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Heterogeneity in households’ stock market beliefs

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  • von Gaudecker, Hans-Martin
  • Wogrolly, Axel

Abstract

We analyse a long panel of households’ stock market beliefs to gain insights into the nature of the levels, dynamics, and informativeness of these expectations. In a first step, we classify respondents into one of five groups based on their beliefs data alone. In a second step, we estimate models of expectations at the group level so that belief levels, volatility, and response to information can vary freely across groups. At opposite extremes in terms of optimism we identify pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average. Two groups expect average returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case. The final group is characterised by its members being unable or unwilling to quantify their beliefs about future returns.

Suggested Citation

  • von Gaudecker, Hans-Martin & Wogrolly, Axel, 2022. "Heterogeneity in households’ stock market beliefs," Journal of Econometrics, Elsevier, vol. 231(1), pages 232-247.
  • Handle: RePEc:eee:econom:v:231:y:2022:i:1:p:232-247
    DOI: 10.1016/j.jeconom.2020.11.011
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    More about this item

    Keywords

    Stock market expectations; Household finance; Heterogeneity; Clustering methods;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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