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Portfolio Optimization with Sector Return Prediction Models

Author

Listed:
  • Wolfgang Bessler

    (Faculty of Business Administration, University of Hamburg, 20148 Hamburg, Germany)

  • Dominik Wolff

    (Department of Business and Law, Frankfurt University of Applied Sciences, 60318 Frankfurt am Main, Germany
    Department of Business Administration, Economics and Law, Technical University Darmstadt, 64289 Darmstadt, Germany
    Deka Investment GmbH, 60439 Frankfurt, Germany)

Abstract

We analyze return predictability for U.S. sectors based on fundamental, macroeconomic, and technical indicators and analyze whether return predictions improve tactical asset allocation decisions. We study the out-of-sample predictive power of individual variables for forecasting sector returns and analyze multivariate predictive regression models, including OLS, regularized regressions, principal component regressions, the three-pass regression filter, and forecast combinations. Using an out-of-sample Black–Litterman portfolio optimization framework and employing predicted returns as investors’ ‘views’, we evaluate the benefits of sector return forecasts for investors. We find that portfolio optimization with sector return prediction models significantly outperforms portfolios using historical averages as well as passive benchmark portfolios.

Suggested Citation

  • Wolfgang Bessler & Dominik Wolff, 2024. "Portfolio Optimization with Sector Return Prediction Models," JRFM, MDPI, vol. 17(6), pages 1-34, June.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:6:p:254-:d:1418899
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    References listed on IDEAS

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