IDEAS home Printed from https://ideas.repec.org/p/man/cgbcrp/214.html
   My bibliography  Save this paper

Prices, fundamental values and learning

Author

Listed:
  • Michele Berardi

Abstract

In this paper we show how uncertainty and learning about fundamental values can lead to excess volatility in prices and to volatility clustering in returns, as observed on real markets. The key assumption is that agents use prices, besides an exogenous signal on long run dividends, to infer fundamental values: as the relative weight on the two signals changes endogenously through learning, price dynamics are a¤ected. In particular, periods of high volatility are periods where agents rely more heavily on prices in predicting fundamentals.

Suggested Citation

  • Michele Berardi, 2015. "Prices, fundamental values and learning," Centre for Growth and Business Cycle Research Discussion Paper Series 214, Economics, The University of Manchester.
  • Handle: RePEc:man:cgbcrp:214
    as

    Download full text from publisher

    File URL: http://hummedia.manchester.ac.uk/schools/soss/cgbcr/discussionpapers/dpcgbcr214.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. William A. Branch & George W. Evans, 2010. "Asset Return Dynamics and Learning," Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1651-1680, April.
    3. Klaus Adam & Albert Marcet & Juan Pablo Nicolini, 2016. "Stock Market Volatility and Learning," Journal of Finance, American Finance Association, vol. 71(1), pages 33-82, February.
    4. Stephen Morris & Hyun Song Shin, 2006. "Endogenous Public Signals and Coordination," Levine's Bibliography 122247000000001309, UCLA Department of Economics.
    5. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-2872, October.
    6. Iván Werning & George-Marios Angeletos, 2006. "Crises and Prices: Information Aggregation, Multiplicity, and Volatility," American Economic Review, American Economic Association, vol. 96(5), pages 1720-1736, December.
    7. William A. Branch & George W. Evans, 2011. "Learning about Risk and Return: A Simple Model of Bubbles and Crashes," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(3), pages 159-191, July.
    8. Hens, Thorsten & Schenk-Hoppe, Klaus Reiner (ed.), 2009. "Handbook of Financial Markets: Dynamics and Evolution," Elsevier Monographs, Elsevier, edition 1, number 9780123742582.
    9. Emre Ozdenoren & Kathy Yuan, 2008. "Feedback Effects and Asset Prices," Journal of Finance, American Finance Association, vol. 63(4), pages 1939-1975, August.
    10. Berardi, Michele, 2015. "Learning and coordination with dispersed information," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 19-33.
    11. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    12. Andreas Park & Hamid Sabourian, 2011. "Herding and Contrarian Behavior in Financial Markets," Econometrica, Econometric Society, vol. 79(4), pages 973-1026, July.
    13. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    14. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    15. Hommes, C.H. & Zhu, M., 2011. "Learning under misspecification: a behavioral explanation of excess volatility in stock prices and persistence in inflation," CeNDEF Working Papers 11-04, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Donato Masciandaro, 2014. "Macroeconomic Ideas, Business Cycles and Economic Policies: One Size Doesn’t Fit All - A Primer," BAFFI CAREFIN Working Papers 14161, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. Donato Masciandaro, 2018. "Central Banking and Macroeconomic Ideas: Economics, Politics and History," BAFFI CAREFIN Working Papers 1858, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michele Berardi, 2016. "Herding through learning in an asset pricing model," Centre for Growth and Business Cycle Research Discussion Paper Series 223, Economics, The University of Manchester.
    2. Michele Berardi, 2018. "Information aggregation and learning in a dynamic asset pricing model," Centre for Growth and Business Cycle Research Discussion Paper Series 241, Economics, The University of Manchester.
    3. Michele Berardi, 2016. "Endogenous time-varying risk aversion and asset returns," Journal of Evolutionary Economics, Springer, vol. 26(3), pages 581-601, July.
    4. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    5. Kuang, Pei, 2014. "A model of housing and credit cycles with imperfect market knowledge," European Economic Review, Elsevier, vol. 70(C), pages 419-437.
    6. Nakov, Anton & Nuño, Galo, 2015. "Learning from experience in the stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 224-239.
    7. Klaus Adam & Albert Marcet & Juan Pablo Nicolini, 2016. "Stock Market Volatility and Learning," Journal of Finance, American Finance Association, vol. 71(1), pages 33-82, February.
    8. Pei Kuang, 2013. "Imperfect Knowledge About Asset Prices and Credit Cycles," Discussion Papers 13-02r, Department of Economics, University of Birmingham.
    9. Ali, Syed Zahid & Anwar, Sajid, 2017. "Exchange rate pass through, cost channel to monetary policy transmission, adaptive learning, and the price puzzle," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 69-82.
    10. George W. Evans, 2011. "Comment on "Natural Expectations, Macroeconomic Dynamics, and Asset Pricing"," NBER Chapters, in: NBER Macroeconomics Annual 2011, Volume 26, pages 61-71, National Bureau of Economic Research, Inc.
    11. Hommes, Cars & in ’t Veld, Daan, 2017. "Booms, busts and behavioural heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 101-124.
    12. Matthijs Lof, 2015. "Rational Speculators, Contrarians, and Excess Volatility," Management Science, INFORMS, vol. 61(8), pages 1889-1901, August.
    13. Gaus, Eric & Sinha, Arunima, 2017. "Characterizing investor expectations for assets with varying risk," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 990-999.
    14. Lof, Matthijs, 2013. "Essays on Expectations and the Econometrics of Asset Pricing," MPRA Paper 59064, University Library of Munich, Germany.
    15. Milani, Fabio, 2017. "Learning about the interdependence between the macroeconomy and the stock market," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 223-242.
    16. Evans, George W. & Hommes, Cars & McGough, Bruce & Salle, Isabelle, 2022. "Are long-horizon expectations (de-)stabilizing? Theory and experiments," Journal of Monetary Economics, Elsevier, vol. 132(C), pages 44-63.
    17. Hommes, Cars & Zhu, Mei, 2014. "Behavioral learning equilibria," Journal of Economic Theory, Elsevier, vol. 150(C), pages 778-814.
    18. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    19. Katsuhiro Oshima, 2019. "Heterogeneous Beliefs, Monetary Policy, and Stock Price Volatility," KIER Working Papers 1013, Kyoto University, Institute of Economic Research.
    20. Cole, Stephen J. & Milani, Fabio, 2021. "Heterogeneity in individual expectations, sentiment, and constant-gain learning," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 627-650.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:man:cgbcrp:214. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marianne Sensier (email available below). General contact details of provider: https://edirc.repec.org/data/semanuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.