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The cross-correlations between price fluctuations of 201 frequently tradedstocks in the National Stock Exchange (NSE) of India are analyzed in thispaper. We use daily closing prices for the period 1996-2006, which coincideswith the period of rapid transformation of the market following liberalization.The eigenvalue distribution of the cross-correlation matrix, $\mathbf{C}$, ofNSE is found to be similar to that of developed markets, such as the New YorkStock Exchange (NYSE): the majority of eigenvalues fall within the boundsexpected for a random matrix constructed from mutually uncorrelated timeseries. Of the few largest eigenvalues that deviate from the bulk, the largestis identified with market-wide movements. The intermediate eigenvalues thatoccur between the largest and the bulk have been associated in NYSE withspecific business sectors with strong intra-group interactions. However, in theIndian market, these deviating eigenvalues are comparatively very few and liemuch closer to the bulk. We propose that this is because of the relative lackof distinct sector identity in the market, with the movement of stocksdominantly influenced by the overall market trend. This is shown by explicitconstruction of the interaction network in the market, first by generating theminimum spanning tree from the unfiltered correlation matrix, and later, usingan improved method of generating the graph after filtering out the market modeand random effects from the data. Both methods show, compared to developedmarkets, the relative absence of clusters of co-moving stocks that belong tothe same business sector. This is consistent with the general belief thatemerging markets tend to be more correlated than developed markets.

Uncovering the Internal Structure of the Indian Financial Market: Cross-correlation behavior in the NSE