Portfolio Optimization with Sector Return Prediction Models

Wolfgang Bessler () and Dominik Wolff
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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

JRFM, 2024, vol. 17, issue 6, 1-34

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.

Keywords: portfolio optimization; Black–Litterman model; return forecasts; predictive regression; three-pass regression filter (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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