

Records of the traded value f_i(t) of stocks display fluctuation
scaling, a proportionality between the standard deviation sigma(i) and
the average : σ(i) ~ f(i)^{α},
with a strong time scale
dependence α(dt). The nontrivial (i.e., neither 0.5 nor 1) value
of α may have different origins and provides information about the
microscopic dynamics. We present a set of recently discovered stylized
facts, and then show their connection to such behavior. The functional
form α(dt) originates from two aspects of the dynamics: Stocks of
larger companies both tend to be traded in larger packages, and also
display stronger correlations of traded value.

We construct a correlation matrix based financial network for a set of New York Stock Exchange (NYSE) traded stocks with stocks corresponding to nodes and the links between them added one after the other, according to the strength of the correlation between the nodes. The eigenvalue spectrum of the correlation matrix reflects the structure of the market, which also shows in the cluster structure of the emergent network. The stronger and more compact a cluster is, the earlier the eigenvalue representing the corresponding business sector occurs in the spectrum. On the other hand, if groups of stocks belonging to a given business sector are considered as a fully connected subgraph of the final network, their intensity and coherence can be monitored as a function of time. This approach indicates to what extent the business sector classifications are visible in market prices, which in turn enables us to gauge the extent of groupbehaviour exhibited by stocks belonging to a given business sector.


