Can we forecast better in periods of low uncertainty? The role of technical indicators

María Ferrer Fernández, Ólan Henry, Sam Pybis and Michalis P. Stamatogiannis

Journal of Empirical Finance, 2023, vol. 71, issue C, 1-12

Abstract: We examine the importance of periods of high versus low financial uncertainty when forecasting stock market returns with technical predictors. Our results suggest that technical predictors perform better in periods of low financial uncertainty and should be avoided due to poor forecasting performance in periods of heightened uncertainty. In-sample, we report disentangled R2 statistics, and out-of-sample we show these results continue when forecasting the equity risk premium. We show similar results when forecasting the volatility of returns with technical predictors. We measure periods of heightened and low financial uncertainty in a regime switching framework. Overall, our results provide insight into the mechanism that suggests that, when uncertainty rises, investors’ opinions polarize leading to a breakdown of predictability based on technical indicators.

Keywords: Forecasting; Stock return predictability; Economic uncertainty; Switching regression (search for similar items in EconPapers)
JEL-codes: C53 C58 G11 G12 G17 G41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:71:y:2023:i:c:p:1-12

DOI: 10.1016/j.jempfin.2022.12.014

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