An empirical study on network conversion of stock time series based on STL method

Author:

Tian Feng1ORCID,Wang Dan1,Wu Qin1,Wei Daijun1ORCID

Affiliation:

1. School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei 445000, China

Abstract

A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the [Formula: see text] method and the visibility graph further highlights the advantages of the [Formula: see text] method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.

Funder

National Natural Science Foundation of China

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

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