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Development of stock networks is an important approach to explore therelationship between different stocks in the era of big-data. Although a numberof methods have been designed to construct the stock correlation networks, itis still a challenge to balance the selection of prominent correlations andconnectivity of networks. To address this issue, we propose a new approach toselect essential edges in stock networks and also maintain the connectivity ofestablished networks. This approach uses different threshold values forchoosing the edges connecting to a particular stock, rather than employing asingle threshold value in the existing asset-value method. The innovation ofour algorithm includes the multiple distributions in a maximum likelihoodestimator for selecting the threshold value rather than the single distributionestimator in the existing methods. Using the Chinese Shanghai security marketdata of 151 stocks, we develop a stock relationship network and analyze thetopological properties of the developed network. Our results suggest that theproposed method is able to develop networks that maintain appropriateconnectivities in the type of assets threshold methods.

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