A network perspective of comovement and structural change: Evidence from the Chinese stock market

Abstract

How to appropriately characterize the comovement between any pair of individual stocks and describe the market comovement structure is a great challenge and plays a key role in understanding emerging markets. This paper applies the complex network approach to deal with this issue for the Chinese stock market. Firstly, in view of the topological properties, we investigate the time-varying comovement between individual stocks by constructing 14 directed weighted stock networks. Furthermore, the weighted LeaderRank algorithm is employed to describe the comovement structure of the entire market. Most importantly, from the perspective of fundamental factors and industry factors, we reveal the driving factors of the comovement and structural change of the entire market. The empirical results suggest that: (i) Stocks with higher weighted LeaderRank algorithm scores generally have more long-term investment value; and the so-called views, “too big to fail” and “too connected to fail”, are further confirmed. (ii) ROE, BMratio and Growth are significantly positively correlated with the comovement between individual stocks, and Mvalue is significantly negatively correlated during normal periods. However, during the crisis, the signs of regression coefficients of above four explanatory variables are reversed. (iii) In normal periods, we only find that the agriculture, forestry, animal husbandry & fishery and composite have significant influence on the comovement structure of the entire market. Besides, public utilities and medias also have a significant impact during the crisis. In addition, a very interesting fact in point is that network density, average clustering coefficient, and global efficiency can provide an “early warning” for possible upcoming crises.