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Diagnosis and Prediction of Market Rebounds in Financial Markets

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  • Wanfeng Yan
  • Ryan Woodard
  • Didier Sornette

Abstract

We introduce the concept of "negative bubbles" as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate describing the collective buying pressure of noise traders. The price fall occurring during a transient negative bubble can be interpreted as an effective random downpayment that rational agents accept to pay in the hope of profiting from the expected occurrence of a possible rally. We validate the model by showing that it has significant predictive power in identifying the times of major market rebounds. This result is obtained by using a general pattern recognition method which combines the information obtained at multiple times from a dynamical calibration of the JLS model. Error diagrams, Bayesian inference and trading strategies suggest that one can extract genuine information and obtain real skill from the calibration of negative bubbles with the JLS model. We conclude that negative bubbles are in general predictably associated with large rebounds or rallies, which are the mirror images of the crashes terminating standard bubbles.

Suggested Citation

  • Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Papers 1003.5926, arXiv.org, revised Mar 2011.
    • Handle: RePEc:arx:papers:1003.5926
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    1. Lux, Thomas & Sornette, Didier, 2002. "On Rational Bubbles and Fat Tails," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(3), pages 589-610, August.
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    7. Didier Sornette & Ryan Woodard & Maxim Fedorovsky & Stefan Reimann & Hilary Woodard & Wei-Xing Zhou, 2010. "The Financial Bubble Experiment: Advanced Diagnostics and Forecasts of Bubble Terminations Volume II-Master Document," Papers 1005.5675, arXiv.org, revised Nov 2010.
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    9. Didier Sornette & Ryan Woodard & Maxim Fedorovsky & Stefan Reimann & Hilary Woodard & Wei-Xing Zhou, 2009. "The Financial Bubble Experiment: advanced diagnostics and forecasts of bubble terminations," Papers 0911.0454, arXiv.org, revised May 2010.
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    Cited by:

    1. Yan, Wanfeng & Woodard, Ryan & Sornette, Didier, 2012. "Leverage bubble," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 180-186.
      • Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Leverage Bubble," Papers 1011.0458, arXiv.org, revised Nov 2010.
    2. Aaron Gerow & Mark Keane, 2012. "Mining the Web for the Voice of the Herd to Track Stock Market Bubbles," Papers 1212.2676, arXiv.org.
    3. Vakhtina, Elena & Wosnitza, Jan Henrik, 2015. "Capital market based warning indicators of bank runs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 304-320.
    4. Wanfeng Yan & Ryan Woodard & Didier Sornette, 2014. "Inferring fundamental value and crash nonlinearity from bubble calibration," Quantitative Finance, Taylor & Francis Journals, vol. 14(7), pages 1273-1282, July.
    5. Alexey Fomin & Andrey Korotayev & Julia Zinkina, 2016. "Negative oil price bubble is likely to burst in March - May 2016. A forecast on the basis of the law of log-periodical dynamics," Papers 1601.04341, arXiv.org.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G01 - Financial Economics - - General - - - Financial Crises
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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