  Investigation of the market graph attracts a growing attention in market
network analysis. One of the important problem connected with market graph is
to identify it from observations. Traditional way for the market graph
identification is to use a simple procedure based on statistical estimations of
Pearson correlations between pairs of stocks. Recently a new class of
statistical procedures for the market graph identification was introduced and
optimality of these procedures in Pearson correlation Gaussian network was
proved. However the obtained procedures have a high reliability only for
Gaussian multivariate distributions of stocks attributes. One of the way to
correct this drawback is to consider a different networks generated by
different measures of pairwise similarity of stocks. A new and promising model
in this context is the sign similarity network. In the present paper the market
graph identification problem in sign similarity network is considered. A new
class of statistical procedures for the market graph identification is
introduced and optimality of these procedures is proved. Numerical experiments
detect essential difference in quality of optimal procedures in sign similarity
and Pearson correlation networks. In particular it is observed that the quality
of optimal identification procedure in sign similarity network is not sensitive
to the assumptions on distribution of stocks attributes.
