Topological evolution of financial network: A genetic algorithmic approach

Abstract

The structure of financial market is captured using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series would be changing as well, which would not be well captured with correlation measurements. The proposed algorithm is applied on Dow Jones Index (DJI) and its compositions consisting of 30 stocks, and different measurements are performed to observe the evolution of the network structure. It is suggested that there are major topological changes during market crashes, leading to a significant decrease in the size of the network.