Return Prediction Models and Portfolio Optimization: Evidence for Industry Portfolios

62 Pages Posted: 3 Dec 2014 Last revised: 4 May 2021

See all articles by Wolfgang Bessler

Wolfgang Bessler

University of Hamburg

Dominik Wolff

Deka Investment GmbH; Technical University of Darmstadt; Frankfurt University of Applied Sciences

Date Written: January 15, 2016


We postulate that utilizing return prediction models with fundamental, macroeconomic, and technical indicators instead of using historical averages should result in superior asset allocation decisions. We investigate the predictive power of individual variables for forecasting industry returns in-sample and out-of-sample and then analyze multivariate predictive regression models including OLS, a regularization technique, principal components, a target-relevant latent factor approach, and forecast combinations. The gains from using industry return predictions are evaluated in an out-of-sample Black-Litterman portfolio optimization framework. We provide empirical evidence that portfolio optimization utilizing industry return prediction models significantly outperform portfolios using historical averages and those being passively managed.

Keywords: Portfolio Optimization, Return forecasts, Predictive Regression, Three-Pass Regression Filter, Black-Litterman Model

JEL Classification: G17, G11, C53

Suggested Citation

Bessler, Wolfgang and Wolff, Dominik and Wolff, Dominik, Return Prediction Models and Portfolio Optimization: Evidence for Industry Portfolios (January 15, 2016). Available at SSRN: or

Wolfgang Bessler (Contact Author)

University of Hamburg ( email )

Allende-Platz 1
Hamburg, 20146

Dominik Wolff

Deka Investment GmbH ( email )

Mainzer Landstrasse 16
Frankfurt am Main, 60325

Technical University of Darmstadt

Hochschulstraße 1
S1|02 40
Darmstadt, Hessen D-64289

Frankfurt University of Applied Sciences ( email )

Nibelungenplatz 1
Frankfurt / Main, 60318

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