Model Combination and Stock Return Predictability

Matthias Hagmann and Joachim Loebb
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Matthias Hagmann: University of Geneva and Concordia Advisors
Joachim Loebb: University of Zurich and Swiss Banking Institute

No 06-05, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Bayesian Model Averaging (BMA) has recently been discussed in the financial literature as an effective way to account for model uncertainty. In this paper we compare BMA to a new model uncertainty framework introduced by Yang (2004), called Aggregate Forecasting Through Exponential Reweighting, which has as well a Bayesian interpretation, but enjoys several attractive features not shared by BMA. The AFTER algorithm has nice theoretical properties if the true model does not belong to the class of considered models and can easily incorporate 'stylized facts' of financial data in the weighting scheme, such as time-varying volatility and fat tails. Most importantly, the determination of model weights in AFTER is based on pseudo out-of-sample performance and not on within sample criteria as it is the case for BMA. This seems rather attractive from an investment perspective.

Keywords: Predictability; model combination; Bayesian Model Averaging; investment strategies (search for similar items in EconPapers)
JEL-codes: C11 G11 G12 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2006-03
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Citations: View citations in EconPapers (1)

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