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Stock return predictability and model uncertainty
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DOI：10.1016/S0304-405X(02)00131-9.png)
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摘要
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We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictor.;. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. Vie show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses. (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
stock return predictability
model uncertainty
Bayesian model averaging
portfolio selection
variance decomposition
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