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Why is it so difficult to outperform the random walk? An application of the Meese-Rogoff puzzle to stock prices

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  • Imad Moosa
  • John Vaz

Abstract

Some economists suggest that the Meese-Rogoff puzzle is equally applicable to the stock market, in the sense that no model of stock prices can outperform the random walk in out-of-sample forecasting. We argue that this is not a puzzle and that we should expect nothing, but this result if forecasting accuracy is measured by the root mean square error (RMSE) and similar metrics that take into account the magnitude of the forecasting error only. We demonstrate by using two models for dividend-paying and nondividend-paying stocks that as price volatility rises, the RMSE of the random walk rises, but the RMSE of the model rises even more rapidly, making it unlikely for the model to outperform the random walk.

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  • Imad Moosa & John Vaz, 2015. "Why is it so difficult to outperform the random walk? An application of the Meese-Rogoff puzzle to stock prices," Applied Economics, Taylor & Francis Journals, vol. 47(4), pages 398-407, January.
    • Handle: RePEc:taf:applec:v:47:y:2015:i:4:p:398-407
    DOI: 10.1080/00036846.2014.972545
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    Cited by:

    1. Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
    2. Imad A. Moosa, 2015. "The random walk versus unbiased efficiency: can we separate the wheat from the chaff?," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 38(2), pages 251-279, October.
    3. Moosa, Imad A. & Vaz, John J., 2016. "Cointegration, error correction and exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 21-34.

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