Download this Paper Open PDF in Browser

Tactical Industry Allocation and Model Uncertainty

36 Pages Posted: 2 Oct 2007  

Manuel Ammann

University of St. Gallen - School of Finance

Michael Verhofen

University of St. Gallen - Swiss Institute of Banking and Finance; Allianz Global Investors

Multiple version iconThere are 2 versions of this paper

Abstract

We use Bayesian model averaging to analyze the sample evidence on industry return predictability within the U.S. stock market in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out-of-sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out-of-sample forecasting power of a naive iid forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.

Keywords: Bayesian Model Averaging, Tactical Asset Allocation

JEL Classification: G11, G12, G14

Suggested Citation

Ammann, Manuel and Verhofen, Michael, Tactical Industry Allocation and Model Uncertainty. Available at SSRN: https://ssrn.com/abstract=1018104 or http://dx.doi.org/10.2139/ssrn.1018104

Manuel Ammann

University of St. Gallen - School of Finance ( email )

Unterer Graben 21
St.Gallen, CH-9000
Switzerland

Michael Verhofen (Contact Author)

Allianz Global Investors ( email )

Frankfurt
Germany

University of St. Gallen - Swiss Institute of Banking and Finance ( email )

Rosenbergstrasse 52
St. Gallen, CH-9000
Switzerland

HOME PAGE: http://www.verhofen.com

Paper statistics

Downloads
244
Rank
100,399
Abstract Views
1,185