Forecasting stock market returns by summing the frequency-decomposed parts

Gonçalo Faria and Fabio Verona

No 5, Working Papers de Economia (Economics Working Papers) from Católica Porto Business School, Universidade Católica Portuguesa

Abstract: We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out- of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.

Keywords: predictability; stock returns; equity premium; asset allocation; frequency domain; wavelets (search for similar items in EconPapers)
JEL-codes: G11 G12 G14 G17 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2016-10
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Forecasting stock market returns by summing the frequency-decomposed parts (2018)
Working Paper: Forecasting stock market returns by summing the frequency-decomposed parts (2017)
Working Paper: Forecasting stock market returns by summing the frequency-decomposed parts (2016)
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