Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data

Abstract

Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of networks. To address this issue, we propose a new approach to select essential edges in stock networks and also maintain the connectivity of established networks. This approach uses different threshold values for choosing the edges connecting to a particular stock, rather than employing a single threshold value in the existing asset-value method. The innovation of our algorithm includes the multiple distributions in a maximum likelihood estimator for selecting the threshold value rather than the single distribution estimator in the existing methods. Using the Chinese Shanghai security market data of 151 stocks, we develop a stock relationship network and analyze the topological properties of the developed network. Our results suggest that the proposed method is able to develop networks that maintain appropriate connectivities in the type of assets threshold methods.

References in corpus (12)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Finding community structure in very large networks
• Uncovering the overlapping community structure of complex networks in nature and society
• The diameter of the world wide web
• Hierarchical Structure in Financial Markets
• Topology of correlation based minimal spanning trees in real and model markets
• Dynamic asset trees and portfolio analysis
• Asset trees and asset graphs in financial markets
• Structure of a large social network
• Complex Networks on Hyperbolic Surfaces
• Stock market as temporal network