In the last decade, financial networks have attracted more attention from the research community.
[1][2][3][4] Stock correlation network has proven its efficacy in predicting market movements.
Chakrabortia and Onella showed that the average distance between the stocks can be a significant indicator of market dynamics.
Andrew Lo and Khandaniy worked on the network of different hedge funds and observed the patterns before the August 2007 stock market turbulence.
[6] The basic approach for building the stock correlation network involves two steps.
The first step aims at finding the correlation between each pair of stock considering their corresponding time series.
The second step applies a criterion to connect the stocks based on their correlation.
Step 4: In case of the minimum spanning tree method a metric distance
The minimum spanning tree and planar maximally filtered graph may cause loss of information, i.e., some high correlation edges are discarded and low correlation edges are retained because of the topological reduction criteria.
[7] Tse, et al. introduced the winner take all connection criterion where in the drawback of minimum spanning tree and planar maximally filtered graph are eliminated.
Tse, et al. showed that for large values of threshold (0.7, 0.8, and 0.9) the stock correlation networks are scale free where the nodes linked in a manner that their degree distribution follows a power law.
[7] For small values of threshold, the network tends to be fully connected and does not exhibit scale free distribution.