  Financial stock returns correlations have been studied in the prism of random
matrix theory, to distinguish the signal from the "noise". Eigenvalues of the
matrix that are above the rescaled Marchenko Pastur distribution can be
interpreted as collective modes behavior while the modes under are usually
considered as noise. In this analysis we use complex network analysis to
simulate the "noise" and the "market" component of the return correlations, by
introducing some meaningful correlations in simulated geometric Brownian motion
for the stocks. We find that the returns correlation matrix is dominated by
stocks with high eigenvector centrality and clustering found in the network. We
then use simulated "market" random walks to build an optimal portfolio and find
that the overall return performs better than using the historical mean-variance
data, up to 50% on short time scale.
