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Predicting Stock Market Returns by Combining Forecasts

  • Laurence Fung

    (Research Department, Hong Kong Monetary Authority)

  • Ip-wing Yu

    (Research Department, Hong Kong Monetary Authority)

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    The predictability of stock market returns has been a challenge to market practitioners and financial economists. This is also important to central banks responsible for monitoring financial market stability. A number of variables have been found as predictors of future stock market returns with impressive in-sample results. Nonetheless, the predictive power of these variables has often performed poorly for out-of-sample forecasts. This study utilises a new method known as "Aggregate Forecasting Through Exponential Re-weighting (AFTER)" to combine forecasts from different models and achieve better out-of-sample forecast performance from these variables. Empirical results suggest that, for longer forecast horizons, combining forecasts based on AFTER provides better out-of-sample predictions than the historical average return and also forecasts from models based on commonly used model selection criteria.

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    File URL: http://www.info.gov.hk/hkma/eng/research/working/pdf/HKMAWP08_01_full.pdf
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    Paper provided by Hong Kong Monetary Authority in its series Working Papers with number 0801.

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    Length: 30 pages
    Date of creation: Mar 2008
    Date of revision:
    Handle: RePEc:hkg:wpaper:0801
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    17. Matthias Hagmann & Joachim Loebb, 2006. "Model Combination and Stock Return Predictability," Swiss Finance Institute Research Paper Series 06-05, Swiss Finance Institute.
    18. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
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