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Elusive return predictability

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Author Info

  • Timmermann, Allan

Abstract

Investors' searches for successful forecasting models cause the data generating process for financial returns to change over time, which means that individual return forecasting models can, at best, hope to uncover evidence of 'local' predictability. We illustrate this point on a suite of forecasting models used to predict US stock returns, and propose an adaptive forecast combination approach. Most of the time the forecasting models perform rather poorly, but there is evidence of relatively short-lived periods with modest return predictability. The short duration of the episodes where return predictability appears to be present and the relatively weak degree of predictability even during such periods makes predicting returns an extraordinarily challenging task.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 24 (2008)
Issue (Month): 1 ()
Pages: 1-18

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Handle: RePEc:eee:intfor:v:24:y:2008:i:1:p:1-18

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Web page: http://www.elsevier.com/locate/ijforecast

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References

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Citations

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Cited by:
  1. Rambaccussing, Dooruj, 2009. "Exploiting price misalignements," MPRA Paper 27147, University Library of Munich, Germany.
  2. Schrimpf, Andreas & Wang, Qingwei, 2010. "A reappraisal of the leading indicator properties of the yield curve under structural instability," International Journal of Forecasting, Elsevier, vol. 26(4), pages 836-857, October.
  3. Schrimpf, Andreas, 2010. "International stock return predictability under model uncertainty," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1256-1282, November.
  4. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2010. "Does the macroeconomy predict U.K. asset returns in a nonlinear fashion? comprehensive out-of-sample evidence," Working Papers 2010-039, Federal Reserve Bank of St. Louis.
  5. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
  6. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2010. "Regime specific predictability in predictive regressions," Discussion Paper Series In Economics And Econometrics 0916, Economics Division, School of Social Sciences, University of Southampton.
  7. Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, School of Economics and Management, University of Aarhus.
  8. Tang, Chor Foon & Lai, Yew Wah, 2011. "The Stability of Export-led Growth Hypothesis: Evidence from Asia's Four Little Dragons," MPRA Paper 27962, University Library of Munich, Germany.
  9. Chor Foon Tang & Soo Y. Chua, 2012. "The savings-growth nexus for the Malaysian economy: a view through rolling sub-samples," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4173-4185, November.
  10. Gao, Yan & Li, Honggang, 2011. "A consolidated model of self-fulfilling expectations and self-destroying expectations in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 77(3), pages 368-381, March.
  11. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Society for Computational Economics, vol. 40(3), pages 245-264, October.
  12. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
  13. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
  14. Stanislav Anatolyev & Natalia Kryzhanovskaya, 2009. "Directional Prediction of Returns under Asymmetric Loss: Direct and Indirect Approaches," Working Papers w0136, Center for Economic and Financial Research (CEFIR).
  15. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
  16. Rechenthin, Michael & Street, W. Nick, 2013. "Using conditional probability to identify trends in intra-day high-frequency equity pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6169-6188.
  17. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.

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