Muzi Chen, Nan Li, Lifen Zheng, Difang Huang, Boyao Wu
arXiv 28 Mar 2024 · Econometrics · publishedPhysica A Statistical Mechanics and its Applications (2021) · 63 citations (OpenAlex)
arXiv:2403.19363 · PDF · DOI · OpenAlex · Extracted main text
The connectivity of stock markets reflects the information efficiency of capital markets and contributes to interior risk contagion and spillover effects. We compare Shanghai Stock Exchange A-shares (SSE A-shares) during tranquil periods, with high leverage periods associated with the 2015 subprime mortgage crisis. We use Pearson correlations of returns, the maximum strongly connected subgraph, and $3\sigma$ principle to iteratively determine the threshold value for building a dynamic correlation network of SSE A-shares. Analyses are carried out based on the networking structure, intra-sector connectivity, and node status, identifying several contributions. First, compared with tranquil periods, the SSE A-shares network experiences a more significant small-world and connective effect during the subprime mortgage crisis and the high leverage period in 2015. Second, the finance, energy and utilities sectors have a stronger intra-industry connectivity than other sectors. Third, HUB nodes drive the growth of the SSE A-shares market during bull periods, while stocks have a think-tail degree distribution in bear periods and show distinct characteristics in terms of market value and finance. Granger linear and non-linear causality networks are also considered for the comparison purpose. Studies on the evolution of inter-cycle connectivity in the SSE A-share market may help investors improve portfolios and develop more robust risk management policies.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | T. K. Lee, J. H. Cho, D. S. Kwon, S. Y. Sohn (2019) Global stock market investment strategies based on financial network indicators using machine learning techniques | 0.644 | 2 | 2 | 100% |
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| 4 | L. Zhao, W. Li, X. Cai (2016) Structure and dynamics of stock market in times of crisis | 0.511 | 2 | 1 | 100% |
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| 10 | A. Clauset, C. R. Shalizi, M. E. J. Newman (2009) Power-law distributions in empirical data | 0.405 | 1 | 1 | 100% |
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