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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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    1. Gabriel Perez‐Quiros & Allan Timmermann, 2000. "Firm Size and Cyclical Variations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(3), pages 1229-1262, June.
    2. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    3. Timmermann, Allan, 2001. "Structural Breaks, Incomplete Information, and Stock Prices," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 299-314, July.
    4. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    5. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    6. Jun Liu & Francis A. Longstaff & Jun Pan, 2003. "Dynamic Asset Allocation with Event Risk," Journal of Finance, American Finance Association, vol. 58(1), pages 231-259, February.
    7. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    8. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    9. Ben S. Bernanke & Mark Gertler, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 27-48, Fall.
    10. Ben S. Bernanke & Cara S. Lown, 1991. "The Credit Crunch," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 22(2), pages 205-248.
    11. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    12. Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
    13. John V. Duca, 1999. "What credit market indicators tell us," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q III, pages 2-13.
    14. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    15. repec:bla:jfinan:v:59:y:2004:i:6:p:2809-2834 is not listed on IDEAS
    16. Edwin J. Elton & Martin J. Gruber & Deepak Agrawal & Christopher Mann, 2001. "Explaining the Rate Spread on Corporate Bonds," Journal of Finance, American Finance Association, vol. 56(1), pages 247-277, February.
    17. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-191, January.
    18. Robert F. Whitelaw, 1997. "Time-Varying Sharpe Ratios and Market Timing," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-074, New York University, Leonard N. Stern School of Business-.
    19. William R. White, 2000. "What have we learned from recent financial crises and policy responses?," BIS Working Papers 84, Bank for International Settlements.
    20. Tobias Adrian & Michael J. Fleming, 2005. "What financing data reveal about dealer leverage," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 11(Mar).
    21. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    22. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," The Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-428.
    23. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    24. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    25. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    26. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    27. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    28. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
    29. Kole, Erik & Koedijk, Kees & Verbeek, Marno, 2006. "Portfolio implications of systemic crises," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2347-2369, August.
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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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