  Transfer entropy measures the strength and direction of information flow
between different time series. We study the information flow networks of the
Chinese stock market and identify important sectors and information flow paths.
This paper uses the daily closing price data of the 28 level-1 sectors from
Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information
transmission between different sectors. We construct information flow networks
with the sectors as the nodes and the transfer entropy between them as the
corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to
extracting important information flows and the hierarchical structure of the
networks. We find that, during the whole sample period, the \textit{composite}
sector is an information source of the whole stock market, while the
\textit{non-bank financial} sector is the information sink. We also find that
the \textit{non-bank finance}, \textit{bank}, \textit{computer},
\textit{media}, \textit{real estate}, \textit{medical biology} and
\textit{non-ferrous metals} sectors appear as high-degree root nodes in the
outgoing and incoming information flow MSAs. Especially, the \textit{non-bank
finance} and \textit{bank} sectors have significantly high degrees after 2008
in the outgoing information flow networks. We uncover how stock market turmoils
affect the structure of the MSAs. Finally, we reveal the specificity of
information source and sink sectors and make a conclusion that the root node
sector as the information sink of the incoming information flow networks.
Overall, our analyses show that the structure of information flow networks
changes with time and the market exhibits a sector rotation phenomenon. Our
work has important implications for market participants and policy makers in
managing market risks and controlling the contagion of risks.
