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

Doureige Jurdi and Jae Kim

MPRA Paper from University Library of Munich, Germany

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

Keywords: Bias-correction; Financial ratios; Forecasting; Return predictability; Utility gains (search for similar items in EconPapers)
JEL-codes: G17 (search for similar items in EconPapers)
Date: 2019-05-20
New Economics Papers: this item is included in nep-fmk, nep-for and nep-upt
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https://mpra.ub.uni-muenchen.de/95317/1/MPRA_paper_94028.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:94028

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