  We use methods of random matrix theory to analyze the cross-correlation
matrix C of price changes of the largest 1000 US stocks for the 2-year period
1994-95. We find that the statistics of most of the eigenvalues in the spectrum
of C agree with the predictions of random matrix theory, but there are
deviations for a few of the largest eigenvalues. We find that C has the
universal properties of the Gaussian orthogonal ensemble of random matrices.
Furthermore, we analyze the eigenvectors of C through their inverse
participation ratio and find eigenvectors with large inverse participation
ratios at both edges of the eigenvalue spectrum--a situation reminiscent of
results in localization theory.
