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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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    File URL: http://arxiv.org/pdf/1003.5926
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Anders Johansen & Didier Sornette, 1999. "Critical Crashes," Papers cond-mat/9901035, arXiv.org.
    4. Refet S. G├╝rkaynak, 2008. "Econometric Tests Of Asset Price Bubbles: Taking Stock ," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 166-186, February.
    5. Ide, Kayo & Sornette, Didier, 2002. "Oscillatory finite-time singularities in finance, population and rupture," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 307(1), pages 63-106.
    6. Sornette, Didier & Johansen, Anders, 1997. "Large financial crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 411-422.
    7. Jiang, Zhi-Qiang & Zhou, Wei-Xing & Sornette, Didier & Woodard, Ryan & Bastiaensen, Ken & Cauwels, Peter, 2010. "Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 149-162, June.
    8. Graf v. Bothmer, Hans-Christian & Meister, Christian, 2003. "Predicting critical crashes? A new restriction for the free variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 539-547.
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    Cited by:

    1. 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.
    2. 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.
    3. Aaron Gerow & Mark Keane, 2012. "Mining the Web for the Voice of the Herd to Track Stock Market Bubbles," Papers 1212.2676, arXiv.org.

    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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