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Expectation formation in finance and macroeconomics: A review of new experimental evidence

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  • Bao, Te
  • Hommes, Cars
  • Pei, Jiaoying

Abstract

This paper reviews the recent development and new findings of the literature on learning-to-forecast experiments (LtFEs). In general, the stylized finding in the typical LtFEs, namely the rapid convergence to the rational expectations equilibrium in negative feedback markets and persistent bubbles and crashes in positive feedback markets, is a robust result against several deviations from the baseline design (e.g., number of subjects in each market, price prediction versus quantity decision, short term versus long term predictions, predicting price or returns). Recent studies also find a high level of consistency between findings from forecasting data from the laboratory and the field, and forecasting accuracy crucially depends on the complexity of the task.

Suggested Citation

  • Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
  • Handle: RePEc:eee:beexfi:v:32:y:2021:i:c:s2214635021001350
    DOI: 10.1016/j.jbef.2021.100591
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    2. Bao, Te & Corgnet, Brice & Hanaki, Nobuyuki & Riyanto, Yohanes E. & Zhu, Jiahua, 2023. "Predicting the unpredictable: New experimental evidence on forecasting random walks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Filippo Gusella & Giorgio Ricchiuti, 2022. "A State-Space Approach for Time-Series Prediction of an Heterogeneous Agent Model," Working Papers - Economics wp2022_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    4. Jiaoying Pei, 2024. "Reference Model Based Learning in Expectation Formation: Experimental Evidence," Papers 2404.08908, arXiv.org.
    5. repec:dpr:wpaper:1213 is not listed on IDEAS
    6. Mignot, Sarah & Tramontana, Fabio & Westerhoff, Frank H., 2023. "Complex dynamics in a nonlinear duopoly model with heuristic expectation formation and learning behavior," BERG Working Paper Series 187, Bamberg University, Bamberg Economic Research Group.
    7. de Jong, Johan & Sonnemans, Joep & Tuinstra, Jan, 2022. "The effect of futures markets on the stability of commodity prices," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 176-211.
    8. Alfarano, Simone & Camacho-Cuena, Eva & Colasante, Annarita & Ruiz-Buforn, Alba, 2022. "The effect of time-varying fundamentals in Learning-to-Forecast Experiments," MPRA Paper 113086, University Library of Munich, Germany.

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    16. Colasante, Annarita & Palestrini, Antonio & Russo, Alberto & Gallegati, Mauro, 2017. "Adaptive expectations versus rational expectations: Evidence from the lab," International Journal of Forecasting, Elsevier, vol. 33(4), pages 988-1006.
    17. Makarewicz, Tomasz, 2019. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," BERG Working Paper Series 141, Bamberg University, Bamberg Economic Research Group.
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    More about this item

    Keywords

    Learning to forecast experiment; Experimental finance; Rational expectations; Bubbles and crashes; Behavioral finance;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G40 - Financial Economics - - Behavioral Finance - - - General

    Statistics

    Access and download statistics

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