Forecasting Stock Market Returns by Summing the Frequency-Decomposed Parts

38 Pages Posted: 2 Dec 2016  

Gonçalo Faria

Catholic University of Portugal (UCP) - School of Economics and Management and CEGE

Fabio Verona

Bank of Finland - Research

Date Written: 2016

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

JEL Classification: G11, G12, G14, G17

Suggested Citation

Faria, Gonçalo and Verona, Fabio, Forecasting Stock Market Returns by Summing the Frequency-Decomposed Parts (2016). Bank of Finland Research Discussion Paper No. 29/2016. Available at SSRN: https://ssrn.com/abstract=2878752

Gonçalo Faria (Contact Author)

Catholic University of Portugal (UCP) - School of Economics and Management and CEGE ( email )

Universidade Católica Portuguesa
Rua Diogo Botelho 1327
Porto, 4169-005
Portugal

Fabio Verona

Bank of Finland - Research ( email )

P.O. Box 160
FIN-00101 Helsinki
Finland

HOME PAGE: http://www.suomenpankki.fi/en/suomen_pankki/organisaatio/asiantuntijoita/Pages/verona_fabio.aspx

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