[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ebg/heccah/1444.html
   My bibliography  Save this paper

Overreaction in Expectations: Evidence and Theory

Author

Listed:
  • Afrouzi, Hassan

    (Columbia University)

  • Kwon, Spencer Yongwook

    (Harvard University)

  • Landier, Augustin

    (HEC Paris)

  • Ma, Yueran

    (University of Chicago - Booth School of Business)

  • Thesmar, David

    (Massachusetts Institute of Technology (MIT))

Abstract
We investigate biases in expectations across different settings through a large-scale randomized experiment where participants forecast stable stochastic processes. The experiment allows us to control forecasters’ information sets as well as the data generating process, so we can cleanly measure biases in beliefs. We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for these variations in the degree of overreaction across different settings. To explain the observed patterns of overreaction, we develop a tractable model of expectations formation with costly information processing. Our model closely fits the empirical findings and generates additional predictions that we confirm in the data.

Suggested Citation

  • Afrouzi, Hassan & Kwon, Spencer Yongwook & Landier, Augustin & Ma, Yueran & Thesmar, David, 2021. "Overreaction in Expectations: Evidence and Theory," HEC Research Papers Series 1444, HEC Paris.
    • Handle: RePEc:ebg:heccah:1444
    DOI: 10.2139/ssrn.3709548
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3709548
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.2139/ssrn.3709548?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Marianne Andries & Milo Bianchi & Karen Huynh & Sébastien Pouget, 2024. "Return Predictability, Expectations, and Investment: Experimental Evidence," Post-Print hal-04680777, HAL.
    2. Conrad, Christian & Lahiri, Kajal, 2023. "Heterogeneous expectations among professional forecasters," ZEW Discussion Papers 23-062, ZEW - Leibniz Centre for European Economic Research.
    3. Jonas Radbruch & Amelie Schiprowski, 2020. "Interview Sequences and the Formation of Subjective Assessments," ECONtribute Discussion Papers Series 045, University of Bonn and University of Cologne, Germany.
    4. Charles, Constantin & Frydman, Cary & Kilic, Mete, 2024. "Insensitive investors," LSE Research Online Documents on Economics 120788, London School of Economics and Political Science, LSE Library.
    5. Yves Breitmoser & Justin Valasek & Justin Mattias Valasek, 2023. "Why Do Committees Work?," CESifo Working Paper Series 10800, CESifo.
    6. Sebastian Link & Andreas Peichl & Christopher Roth & Johannes Wohlfart, 2023. "Attention to the Macroeconomy," ECONtribute Discussion Papers Series 256, University of Bonn and University of Cologne, Germany.
    7. Matteo Bizzarri & Daniele d'Arienzo, 2023. "The social value of overreaction to information," CSEF Working Papers 690, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    8. Breitmoser, Yves & Valasek, Justin, 2023. "Why do committees work?," Discussion Paper Series in Economics 18/2023, Norwegian School of Economics, Department of Economics.
    9. Enke, Benjamin & Schwerter, Frederik & Zimmermann, Florian, 2024. "Associative memory, beliefs and market interactions," Journal of Financial Economics, Elsevier, vol. 157(C).
    10. Angelico, Cristina, 2024. "The green transition and firms' expectations on future prices: Survey evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 519-543.
    11. Leland Bybee, 2023. "Surveying Generative AI's Economic Expectations," Papers 2305.02823, arXiv.org, revised May 2023.

    More about this item

    Keywords

    expectations; experiment; overreaction;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

    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:ebg:heccah:1444. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Antoine Haldemann (email available below). General contact details of provider: https://edirc.repec.org/data/hecpafr.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.