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Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method

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Listed:
  • Jurdi, Doureige
  • Kim, Jae

Abstract

We examine whether the stock market return is predictable from a range of financial indicators and macroeconomic variables, using monthly U.S. data from 1926 to 2012. We adopt the improved augmented regression method for parameter estimation, statistical inference, and out-of-sample forecasting. By employing moving sub-sample windows, we evaluate the time-variation of predictability free from data snooping bias and report changes in predictability dynamics over time. Although we may find statistically significant in-sample predictability from time to time, the associated effect size estimates are fairly small in most cases. We also find weak predictability of the stock market return from multistep ahead (out-of-sample) forecasts. In addition, we find that mean-variance investors realize sporadic economic gains in utility based on predictive regression forecasts relative to naive model historic average forecasts

Suggested Citation

  • Jurdi, Doureige & Kim, Jae, 2019. "Predicting the U.S. Stock Market Return: Evidence from the Improved Augmented Regression Method," MPRA Paper 94028, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:94028
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    File URL: https://mpra.ub.uni-muenchen.de/95317/1/MPRA_paper_94028.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Bias-correction; Financial ratios; Forecasting; Return predictability; Utility gains;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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    This paper has been announced in the following NEP Reports:

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