Research on the stock correlation networks and network entropies in the Chinese green financial market

Pan, Zicheng; Ma, Qianting; Ding, Junfei; Wang, Lei

doi:10.1140/epjb/s10051-021-00063-5

Cited by 8 publications

(11 citation statements)

References 25 publications

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“…In a semantic network, nodes are notional words and the relationship between nodes is semantic relationship [5]. Because the semantic network is an intermediate layer between syntactic and conceptual network, the dynamic semantic network based on real text is helpful to the research of the following three problems: the organization of human semantic or conceptual knowledge, human semantic processing mechanism, and semantic retrieval process [6]. Syntax and semantics are closely related.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

Wang

2022

Wireless Communications and Mobile Computing

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The research on prediction of Chinese semantic word-formation patterns based on complex network features has certain practical and theoretical significance in the field of natural language understanding. In this paper, complex networks are introduced into the prediction of Chinese semantic word-formation patterns, and a new prediction method of Chinese semantic word-formation patterns based on complex networks is proposed. And a solution that combines the semantic word-building rules of Chinese language with pattern recognition algorithm is put forward. Aiming at this scheme, a variety of pattern recognition algorithms are compared and analyzed, and the most suitable binary logistic regression model and naive Bayes model are found to predict Chinese semantic word-building patterns. The semantic loss is reduced, and the text classification model and corresponding classification algorithm are constructed, by introducing the maximum common subgraph theory to calculate text similarity under the complex network representation. The results of the experiments show that using complex networks to predict Chinese semantic word-formation patterns is both effective and feasible. The computer can judge the semantic word-formation pattern more accurately using the semantic word-formation pattern prediction model based on this theory.

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Section: Introductionmentioning

confidence: 99%

Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

Wang

2022

Wireless Communications and Mobile Computing

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“…Based on the research of Peron et al [33], Li et al [28], and Pan et al [29], it is found that the Renyi network entropy and the Tsallis network entropy perform better than other network entropies in describing stock market volatility. At the same time, the two kinds of network entropy have high robustness.…”

Section: Methodsmentioning

confidence: 99%

“…e internal factors of network entropy need further quantitative research. e network aggregation coefficient and the network centrality are important topological indexes to describe the stock correlation network in the interbank market (Pan et al [29]). e bank stock return rate, the bank stock return's volatility, the bank size, and the bank age are important indicators to reflect the financial and operational status of banks (Milanov and Shepherd [37]; Pollock et al [38]).…”

Section: Empirical Analysismentioning

confidence: 99%

“…It is found that network entropy can not only be used to describe the fluctuation of the Chinese stock market's return rate but also can be used as a tool for analyzing the structure of stock network. Besides, Pan et al [29] also constructed Shannon network entropy, Renyi network entropy, and Tsallis network entropy to study stock market volatility and found that Renyi network entropy and Tsallis network entropy have better performance. Besides, Lahmiri et al [30], Almog and Shmueli [31], and Zalmijn et al [32] also studied financial market volatility from the perspective of network entropy.…”

Section: Introductionmentioning

confidence: 99%

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Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market

Qiao

Liu

2021

Mathematical Problems in Engineering

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The stability of the interbank market is an essential guarantee for the sustainable development of society. Network entropy theory provides a critical research paradigm for the study of the stability of the interbank market. Considering that few scholars have conducted in-depth analysis on the influencing factors of the network entropy’s evolution process, this paper constructs the stock correlation network entropy model in the Chinese interbank market based on the correlation of stock price fluctuation. It focuses on analyzing the influencing factors in the dynamic evolution process of the stock correlation network entropy. In the light of empirical research, we can obtain the following results. First, the interbank market network’s aggregation coefficient, the interbank market network’s centrality, and the bank stock’s return rate play a positive role in the dynamic evolution of stock correlation network entropy in the Chinese interbank market. Second, the bank stock return’s volatility is negatively correlated with its network entropy. To maintain the stability of the financial market, the supervision department can monitor the stability of the interbank market by constructing the stock correlation network entropy.

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“…The network edges among nodes represent the correlation of price uctuations among stocks. According to the research of Pan et al (2021), we construct a stock correlation network model. We rst assume that there are N stocks in the market, and the period is T days.…”

Section: Stock Correlation Network Modelmentioning

confidence: 99%

The Stock Correlation Network Model and Its Influencing Factors in China's Green Finance Market

Zhao

et al. 2022

Preprint

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Green finance is a significant step to achieving environmentally sustainable development in the context of carbon neutrality. Based on the perspective of the stock correlation network, we select 51 green and environmental protection enterprises and construct the stock correlation network model in China's green financial market according to the correlation of their stock price fluctuations. On this basis, it discusses its network topologies and its dynamic evolution characteristics. After that, it deeply studies its impact mechanism of systemically importance in China's green financial market and introduces the major green financial policies for comparative analysis. In the light of empirical research, we can obtain the following results. First, the stock size distribution in China's green financial market has a power-law tail. Second, a sharp drop in the market index will increase the aggregation of the stock correlation network in the green financial market. Third, the variables about corporate social responsibility, corporate R&D investment intensity, and corporate green innovation output play significant roles in promoting the individual companies’ systemically importance ranking in the stock correlation networks of China’s green financial market. Fourth, the implementation of major green financial policies has promoted the improvement of the systematic importance of state-owned enterprises. Finally, the research enriches the application research of complex network theory in the green financial market and provides practical guidance for regulators to strengthen the risk monitoring of the green financial market.

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Research on the stock correlation networks and network entropies in the Chinese green financial market

Cited by 8 publications

References 25 publications

Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market

The Stock Correlation Network Model and Its Influencing Factors in China's Green Finance Market

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