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Inferring Financial Stock Returns Correlation From Complex Network Analysis

Author

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  • Ixandra Achitouv

    (ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France - ENS Cachan - École normale supérieure - Cachan - UP1 - Université Paris 1 Panthéon-Sorbonne - Institut Curie [Paris] - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

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 [Formula: see text] on short-time scale.

Suggested Citation

  • Ixandra Achitouv, 2025. "Inferring Financial Stock Returns Correlation From Complex Network Analysis," Post-Print hal-05410282, HAL.
    • Handle: RePEc:hal:journl:hal-05410282
    DOI: 10.1142/S0219525925400053
    as

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