Abstract
Previous research on financial time-series data mainly focused on the analysis of market evolution and trends, ignoring its characteristics in different resolutions and stages. This paper discusses the evolution characteristics of the financial market in different resolutions, and presents a method of complex network analysis based on wavelet transform. The analysis method has proven the linkage effects of the plate sector in China’s stock market and has that found plate drift phenomenon occurred before and after the stock market crash. In addition, we also find two different evolutionary trends, namely the W-type and M-type trends. The discovery of linkage plate and drift phenomena are important and referential for enterprise investors to build portfolio investment strategy, and play an important role for policy makers in analyzing evolution characteristics of the stock market.
Highlights
In recent years, an increasing amount of scholars have begun new research and discussion on dynamic evolution analysis and trends of financial time series
As eigenvector centrality (EC) is used to describe the role of plate nodes in promoting the overall development of the stock market, we summarize the dominant resolution path and dominant plate in the evolution process of the degree of integration of the stock market based on different resolutions in different stages
Previous research mainly focused on the evolution and trend analysis of financial time series without considering the analysis of financial time series in different resolutions and stages
Summary
An increasing amount of scholars have begun new research and discussion on dynamic evolution analysis and trends of financial time series. In order to analyze collectivization characteristics of the stock market, Cai et al [19] constructed a time-series influence matrix to reconstruct complex networks and divided the clusters by using association propagation. The research on financial time series mainly focuses on the evolution and trend analysis of the whole stock market. Based on the similar structural relations among stock time series, the network is constructed to analyze the evolution and trend characteristics of different resolutions between plates in different stages. In contrast to the traditional research, an innovative complex network model based on wavelet was proposed (see Figure 1) This model includes the MODWT (used in signal analysis field), the Pearson correlation coefficient, and the complex network method.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
