Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?

Joao Caldeira (), Rangan Gupta and Hudson Torrent ()
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Joao Caldeira: Department of Economics, Universidade Federal de Santa Catarina & CNPq, Brazil
Hudson Torrent: Department of Statistics, Universidade Federal do Rio Grande do Sul, Brazil

No 202087, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper analyzes the forecast performance of historical S&P500 and Dow Jones Industrial Average (DJIA) excess returns using nonparametric functional data analysis (NP-FDA). Our results indicate that the NP-FDA specifications generally outperform the prevailing-mean model, not only statistically, but also from the perspective of economic gains. In addition, the same hold when adding NP-FDA forecasts to the traditional univariate predictive regressions with standard predictors used in the literature. Our results, clearly have important implications for investors.

Keywords: return forecast; nonparametric functional data analysis; performance evaluation; predictive regression; classical financial mathematics (search for similar items in EconPapers)
JEL-codes: C14 G11 G17 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2020-09
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Citations: View citations in EconPapers (1)

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Journal Article: Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value? (2020)
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