Abstract
From the cross perspective of media emotion and multilayer network correlation, this paper uses big data technology to mine the data of news reports about bank liquidity, constructs a bank liquidity multilayer network including different media emotional layers, and then analyzes the correlation characteristics of interbank liquidity risk. The main conclusions are as follows: there are banks with higher strength and activity in the bank liquidity multilayer network. These systemically important banks are large state-owned banks and joint-stock commercial banks with large assets. However, among systemically important banks based on strength, large state-owned banks are dominant, and when based on activity, joint-stock commercial banks have more significant influence. The interlayer correlation of the bank liquidity multilayer network is high, and the importance of banks is relatively consistent in different layers. A bank liquidity multilayer network has the characteristics of a small world network with smaller average path length and larger clustering coefficient. The interlayer similarity of the bank liquidity multilayer network is low, and the substitution is poor between positive media emotional network layer and negative media emotional network layer.
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