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Quantifying survey expectations: What's wrong with the probability approach?

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  • Breitung, Jörg
  • Schmeling, Maik

Abstract

We study a matched sample of individual stock market forecasts consisting of both qualitative and quantitative forecasts. This allows us to test for the quality of forecast quantification methods by comparing quantified qualitative forecasts with actual quantitative forecasts. Focusing mainly on the widely used quantification framework advocated by Carlson and Parkin (1975), the so-called "probability approach", we find that quantified expectations derived from the probability approach display a surprisingly weak correlation with reported quantitative stock return forecasts. We trace the reason for this low correlation to the importance of asymmetric and time-varying thresholds, whereas individual heterogeneity across forecasters seems to play a minor role. Hence, our results suggest that qualitative survey data may not be a very useful device to obtain quantitative forecasts and we suggest ways to remedy this problem when designing qualitative surveys.

Suggested Citation

  • Breitung, Jörg & Schmeling, Maik, 2011. "Quantifying survey expectations: What's wrong with the probability approach?," Hannover Economic Papers (HEP) dp-485, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-485
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    Citations

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    Cited by:

    1. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    3. repec:spr:soinre:v:135:y:2018:i:1:d:10.1007_s11205-016-1490-3 is not listed on IDEAS
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    6. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
    7. repec:spr:qualqt:v:51:y:2017:i:6:d:10.1007_s11135-016-0416-0 is not listed on IDEAS
    8. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    9. Kajal Lahiri & Yongchen Zhao, 2013. "Quantifying Heterogeneous Survey Expectations: The Carlson-Parkin Method Revisited," Discussion Papers 13-08, University at Albany, SUNY, Department of Economics.

    More about this item

    Keywords

    Quantification; Stock Market Expectations; Probability Approach; Heterogeneity;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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