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

João F. Caldeira, Rangan Gupta and Hudson S. Torrent
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João F. Caldeira: Department of Economics, Universidade Federal de Santa Catarina & CNPq, Florianópolis 88040-970, Brazil
Hudson S. Torrent: Department of Statistics, Universidade Federal do Rio Grande do Sul, Porto Alegre 91509-900, Brazil

Mathematics, 2020, vol. 8, issue 11, 1-16

Abstract: This paper analyzes the forecast performance of historical S&P500 and Dow Jones Industrial Average (DJIA) excess returns while using nonparametric functional data analysis (NP-FDA). The empirical results show that the NP-FDA forecasting strategy outperforms not only the the prevailing-mean model, but also the traditional univariate predictive regressions with standard predictors used in the literature and, most cases, also combination approaches that use all predictors jointly. In addition, our results clearly have important implications for investors, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Indeed, our results show that NP-FDA is the only one individual model that can overcome the historical average forecasts for excess returns in statistically and economically significant manners for both S&P500 and DJIA during the entire period, NBER recession, and expansions periods.

Keywords: return forecast; nonparametric functional data analysis; performance evaluation; predictive regression; classical financial mathematics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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Working Paper: Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value? (2020)
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