Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Wang, Xudong; Xia, Hui

doi:10.3390/e20090663

Cited by 17 publications

(13 citation statements)

References 62 publications

(110 reference statements)

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“…By constructing coattention networks for the Chinese stock market, Chen et al [43] discovered the structural differences of bull and bear markets and utilised such differences to predict stock returns. Dividing the year 2015 into four periods (the tranquil, bull, crash, and postcrash), Wang and Hui [44] used kernel estimation to build the information transfer network for studying information transition before and after the 2015 crash. Adopting the mutual information and symbolisation methods, Khoojine and Han [45] generated minimum spanning trees of the top 110 companies listed on the China Securities Index 300 from January 2014 to December 2017 to study the differences in topological characteristics of preturbulence, turbulence, and post-turbulence networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

Chen

Wang

et al. 2021

Entropy

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The interactive effect is significant in the Chinese stock market, exacerbating the abnormal market volatilities and risk contagion. Based on daily stock returns in the Shanghai Stock Exchange (SSE) A-shares, this paper divides the period between 2005 and 2018 into eight bull and bear market stages to investigate interactive patterns in the Chinese financial market. We employ the Least Absolute Shrinkage and Selection Operator (LASSO) method to construct the stock network, compare the heterogeneity of bull and bear markets, and further use the Map Equation method to analyse the evolution of modules in the SSE A-shares market. Empirical results show that (1) the connected effect is more significant in bear markets than bull markets and gives rise to abnormal volatilities in the stock market; (2) a system module can be found in the network during the first four stages, and the industry aggregation effect leads to module differentiation in the last four stages; (3) some stocks have leading effects on others throughout eight periods, and medium- and small-cap stocks with poor financial conditions are more likely to become risk sources, especially in bear markets. Our conclusions are beneficial to improving investment strategies and making regulatory policies.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

Chen

Wang

et al. 2021

Entropy

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“…In Section 3.3 , we analyze the time trend fitness of variables, and empirically demonstrate the potential threshold effects of leveraged trading on the stock price crash risk. Although the non-linear effects of the stock price crash risk may vary [ 30 , 31 ]; however, the quadratic effect of leveraged trading on the stock price crash risk has been confirmed by many studies [ 23 , 24 , 32 ]. Therefore, we compare the linear trend and quadratic trend fitness of variables based on the literatures mentioned above.…”

Section: Data Preprocessing and Descriptive Statisticsmentioning

confidence: 99%

The Threshold Effect of Leveraged Trading on the Stock Price Crash Risk: Evidence from China

Peng

2020

Entropy

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The stock price crash constitutes one part of the complexity in the stock market. We aim to verify the threshold effect of leveraged trading on the stock price crash risk from the perspective of feedback trading. We empirically demonstrate that leveraged trading has a threshold effect on the stock price crash risk on the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016. At a low leverage ratio, leveraged trading reduces the stock price crash risk; however, as the leverage ratio increases and exceeds a certain threshold, leveraged trading asymmetrically increases the stock price crash risk. These findings provide new insights in understanding the complexity in the Chinese stock market.

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“…Wang and Hui applied effective transfer entropy to study the information transfer in the Chinese stock market around its crash in 2015. They divided the crash into the tranquil, bull, crash, and post-crash periods and they found the information technology sector is the biggest information source, while the consumer staples sector receives the most information [42]. Li et al used transfer entropy to research the risk contagion in Chinese banking system, and evaluated the stability of Chinese banking system by simulating the risk contagion process [43].…”

Section: Introductionmentioning

confidence: 99%

Information Flow Networks of Chinese Stock Market Sectors

et al. 2020

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Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin & Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extract important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the composite sector is an information source of the whole stock market, while the nonbank financial sector is the information sink. We also find that the non-bank finance, bank, computer, media, real estate, medical biology and non-ferrous metals sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the non-bank finance and bank sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector acts as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.

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Cross-Sectoral Information Transfer in the Chinese Stock Market around Its Crash in 2015

Cited by 17 publications

References 62 publications

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

Dynamic Analyses of Contagion Risk and Module Evolution on the SSE A-Shares Market Based on Minimum Information Entropy

The Threshold Effect of Leveraged Trading on the Stock Price Crash Risk: Evidence from China

Information Flow Networks of Chinese Stock Market Sectors

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