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A behavioral approach to instability pathways in financial markets

Author

Listed:
  • Alessandro Spelta

    (University of Pavia
    Human Technopole)

  • Andrea Flori

    (Politecnico di Milano)

  • Nicolò Pecora

    (Catholic University)

  • Sergey Buldyrev

    (Yeshiva University)

  • Fabio Pammolli

    (Human Technopole
    Politecnico di Milano)

Abstract

We introduce an indicator that aims to detect the emergence of market instabilities by quantifying the intensity of self-organizing processes arising from stock returns’ co-movements. In financial markets, phenomena like imitation, herding and positive feedbacks characterize the emergence of endogenous instabilities, which can modify the qualitative and quantitative behavior of the underlying system. The impossibility to formalize ex-ante the dynamic laws that rule the evolution of financial systems motivates the use of a parsimonious synthetic indicator to detect the disruption of an existing equilibrium configuration. Here we show that the emergence of an interconnected sub-graph of stock returns co-movements from a broader market index is a signal of an out-of-equilibrium transition of the underlying system. To test the validity of our approach, we propose a model-free application that builds on the identification of up and down market phases.

Suggested Citation

  • Alessandro Spelta & Andrea Flori & Nicolò Pecora & Sergey Buldyrev & Fabio Pammolli, 2020. "A behavioral approach to instability pathways in financial markets," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15356-z
    DOI: 10.1038/s41467-020-15356-z
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    Cited by:

    1. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    2. Flori, Andrea & Pammolli, Fabio & Spelta, Alessandro, 2021. "Commodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions," Journal of Financial Stability, Elsevier, vol. 54(C).
    3. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    4. Xi, Xian & Gao, Xiangyun & Zhou, Jinsheng & Zheng, Huiling & Ding, Jiazheng & Si, Jingjian, 2021. "Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels," International Review of Financial Analysis, Elsevier, vol. 76(C).
    5. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    6. Pagnottoni, Paolo & Spelta, Alessandro & Flori, Andrea & Pammolli, Fabio, 2022. "Climate change and financial stability: Natural disaster impacts on global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    7. Maria Elena Giuli & Alessandro Spelta, 2023. "Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices," Computational Management Science, Springer, vol. 20(1), pages 1-17, December.

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