Return Prediction Models and Portfolio Optimization: Evidence for Industry Portfolios


Empirische Kapitalmarktforschung, Risk & Optimization

Universität Gießen


  • Black-Litterman model
  • Portfolio optimization
  • Predictive regression
  • Return forecasts
  • Three-Pass Regression filter


Prof. Dr. Dominik Wolff Bessler, Dr. W.


An essential motive for investing in commodities is to enhance the performance of portfolios traditionally including only stocks and bonds. We analyze the in-sample and out-of-sample portfolio effects resulting from adding commodities to a stock-bond portfolio for commonly implemented asset-allocation strategies such as equally and strategically weighted portfolios, risk-parity, minimum-variance as well as reward-to-risk timing, mean-variance and Black-Litterman. We analyze different commodity groups such as agricultural and livestock com-modities that currently are critically discussed. The out-of-sample portfolio analysis indicates that the attainable benefits of commodities are much smaller than suggested by previous in-sample studies. Hence, in-sample analyses, such as spanning tests, might exaggerate the ad-vantages of commodities. Moreover, the portfolio gains greatly vary between different types of commodities and sub-periods. While aggregate commodity indices, industrial and precious metals as well as energy improve the performance of a stock-bond portfolio for most asset-allocation strategies, we hardly find positive portfolio effects for agriculture and livestock. Consequently, investments in food commodities are not essential for efficient asset allocation.

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