INFERRING FINANCIAL STOCK RETURNS CORRELATION FROM COMPLEX NETWORK ANALYSIS

Ixandra Achitouv ()
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Ixandra Achitouv: Institut des Systèmes Complexes ISC-PIF, CNRS, 113 rue Nationale, Paris 75013, France

Advances in Complex Systems (ACS), 2025, vol. 28, issue 06, 1-18

Abstract: Financial stock returns correlations have been studied in the prism of random matrix theory to distinguish the signal from the “noise†. Eigenvalues of the matrix that are above the rescaled Marchenko–Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis, we use complex network analysis to simulate the “noise†and the “market†component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated “market†random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short-time scale.

Keywords: Complex network; stock returns correlation; stock market; random matrix theory (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219525925400053

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