Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data

Yan, Yan; Wu, Boyao; Tian, Tianhai; Zhang, Hu

doi:10.3390/e22070773

Cited by 24 publications

(20 citation statements)

References 44 publications

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“…It is possible to use mutual information (MI) as a similarity measure between the stocks [16,59]. For simplicity, we do not include t, τ , and T in the notations of the return series in subsequent discussions.…”

Section: Nonlinear Measurement-based Graphsmentioning

confidence: 99%

A survey of the application of graph-based approaches in stock market analysis and prediction

Saha

Gao

Gerlach

2022

Int J Data Sci Anal

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Graph-based approaches are revolutionizing the analysis of different real-life systems, and the stock market is no exception. Individual stocks and stock market indices are connected, and interesting patterns appear when the stock market is considered as a graph. Researchers are analyzing the stock market using graph-based approaches in recent years, and there is a need to survey those works from multiple perspectives. We discuss the existing graph-based works from five perspectives: (i) stock market graph formulation, (ii) stock market graph filtering, (iii) stock market graph clustering, (iv) stock movement prediction, and (v) portfolio optimization. This study contains a concise description of major techniques and algorithms relevant to graph-based approaches for the stock market.

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Section: Nonlinear Measurement-based Graphsmentioning

confidence: 99%

A survey of the application of graph-based approaches in stock market analysis and prediction

Saha

Gao

Gerlach

2022

Int J Data Sci Anal

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“…The premise of understanding the mechanism of economic development is to measure the structural complexity of the entire system, which is made easier by constructing complex networks based on economic physics [ 1 , 2 ]. Complex network refers to the existence of many nodes or variables in the system and the relationship between these nodes [ 3 ]. Digital economy is an advanced stage of the development of information economy and informatization, and the flow of information is complex and uncertain.…”

Section: Introductionmentioning

confidence: 99%

Spatial Correlation Network and Regional Differences for the Development of Digital Economy in China

Tang

Tian

2021

Entropy

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The rapid development of the digital economy is a powerful driving force to promote high-quality economic growth all over the world. Although a number of studies have been conducted to investigate the development of the digital economy in China, these studies pay little attention to the spatial linkages between the 30 provinces in China and the developmental differences between northern and southern China. Using Chinese digital economic data from 2004 to 2019, we propose an index system to measure the developmental levels of the digital economy and obtain the annual developmental levels of these provinces by using the factor analysis method. We analyze the regional differences of developmental levels by using the Theil index and kernel density estimation method. More importantly, the network method is used to analyze the correlations between the developmental levels of the digital economy in all provinces of China. By decomposing regional differences, our study shows that polarized and uncoordinated development is prominent. The development level of the digital economy in the southern region is higher than that in the northern region. In terms of regional correlations, the network study suggests that there are beneficial and spillover effects of the digital economy development between provinces. Based on the analysis results, we propose policies for improving the development of the digital economy in China.

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“…Although the multivariate regression model can characterise interactions between equities in a systemic way, this framework may fail to effectively fit the financial data due to the highdimensional problem that limited observations are used to estimate a significant number of parameters reflecting relationships of stocks [11][12][13][14]. To overcome these above issues, we use the Least Absolute Shrinkage and Selection Operator (LASSO) method to model the SSE A-shares market's network by measuring statistically significant connections in the equities system and shrinking insignificant ones into zeros [6,15]. More importantly, we also use the Map Equation method to conduct a dynamic analysis of financial contagion patterns in the SSE A-shares market.…”

Section: Introductionmentioning

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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Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data

Cited by 24 publications

References 44 publications

A survey of the application of graph-based approaches in stock market analysis and prediction

A survey of the application of graph-based approaches in stock market analysis and prediction

Spatial Correlation Network and Regional Differences for the Development of Digital Economy in China

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

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