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Financial "Anti-Bubbles": Log-Periodicity In Gold And Nikkei Collapses

Author

Listed:
  • A. JOHANSEN

    (Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California 90095, USA)

  • D. SORNETTE

    (Institute of Geophysics and Planetary Physics, University of California, Los Angeles, California 90095, USA;
    Department of Earth and Space Science, University of California, Los Angeles, California 90095, USA;
    Laboratoire de Physique de la Matière Condensée, CNRS UMR6622 and Université de Nice-Sophia Antipolis, B.P. 71, Parc Valrose, 06108 Nice Cedex 2, France)

Abstract

We propose that the herding behavior of traders leads not only to speculative bubbles with accelerating over-valuations of financial markets possibly followed by crashes, but also to "anti-bubbles" with decelerating market devaluations following all-time highs. For this, we propose a simple market dynamics model in which the demand decreases slowly with barriers that progressively quench in, leading to a power law decay of the market price characterized by decelerating log-periodic oscillations. We document this behavior of the Japanese Nikkei stock index from 1990 to present and of the gold future prices after 1980, both after their all-time highs. We perform simultaneously parametric and nonparametric analyses that are fully consistent with each other. We extend the parametric approach to the next order of perturbation, comparing the log-periodic fits with one, two and three log-frequencies, the latter providing a prediction for the general trend in the coming years. The nonparametric power spectrum analysis shows the existence of log-periodicity with high statistical significance, with a preferred scale ratio ofλ≈3.5for the Nikkei index andλ≈1.9for the Gold future prices, comparable to the values obtained for speculative bubbles leading to crashes.

Suggested Citation

  • A. Johansen & D. Sornette, 1999. "Financial "Anti-Bubbles": Log-Periodicity In Gold And Nikkei Collapses," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 563-575.
    • Handle: RePEc:wsi:ijmpcx:v:10:y:1999:i:04:n:s0129183199000437
    DOI: 10.1142/S0129183199000437
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    Citations

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    Cited by:

    1. Hans-Christian Graf v. Bothmer, 2003. "Significance of log-periodic signatures in cumulative noise," Papers cond-mat/0302507, arXiv.org, revised May 2003.
    2. Focardi, Sergio & Cincotti, Silvano & Marchesi, Michele, 2002. "Self-organization and market crashes," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 241-267, October.
    3. Zhou, Wei-Xing & Sornette, Didier, 2006. "Fundamental factors versus herding in the 2000–2005 US stock market and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 459-482.
    4. M. Ausloos & K. Ivanova & N. Vandewalle, 2001. "Crashes : symptoms, diagnoses and remedies," Papers cond-mat/0104127, arXiv.org, revised Apr 2001.
    5. Wei-Xing Zhou & Didier Sornette, 2003. "Nonparametric Analyses Of Log-Periodic Precursors To Financial Crashes," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 14(08), pages 1107-1125.
    6. Boon Kin Teh & Siew Ann Cheong, 2016. "The Asian Correction Can Be Quantitatively Forecasted Using a Statistical Model of Fusion-Fission Processes," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-13, October.
    7. Didier Sornette & Wei-Xing Zhou, 2003. "The US 2000-2002 market descent: clarification," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 39-41.
    8. Piotr Gnacinski & Danuta Makowiec, 2003. "Another type of log-periodic oscillations on Polish stock market?," Papers cond-mat/0307323, arXiv.org, revised Aug 2003.
    9. Wanfeng Yan & Edgar van Tuyll van Serooskerken, 2015. "Forecasting Financial Extremes: A Network Degree Measure of Super-Exponential Growth," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.
    10. A. Johansen & D. Sornette, 2002. "Endogenous versus Exogenous Crashes in Financial Markets," Papers cond-mat/0210509, arXiv.org.
    11. George Chang & James Feigenbaum, 2006. "A Bayesian analysis of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 6(1), pages 15-36.
    12. Petr Geraskin & Dean Fantazzini, 2013. "Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
    13. Bikramaditya Ghosh & Spyros Papathanasiou & Nikita Ramchandani & Dimitrios Kenourgios, 2021. "Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT," Mathematics, MDPI, vol. 9(9), pages 1-14, April.
    14. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    15. Fry, John, 2012. "Exogenous and endogenous crashes as phase transitions in complex financial systems," MPRA Paper 36202, University Library of Munich, Germany.
    16. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    17. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.

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