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Stock-Market Expectations: Econometric Evidence that both REH and Behavioral Insights Matter

Author

Listed:
  • Roman Frydman

    () (New York University)

  • Joshua R. Stillwagon

    () (Trinity College)

Abstract

Behavioral finance views stock-market investors’ expectations as largely unrelated to fundamental factors. Relying on survey data, this paper presents econometric evidence that fundamentals are a major driver of investors’ expectations. Although expectations are also in part extrapolative, this effect is transient. The paper’s approach underscores the central importance of opening models to structural change and imposing discipline on econometric analysis through specification testing. Our findings support the novel hypothesis that rational market participants, faced with unforeseeable change, base their forecasts on both fundamentals - the focus of the REH approach - and the psychological and technical considerations underlying behavioral finance.

Suggested Citation

  • Roman Frydman & Joshua R. Stillwagon, 2016. "Stock-Market Expectations: Econometric Evidence that both REH and Behavioral Insights Matter," Working Papers Series 44, Institute for New Economic Thinking.
  • Handle: RePEc:thk:wpaper:44
    as

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    File URL: http://ineteconomics.org/uploads/papers/WP_44-Frydman-and-Stillwagon.pdf
    File Function: First version, 2016
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    References listed on IDEAS

    as
    1. Stillwagon, Josh R., 2016. "Non-linear exchange rate relationships: An automated model selection approach with indicator saturation," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 84-109.
    2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-25, April.
    3. repec:fip:fedfsp:y:2013:i:sept9 is not listed on IDEAS
    4. John C Williams, 2013. "Bubbles Tomorrow and Bubbles Yesterday, but Never Bubbles Today?," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 48(4), pages 224-230, October.
    5. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    6. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    7. John C. Williams, 2013. "Bubbles tomorrow and bubbles yesterday, but never bubbles today?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue sept23.
    8. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    9. Frydman, Roman & Goldberg, Michael D. & Mangee, Nicholas, 2015. "Knightian uncertainty and stock-price movements: Why the REH present-value model failed empirically," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 9, pages 1-50.
    10. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Behavioral finance; REH; Knightian uncertainty; survey expectations; structural change; model specification; automated model selection.;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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