Analysis of the stock market signed network

Complex networks have been widely applied to financial systems, but only a few utilize signed networks for the stock market. Signed networks are constructed to simulate the Chinese stock market in the present work. Signed networks were generated by calculating correlations between stock returns in the most recent fluctuates, in both bull and bear markets and setting threshold value by optimal threshold and fixed threshold methods. The proportion of negative edges was found low and the majority were between bank stocks. The main focuse of the paper is signed stock network in the bull market. Topological properties were analyzed. Degree distribution, popularity measures and eigenvector centrality are studied on the microcosmic scale, while structural balance, clustering coefficient and degree correlations are studied on the macroscopic scale. The work also compares the signed network with traditional unsigned positive network to investigate the impact of negative edges. These data indicate that negative edges have no influence on the degree distribution but affect the importance of nodes greatly.