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The ZEW Financial Market Survey Panel

Author

Listed:
  • Brückbauer Frank

    (ZEW Mannheim, Mannheim, Germany)

  • Schröder Michael

    (ZEW Mannheim, Mannheim, Germany)

Abstract

The ZEW Financial Market Survey is a monthly panel survey among financial market experts that was launched in December 1991. The survey focuses on the experts’ expectations about international financial markets and macroeconomic developments. We describe the ZEW Financial Market Survey and the resulting research dataset, which is available for free for academic researchers, is large and includes long individual time series (99,001 responses by 2002 respondents, as of September 2021), and contains rich information on the financial market experts collected over the years and which can be combined with the data on expectations.

Suggested Citation

  • Brückbauer Frank & Schröder Michael, 2023. "The ZEW Financial Market Survey Panel," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 451-469, June.
    • Handle: RePEc:jns:jbstat:v:243:y:2023:i:3-4:p:451-469:n:8
    DOI: 10.1515/jbnst-2022-0050
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    References listed on IDEAS

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    1. Lux, Thomas, 2009. "Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey," Journal of Economic Behavior & Organization, Elsevier, vol. 72(2), pages 638-655, November.
    2. Schmeling, Maik & Schrimpf, Andreas, 2011. "Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?," European Economic Review, Elsevier, vol. 55(5), pages 702-719, June.
    3. Thomas Lux, 2009. "Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey," Post-Print hal-00720175, HAL.
    4. Oliver Gloede & Lukas Menkhoff, 2014. "Financial Professionals' Overconfidence: Is It Experience, Function, or Attitude?," European Financial Management, European Financial Management Association, vol. 20(2), pages 236-269, March.
    5. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2015. "Exchange rate forecasts and expected fundamentals," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 235-256.
    6. Horst Entorf & Anne Gross & Christian Steiner, 2012. "Business Cycle Forecasts and their Implications for High Frequency Stock Market Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(1), pages 1-14, January.
    7. Richard Deaves & Jin Lei & Michael Schröder, 2019. "Forecaster Overconfidence and Market Survey Performance," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(2), pages 173-194, April.
    8. Rolf Scheufele, 2011. "Are Qualitative Inflation Expectations Useful to Predict Inflation?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 29-53.
    9. Szczesny, Andrea & Dornau, Robert & Anders, Ulrich, 1997. "G-Mind - German market indicator: Analyse des Stimmungsindikators und seiner Subkomponenten," ZEW Dokumentationen 97-04, ZEW - Leibniz Centre for European Economic Research.
    10. Dick, Christian D. & Menkhoff, Lukas, 2013. "Exchange rate expectations of chartists and fundamentalists," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1362-1383.
    11. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    12. Mokinski, Frieder, 2016. "Using time-stamped survey responses to measure expectations at a daily frequency," International Journal of Forecasting, Elsevier, vol. 32(2), pages 271-282.
    13. Michael Schroder & Robert Dornau, 2002. "Do forecasters use monetary models? an empirical analysis of exchange rate expectations," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 535-543.
    14. Menkhoff, Lukas & Rebitzky, Rafael R., 2008. "Investor sentiment in the US-dollar: Longer-term, non-linear orientation on PPP," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 455-467, June.
    15. Deaves, Richard & Lüders, Erik & Schröder, Michael, 2010. "The dynamics of overconfidence: Evidence from stock market forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 402-412, September.
    16. Menkhoff, Lukas & Rebitzky, Rafael R. & Schröder, Michael, 2009. "Heterogeneity in exchange rate expectations: Evidence on the chartist-fundamentalist approach," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 241-252, May.
    17. Lukas Menkhoff & Rafael Rebitzky & Michael Schroder, 2008. "Do dollar forecasters believe too much in PPP?," Applied Economics, Taylor & Francis Journals, vol. 40(3), pages 261-270.
    18. Björn Fastrich & Peter Winker, 2014. "Combining Forecasts with Missing Data: Making Use of Portfolio Theory," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 127-152, August.
    19. Johannes Leitner & Robert Schmidt, 2006. "A systematic comparison of professional exchange rate forecasts with the judgemental forecasts of novices," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(1), pages 87-102, February.
    20. Markus Glaser & Zwetelina Iliewa & Martin Weber, 2019. "Thinking about Prices versus Thinking about Returns in Financial Markets," Journal of Finance, American Finance Association, vol. 74(6), pages 2997-3039, December.
    21. Rangvid, Jesper & Schmeling, Maik & Schrimpf, Andreas, 2009. "Higher-order beliefs among professional stock market forecasters: some first empirical tests," ZEW Discussion Papers 09-042, ZEW - Leibniz Centre for European Economic Research.
    22. Dieter Hess & Alexandra Niessen, 2010. "The early news catches the attention: On the relative price impact of similar economic indicators," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(10), pages 909-937, October.
    23. Schröder, Michael & Hüfner, Felix P., 2002. "Forecasting economic activity in Germany: how useful are sentiment indicators?," ZEW Discussion Papers 02-56, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Joachim Wagner, 2024. "Data Observer—a guide to data that can help to inform evidence-based policymaking," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 279-287, June.

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    More about this item

    Keywords

    ZEW Financial Market Survey; financial market experts; financial market expectations;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G4 - Financial Economics - - Behavioral Finance

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