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We use methods of random matrix theory to analyze the cross-correlationmatrix C of price changes of the largest 1000 US stocks for the 2-year period1994-95. We find that the statistics of most of the eigenvalues in the spectrumof C agree with the predictions of random matrix theory, but there aredeviations for a few of the largest eigenvalues. We find that C has theuniversal properties of the Gaussian orthogonal ensemble of random matrices.Furthermore, we analyze the eigenvectors of C through their inverseparticipation ratio and find eigenvectors with large inverse participationratios at both edges of the eigenvalue spectrum--a situation reminiscent ofresults in localization theory.Comment: 14 pages, 3 figures, Revte

Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series