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Information Feedback in Temporal Networks as a Predictor of Market Crashes

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  • Stjepan Begušić
  • Zvonko Kostanjčar
  • Dejan Kovač
  • H. Eugene Stanley
  • Boris Podobnik

Abstract

In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.

Suggested Citation

  • Stjepan Begušić & Zvonko Kostanjčar & Dejan Kovač & H. Eugene Stanley & Boris Podobnik, 2018. "Information Feedback in Temporal Networks as a Predictor of Market Crashes," Complexity, Hindawi, vol. 2018, pages 1-13, September.
    • Handle: RePEc:hin:complx:2834680
    DOI: 10.1155/2018/2834680
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    1. Bolt, Wilko & Demertzis, Maria & Diks, Cees & Hommes, Cars & Leij, Marco van der, 2019. "Identifying booms and busts in house prices under heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 103(C), pages 234-259.
    2. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    3. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    5. Bolt, W. & Demertzis, D. & Diks, C.G.H. & Van der Leij, M.J., 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," CeNDEF Working Papers 11-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    6. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517, Decembrie.
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    Cited by:

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    8. Dey, Asim K. & Hoque, G.M. Toufiqul & Das, Kumer P. & Panovska, Irina, 2022. "Impacts of COVID-19 local spread and Google search trend on the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).

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