  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.
