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

Author

Listed:
  • Joao F. Caldeira

    (Department of Economics, Universidade Federal de Santa Catarina & CNPq, Brazil)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Hudson S. Torrent

    (Department of Statistics, Universidade Federal do Rio Grande do Sul, Brazil)

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.

Suggested Citation

  • Joao F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Working Papers 202087, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202087
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    Cited by:

    1. Helida Nurcahayani & I Nyoman Budiantara & Ismaini Zain, 2021. "The Curve Estimation of Combined Truncated Spline and Fourier Series Estimators for Multiresponse Nonparametric Regression," Mathematics, MDPI, vol. 9(10), pages 1-22, May.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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