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On the Predictability of Global Stock Returns

Author

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  • Hjalmarsson, Erik

    (Department of Economics)

Abstract
Stock return predictability is a central issue in empirical finance. Yet no comprehensive study of international data has been performed to test the predictive ability of lagged explanatory variables. In fact, most stylized facts are based on U.S. stock-market data. In this paper, I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend- and earnings-price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including markets in 22 of the 24 OECD countries. I also develop new asymptotic results for long-run regressions with overlapping observations. I show that rather than using auto-correlation robust standard errors, the standard t-statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data. Further, when the regressors are persistent and endogenous, the long-run OLS estimator suffers from the same problems as does the short-run OLS estimator, and similar corrections and test procedures as those proposed by Campbell and Yogo (2003) for the short-run case should also be used in the long-run; again, the resulting test statistics should be scaled due to the overlap. The empirical analysis conducts time-series regressions for individual countries as well as pooled regressions. The results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in OECD countries. The predictive abilities of both the short rate and the term spread are short-run phenomena; in particular, there is only evidence of predictability at one and 12-month horizons. In contrast to the interest rate variables, no strong or consistent evidence of predictability is found when considering the earnings- and dividend-price ratios as predictors. Any evidence that is found is primarily seen at the long-run horizon of 60 months. Neither of these predictors yields any consistent predictive power for the OECD countries. The interest rate variables also have out-of-sample predictive power that is economically significant; the welfare gains to a log-utility investor who uses the predictive ability of these variables to make portfolio decisions are substantial.

Suggested Citation

  • Hjalmarsson, Erik, 2005. "On the Predictability of Global Stock Returns," Working Papers in Economics 161, University of Gothenburg, Department of Economics.
    • Handle: RePEc:hhs:gunwpe:0161
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    File URL: http://hdl.handle.net/2077/2764
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    References listed on IDEAS

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    Cited by:

    1. Schrimpf, Andreas, 2010. "International stock return predictability under model uncertainty," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1256-1282, November.
    2. Hjalmarsson, Erik, 2005. "Predictive regressions with panel data," Working Papers in Economics 160, University of Gothenburg, Department of Economics.
    3. Oberndorfer, Ulrich, 2009. "Energy prices, volatility, and the stock market: Evidence from the Eurozone," Energy Policy, Elsevier, vol. 37(12), pages 5787-5795, December.
    4. GIOT, Pierre & PETITJEAN, Mikael, 2006. "International stock return predictability: statistical evidence and economic significance," LIDAM Discussion Papers CORE 2006088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Oberndorfer, Ulrich, 2008. "Returns and Volatility of Eurozone Energy Stocks," ZEW Discussion Papers 08-017, ZEW - Leibniz Centre for European Economic Research.
    6. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.

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    More about this item

    Keywords

    Predictive regressions; long-horizon regressions; panel data; stock return predictability;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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