Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer‐Coupled Networks

Dong, Yue; Wang, Jiepeng; Chen, Tingqiang

doi:10.1155/2019/4727868

Cited by 8 publications

(7 citation statements)

References 46 publications

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“…In [18], the authors proposed a bilayer network model to investigate the interaction mechanism between information spread and credit risk contagion. In [19], based on the epidemic spread of price linkage rumors, the authors explored the contagion of investor risk and discovered global and local effect of investor heterogeneity.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Diffusion of Investment Decisions on Modular Social Networks

Cheng

Zhao

2020

Complexity

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In the financial market, information and investment behaviors disseminate in investor social networks, and different contagion patterns may cause diverse investment trends. Prior studies have investigated the impact of investor social networks, but few have considered community structure. In this paper, we study the impact of the community structure of investor social networks on the diffusion of internet investment products. A two-stage diffusion model is proposed, and the clustering coefficient and modularity of an investor social network are considered. The results show that both modularity and the clustering coefficient have an impact on the diffusion velocity and scale and that the impact is most evident at the stage of explosive growth. The negative influence of a large modularity can be hardly mitigated by adjusting other factors. Furthermore, a decrease in modularity and an increase in the clustering coefficient can better facilitate diffusion when the temporary investment rate is high and can partly offset the negative impact of information discarding and divestment.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling the Diffusion of Investment Decisions on Modular Social Networks

Cheng

Zhao

2020

Complexity

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“…However, this relation loses its significance when the sentiment is high. Dong et al discussed the effects of different mechanism probabilities and investor heterogeneity on the risk contagion effect of the stock market [26]. Wang examined the role of institutional investor sentiment in determining betareturn relationships, and through empirical analysis showed that the beta-return relationship is asymmetric between the bearish period and the bullish period [27].…”

Section: Introductionmentioning

confidence: 99%

The Measurement Method of Investor Sentiment and Its Relationship with Stock Market

Sui

2021

Computational Intelligence and Neuroscience

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Investor sentiment is a hot topic in behavioral finance. How to measure investor sentiment? Is the influence of investor sentiment on the stock market symmetrical? That is all we need to think about. Therefore, this paper firstly selects five emotional proxy variables and constructs an investor sentiment composite index by principal component analysis. Secondly, the MS-VAR model is employed to study the dynamic relationship among investor sentiment, stock market returns, and volatility. Using the model MSIH (2)-VAR (2), we found that the relationship among the investor sentiment, stock returns, and volatility is different in different regimes. The results of orthogonal cumulative impulse response analysis showed that the shock to investor sentiment has a significant impact on stock market returns, and this impact in the bullish stock market is significantly higher than in the bearish stock market. The impact of the shock to stock market returns on investor sentiment and stock market volatility is relatively significant. The shock to stock market volatility has significant effects on the stock market returns. Overall, the influence of investor sentiment on the stock market is asymmetric; that is, in different regimes of the stock market, the impact of investor sentiment on the stock market is different. Realizing this, investors can better understand and grasp the market, guiding their own investment behavior. Other researchers can also further study the measurement of investor sentiment on this basis to better guide investors’ behavior.

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“…is case in turn will not only amplify the initial impact of the enterprise but also bring deterioration in the financial situation. From the perspective of stock price fluctuation, Dong et al [48] constructed a single-layer network SCIR investor risk contagion model considering the heterogeneity of investors based on the single-layer network SCIR investor risk contagion model. ey constructed a two-layer coupling network investor risk contagion model and compared the evolution characteristics Complexity and rules of investor risk contagion under different connection modes and mechanism probabilities.…”

Section: Introductionmentioning

confidence: 99%

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

et al. 2020

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Banks and enterprises constitute a multilayered, multiattribute, multicriteria credit-related super network due to financial transaction behaviors, such as credit, wealth management, savings, and derivatives. Such a network has become an important channel for credit risk cross-contagion. This study constructs a two-layer network model of credit risk contagion between the bank and corporate counterparties from the perspective that banks do not withdraw loans from enterprises by considering the influence of corporate credit defaults on their counterparties under the credit linkage. This study analyzes the mechanism of influencing the evolution of bank-enterprise counterparty credit risk contagion in the two-tier network through theoretical analysis, including the following: the enterprises’ coping ability, risk preference, influence, level of interenterprise credit risk contagion and its network heterogeneity in the interenterprise credit association network, the risk prevention and control ability, business correlation degree, interbank credit risk contagion and its network heterogeneity in the interbank credit association network, the level of credit risk contagion between bank-enterprise counterparty credit association networks, and other factors in the case that banks do not withdraw loans from enterprises. In addition, this study performs a calculation experiment to analyze the characteristics of the evolution of counterparty credit risk contagion of bank and corporate counterparties under the double-layer network. The following four major conclusions can be drawn from the results. First, in the interenterprise credit-related network, the threshold of credit risk contagion rate is positively correlated with the marginal increase in risk perception and risk leveling ability of the enterprise. By contrast, such threshold is negatively correlated with the marginal decrease in the initial economic impact, leverage level, and influence of the enterprise. Moreover, the scale of corporate counterparty credit risk contagion is negatively correlated with the enterprise’s risk perception level and risk spillover ability but positively correlated with the enterprise’s initial economic shock level, the enterprise’s leverage level, and influence. Second, in the interbank credit association network, the threshold of the rate of credit risk contagion is negatively correlated with the marginal decrease in the degree of interbank business association but positively correlated with the marginal increase in the bank’s risk resistance ability and risk information processing ability. Furthermore, the scale of credit risk contagion of bank counterparties is positively correlated with the degree of interbank business association but negatively correlated with the bank’s ability to resist risks and process risk information. Third, if the heterogeneity of the credit-related network of bank-enterprise counterparties is high, then the rate threshold of credit risk contagion is high and the scale of credit risk diffusion is low. Moreover, the scale of credit risk contagion of bank counterparties is positively correlated with the marginal decrease in the degree of corporate and bank counterparties. Finally, the scale of bank counterparty credit risk contagion is a monotonically increasing convex function of the credit risk contagion rate in the enterprise credit association network and among the bank-enterprise networks.

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Price Linkage Rumors in the Stock Market and Investor Risk Contagion on Bilayer‐Coupled Networks

Cited by 8 publications

References 46 publications

Modelling the Diffusion of Investment Decisions on Modular Social Networks

Modelling the Diffusion of Investment Decisions on Modular Social Networks

The Measurement Method of Investor Sentiment and Its Relationship with Stock Market

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

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