  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.
