Financial risk propagation between Chinese and American stock markets based on multilayer networks

Huang, Qian; Zhao, Junchan; Wu, Xiaoqun

doi:10.1016/j.physa.2021.126445

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…The utility of macroeconomic attention-based indicators in predicting stock market return was acknowledged by Ma et al (2022). A large proportion of the literature reports the usage of machine learning methodologies to establish the dependence of market movements on cognate aspects of COVID-19 in place of explicit predictive modeling exercises (Asl et al 2022;Dey et al 2022;Lúcio and Caiado 2022;Huang et al 2022). The review suggests that the macroeconomic and media chatter were primarily used in an isolated manner for forecasting future figures for the respective stock markets.…”

Section: Predictive Modeling Of Stock Markets During Covid-19mentioning

confidence: 99%

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

Ghosh

Alfaro

Gámez

et al. 2023

Risks

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Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The present research attempts to propound a robust framework capable of channeling macroeconomic reflectors and essential media chatter-linked variables to draw precise forecasts of future figures for Spanish and Indian stock markets. The predictive structure combines Isometric Mapping (ISOMAP), which is a non-linear feature transformation tool, and Gradient Boosting Regression (GBR), which is an ensemble machine learning technique to perform predictive modelling. The Explainable Artificial Intelligence (XAI) is used to interpret the black-box type predictive model to infer meaningful insights. The overall results duly justify the incorporation of local and global media chatter indices in explaining the dynamics of respective financial markets. The findings imply marginally better predictability of Indian stock markets than their Spanish counterparts. The current work strives to compare and contrast the reaction of developed and developing financial markets during the COVID-19 pandemic, which has been argued to share a close resemblance to the Black Swan event when applying a robust research framework. The insights linked to the dependence of stock markets on macroeconomic indicators can be leveraged for policy formulations for augmenting household finance.

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Section: Predictive Modeling Of Stock Markets During Covid-19mentioning

confidence: 99%

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

Ghosh

Alfaro

Gámez

et al. 2023

Risks

View full text Add to dashboard Cite

show abstract

“…In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, namely, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng (medium), it established a two-layer Granger networks [ 10 , 11 ]. These studies show the potential of deep learning financial time series in the presence of a large number of noise data, which makes the application of deep learning in intelligent investment and risk management and control.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

Sun

2022

Computational Intelligence and Neuroscience

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Financial investment promotes the market’s fast economic growth and gradually becomes a new trend of social development in the contemporary era. From the national level, financial risk investment activities directly affect the development process of the information technology industry and social and economic benefits. The management of financial risk investment has received more attention, and the types and difficulties of risks are also gradually increasing. Financial regulatory agencies urgently need to establish a sensitive and scientific economic hazard early alarm system. The perfect earlier alarm system stands based on in-depth scientific theoretical research, so studying financial security evaluation and systemic economic earlier alarm systems is of great practical significance. Taking the systemic financial risk as the research object, this paper analyzes the mechanism of financial systemic risk. After that, deep learning technology in financial investment has been used for the first time to reconstruct the index system of financial security evaluation and early warning. The application of deep learning technology in the early warning of systemic financial risks is realized, which provides a reliable basis for the regulatory authorities to build a financial risk early warning system and makes empirical research.

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“…In the current research, multilayer networks have been used in several academic fields. Examples include the financial stock market [33], transportation systems [34], and national energy trade [35]. The complexity of many bio-logical, social, and technological systems stems from the richness of the interactions among their units [36].…”

Section: Introductionmentioning

confidence: 99%

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Jun-li

Wang

et al. 2022

IJGI

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Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and co-occurrence theory into a coupled grain–disaster–economy (GDE) multilayer network, which provides a new perspective to further explore the spatial correlation between these three systems. We identify the spatial coupled characteristics of the GDE multilayer network using three aspects: degree, centrality, and community detection. The research results show the following: (1) Provinces in the major grain-producing regions have a stronger role in allocating and controlling grain resources, and the correlation between grain and disasters in these provinces is stronger and more prone to disasters. Whereas provinces in the Beijing–Tianjin–Hebei economic zone, and the Yangtze River Delta and Pearl River Delta economic zones, such as Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, have a high level of economic development, thereby a stronger ability to allocate economic resources. (2) The economic subsystem assumes a more important, central role compared with the grain and disaster subsystems in the formation and development of the coupled GDE multilayer network, with a stronger coordination for the co-development between the complex grain, disaster, and economy systems in the nodal provinces of the network. (3) The community modularity of the coupled GDE multilayer network is significantly higher than that of the three single-layer networks, indicating a more reasonable community division after coupling the three subsystems. The identification of the spatial characteristics of GDE using multilayer network analysis offers a new perspective on taking various measures to improve the joint sustainable development of grain, disaster, and the economy in different regions of China according to local conditions.

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Financial risk propagation between Chinese and American stock markets based on multilayer networks

Cited by 9 publications

References 36 publications

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

Deep Learning for Intelligent Assessment of Financial Investment Risk Prediction

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

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