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How Stable are Financial Prediction Models? Evidence from US and International Stock Market Data


  • Paye, Bradley S.
  • Timmermann, Allan


This study examines evidence of structural breaks in models of predictable components in stock returns related to state variables such as the lagged dividend yield, Treasury bill rate, term spread and default premium. We examine a large set of size-and-industry-sorted profolios of US stocks as well as 18 international stock market profolios and find systematic evidence of breaks in the vast majority of porfolios. The breakpoints most frequently identified in the US data are 1966, 1974, 1983, and 1990. The 1966 and 1974 breaks appear to have been driven by the T-bill rate and the default premium coefficients, while the 1983 break reflects changes in the coefficient on the T-bill rate and the term spread and the 1990 break was driven by the dividend yield and default premium coeffciencts. Our evidence also suggests that, while the size of the predictable component in stock returns has come down after the most recent break, many predictors continue to be significant. Although in-sample predictability of returns was lower in the 1990s than in some previous decades, it does not seem to have

Suggested Citation

  • Paye, Bradley S. & Timmermann, Allan, 2002. "How Stable are Financial Prediction Models? Evidence from US and International Stock Market Data," University of California at San Diego, Economics Working Paper Series qt74v515fr, Department of Economics, UC San Diego.
  • Handle: RePEc:cdl:ucsdec:qt74v515fr

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    Cited by:

    1. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    2. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    3. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    5. Jan R. Magnus & Dmitry Danilov, 2004. "Forecast accuracy after pretesting with an application to the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 251-274.


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