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Extrapolation and Bubbles

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  • Nicholas Barberis
  • Robin Greenwood
  • Lawrence Jin
  • Andrei Shleifer

Abstract

We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals?an average of the asset?s past price changes and the asset?s degree of overvaluation. The two signals are in conflict, and investors ?waver? over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model?s distinctive predictions.

Suggested Citation

  • Nicholas Barberis & Robin Greenwood & Lawrence Jin & Andrei Shleifer, 2015. "Extrapolation and Bubbles," Working Paper 357401, Harvard University OpenScholar.
  • Handle: RePEc:qsh:wpaper:357401
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Greenwood, Robin & Shleifer, Andrei & You, Yang, 2019. "Bubbles for Fama," Journal of Financial Economics, Elsevier, vol. 131(1), pages 20-43.
    2. Michael Bailey & Ruiqing Cao & Theresa Kuchler & Johannes Stroebel, 2016. "Social Networks and Housing Markets," NBER Working Papers 22258, National Bureau of Economic Research, Inc.
    3. Alexandre Kohlhas, 2018. "Asymmetric Attention," 2018 Meeting Papers 1040, Society for Economic Dynamics.
    4. Cars Hommes & Anita Kopányi-Peuker & Joep Sonnemans, "undated". "Bubbles, crashes and information contagion in large-group asset market experiments," Tinbergen Institute Discussion Papers 19-016/II, Tinbergen Institute.
    5. Saadi, Vahid, 2016. "Mortgage supply and the US housing boom: The role of the Community Reinvestment Act," SAFE Working Paper Series 155, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    6. Saadi, Vahid, 2016. "Mortgage supply and the US housing boom: The role of the community reinvestment act," IWH Discussion Papers 32/2016, Halle Institute for Economic Research (IWH).
    7. Gao, Can & Martin, Ian, 2019. "Volatility, Valuation Ratios, and Bubbles: An Empirical Measure of Market Sentiment," CEPR Discussion Papers 13454, C.E.P.R. Discussion Papers.
    8. repec:eee:finsta:v:36:y:2018:i:c:p:66-81 is not listed on IDEAS
    9. Baghestanian, Sascha & Massenot, Baptiste, 2016. "Credit cycles: Experimental evidence," SAFE Working Paper Series 104 [rev.], Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    10. Stefano Giglio & Bryan Kelly, 2016. "Excess Volatility: Beyond Discount Rates," NBER Working Papers 22045, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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