A new identification method of the market downturns in Chinese A-shares using liquidity network

Abstract

With an increasing interest in big datasets, there exists a rise in the application of complex networks to financial big data. Many literatures have indicated that liquidity plays a crucial part in interpreting the fluctuation in stock returns. However, most researchers focus on relationship between the liquidity of individual stock and returns but seldom investigate market-wide liquidity. In this paper, we propose a way based on a dynamic stock liquidity network to analyze the change of market-wide liquidity. Then two indexes are built to quantify the daily change of stock liquidity correlation network, which are calculated from the maximum connected subgraph. When comparing our indexes with market index in Chinese A-shares, we empirically find that they are sensitive to market downturns and can recognize the tendency of stock market. Furthermore, the stock market investment behavior in Chinese A-shares tends to keep the same with previous investment behavior during market upturns and it differs apparently when market downturns.