  We analyse the structure of the distribution of eigenvalues of the stock
market correlation matrix with increasing length of the time series
representing the price changes. We use 100 highly-capitalized stocks from the
American market and relate result to the corresponding ensemble of Wishart
random matrices. It turns out that systematically more eigenvalues stay beyond
the borders prescribed by this variant of the Random Matrix Theory (RMT). This
may indicate that even the bulk of the spectrum of the stock market correlation
matrix carries some sort of correlations that are masked by a measurement noise
when the time series used to construct the matrix are short. We also study some
other characteristics of the "noisy" eigensignals, like their return
distributions, temporal correlations or their multifractal spectra and the
results support the above conclusions.
