Clustering analysis of listed companies in stock market based on chaotic map synchronization

Identification and clustering analysis for the listed companies in stock market is helpful to set down portfolio optimization strategies. In this paper, chaotic map clustering algorithm was used to establish correlated maps based on the stock prices of the listed companies, and the coupling strength associated with maps was related to the correlation coefficients of the financial time series. A pairwise clustering approach is applied to identify similarity of the listed companies which constituted Hang Seng mainland 25 index by using their stock prices. This empirical study shows that the simulation of a chaotic map dynamics leads to a natural partition of the data, since companies involving in the same industrial branch are usually grouped together.