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Stock Market Crashes In 2007–2009: Were We Able To Predict Them?

In: Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis

Author

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  • Sébastien Lleo

    (Reims Management School, France)

  • William T. Ziemba

    (University of British Columbia, Canada and University of Reading, UK)

Abstract

We investigate the stock market crashes in China, Iceland, and the US in the 2007–2009 period. The bond stock earnings yield difference model is used as a prediction tool. Historically, when the measure is too high, meaning that long bond interest rates are too high relative to the trailing earnings over price ratio, then there usually is a crash of 10% or more within four to twelve months. The model did in fact predict all three crashes. Iceland had a drop of fully 95%, China fell by two thirds and the US by 57%.

Suggested Citation

  • Sébastien Lleo & William T. Ziemba, 2013. "Stock Market Crashes In 2007–2009: Were We Able To Predict Them?," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 13, pages 457-499, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789814417501_0013
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    1. Lleo, Sébastien & Ziemba, William T., 2015. "Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world," International Journal of Forecasting, Elsevier, vol. 31(2), pages 399-425.
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    Cited by:

    1. Boubaker, Sabri & Liu, Zhenya & Sui, Tianqing & Zhai, Ling, 2022. "The mirror of history: How to statistically identify stock market bubble bursts," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 128-147.
    2. Lleo, Sebastien & Ziemba, William, 2017. "A tale of two indexes: predicting equity market downturns in China," LSE Research Online Documents on Economics 118952, London School of Economics and Political Science, LSE Library.
    3. Lleo, Sebastien & Ziemba, William, 2017. "A tale of two indexes: predicting equity market downturns in China," LSE Research Online Documents on Economics 85131, London School of Economics and Political Science, LSE Library.
    4. Zhidong Bai & Hua Li & Michael McAleer & Wing-Keung Wong, 2015. "Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 889-900, May.
    5. Mo, Guoli & Tan, Chunzhi & Zhang, Weiguo & Liu, Fang, 2019. "International portfolio of stock indices with spatiotemporal correlations: Can investors still benefit from portfolio, when and where?," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 168-183.
    6. Shiryaev, Albert N. & Zhitlukhin, M. V. & Ziemba, William T., 2013. "When to sell Apple and the NASDAQ? Trading bubbles with a stochastic disorder model," LSE Research Online Documents on Economics 60966, London School of Economics and Political Science, LSE Library.
    7. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    8. Lleo, Sébastien & Ziemba, William T., 2015. "Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world," International Journal of Forecasting, Elsevier, vol. 31(2), pages 399-425.
    9. Lleo, Sebastien & Zhitlukhin, Mikhail & Ziemba, William, 2021. "Using a mean changing stochastic processes exit-entry model for stock market long-short prediction," LSE Research Online Documents on Economics 118875, London School of Economics and Political Science, LSE Library.
    10. Mo, Guoli & Zhang, Weiguo & Tan, Chunzhi & Liu, Xing, 2022. "Predicting the portfolio risk of high-dimensional international stock indices with dynamic spatial dependence," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).

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    More about this item

    Keywords

    Risk Management; Sovereign Risk; Systemic Risk; Liquidity; Credit Risk; Equity Risk Premium; Enterprise Risk Management;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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