Scale-free Network in Financial Correlations

Abstract

We study the cross-correlations in stock price changes between the S&P 500 companies by introducing a weighted random graph, where all vertices (companies) are fully connected, and each edge is weighted. The weight assigned to each edge is given by the normalized covariance of the two modified returns connected, so that it is ranged from -1 to 1. Here the modified return means the deviation of a return from its average over all companies. We define influence-strength at each vertex as the sum of the weights on the edges incident upon that vertex. Then we found that the influence-strength distribution in its absolute magnitude $|s|$ follows a power-law, $P(|s|)\sim |s{|}^{-\delta }$, with exponent $\delta \approx 1.8(1)$.

References in corpus (3)

• Spectra of "Real-World" Graphs: Beyond the Semi-Circle Law
• Weighted Evolving Networks
• Spectra and eigenvectors of scale-free networks

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