Information Transfer between Stock Market Sectors: A Comparison between the USA and China Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the nonbank financial sector in the Chinese market and the technology sector in the USA market. This is consistent with the role of the nonbank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the bank sector in the Chinese market and the energy sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the real estate sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability.

Credit Risk Contagion Model Based on Financial Industry Clusters Financial industry clusters have been a new trend in modern economic development, and also provide potential channels for risk contagion among the financial system. The current credit risk contagion models mainly study the impact of the interconnectedness of the whole network on risk contagion, still lack consideration of the local risk exposuresu0027 characteristics under the impact of financial industry clusters. In view of this, we propose a credit risk contagion model based on financial industry clusters and analyze a scalefree financial network according to institution balance sheet. We detect financial industry clusters by identifying systemically important financial institutions. And by calculating the default probability of the institution in clusters, we propose a xe2x80x9ccluster contagion indexxe2x80x9d to assess the impact of local risk exposures on contagion. We verify the rationality of the model by comparing the default extent after the risk contagion under different network scales and different initial shocking scopes. The results of our model also highlight the impact that heterogeneous connectivity in clusters to magnify shocks, which will be significant for financial market regulators.