Spectral and network methods in the analysis of correlation matrices of stock returns

Tapio Heimo, Jari Saramäki, Jukka-Pekka Onnela and Kimmo Kaski

Physica A: Statistical Mechanics and its Applications, 2007, vol. 383, issue 1, 147-151

Abstract: Correlation matrices inferred from stock return time series contain information on the behaviour of the market, especially on clusters of highly correlating stocks. Here we study a subset of New York Stock Exchange (NYSE) traded stocks and compare three different methods of analysis: (i) spectral analysis, i.e. investigation of the eigenvalue–eigenvector pairs of the correlation matrix, (ii) asset trees, obtained by constructing the maximal spanning tree of the correlation matrix, and (iii) asset graphs, which are networks in which the strongest correlations are depicted as edges. We illustrate and discuss the localisation of the most significant modes of fluctuation, i.e. eigenvectors corresponding to the largest eigenvalues, on the asset trees and graphs.

Keywords: Asset; Stock; Correlation; Complex networks; Spectral analysis (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:383:y:2007:i:1:p:147-151

DOI: 10.1016/j.physa.2007.04.124

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