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Economic and Financial Crises and the Predictability of U.S. Stock Returns

Author

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  • Hartmann, Daniel
  • Kempa, Bernd
  • Pierdzioch, Christian

Abstract

We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our results suggest that accounting for structural breaks and regime shifts in forecasting regressions caused by economic and financial crises has the potential to increase the out-of-sample predictability of stock returns, the performance of simple trading rules, and the market-timing ability of an investor trading in the U.S. stock market.

Suggested Citation

  • Hartmann, Daniel & Kempa, Bernd & Pierdzioch, Christian, 2006. "Economic and Financial Crises and the Predictability of U.S. Stock Returns," MPRA Paper 561, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:561
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Christian Pierdzioch, 2012. "Macroeconomic Factors and the German Real Estate Market: A Stock-Market-Based Forecasting Experiment," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 87-96, May.
    2. Michael Berlemann & Julia Freese & Sven Knoth, 2012. "Eyes Wide Shut? The U.S. House Market Bubble through the Lense of Statistical Process Control," CESifo Working Paper Series 3962, CESifo.
    3. Christian Pierdzioch & Marian Risse & Sebastian Rohloff, 2016. "Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy," Empirical Economics, Springer, vol. 51(4), pages 1481-1499, December.
    4. Christian Pierdzioch & Daniel Hartmann, 2013. "Forecasting Eurozone real-estate returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(14), pages 1185-1196, July.
    5. Bee-Hoong Tay & Pei-Tha Gan, 2016. "The Determinants of Investment Rewards: Evidence for Selected Developed and Developing Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 6(3), pages 1180-1188.
    6. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    7. Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.
    8. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.
    9. Yu-Hau Hu & Shun-Jen Hsueh, 2013. "A Study of yhe Nonlinear Relationships among the U.S. and Asian Stock Markets during Financial Crises," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 134-147, December.
    10. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
    11. Döpke, Jörg & Müller, Karsten & Tegtmeier, Lars, 2018. "The economic value of business cycle forecasts for potential investors – Evidence from Germany," Research in International Business and Finance, Elsevier, vol. 46(C), pages 445-461.
    12. Jing Tian & Qing Zhou, 2018. "Improving equity premium forecasts by incorporating structural break uncertainty," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 619-656, November.

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    More about this item

    Keywords

    Forecasting stock returns; financial and economic crises; trading rules;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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