Hui. An Application of Decision Tree and Genetic Algorithms for Financial Ratios' Dynamic Selection and Financial Distress Prediction. 2006 International Conference on Machine Learning and Cybernetics. 2006; ():2413-2418.
Chicago/Turabian StyleJie Sun; Xiao-Feng Hui. 2006. "An Application of Decision Tree and Genetic Algorithms for Financial Ratios' Dynamic Selection and Financial Distress Prediction." 2006 International Conference on Machine Learning and Cybernetics , no. : 2413-2418.