Exploitation of Information as a Trading Characteristic: A Causality-Based Analysis of Simulated and Financial Data

Kyrtsou, Catherine; Mikropoulou, Christina; Papana, Angeliki

doi:10.3390/e22101139

Cited by 2 publications

(2 citation statements)

References 56 publications

(51 reference statements)

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“…The interactions between stocks and contagion risks are essential to understand the stock market fluctuations and global financial crisis [16][17][18]. The classical econometric methods rely on pairwise measurements including Pearson's correlation to describe the relationships within the network system [19][20][21][22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The interactions between stocks and contagion risks are essential to understand the stock market fluctuations and global financial crisis (Girardi and Ergün, 2013;Hautsch et al, 2015;Kyrtsou et al, 2020). The classical econometric methods rely on pair-wise measurements including the Pearson correlations to describe the relationships within the network system (Adeloye et al, 2015a,b;Papana et al, 2013;Rudan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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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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