Stock Return Predictability and Model Uncertainty
Stock Return Predictability and Model Uncertainty
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DOI：
10.2139/ssrn.260591
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发表时间：
2001-04
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影响因子：
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通讯作者：
D. Avramov
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文献类型：
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作者：
D. Avramov
在存在模型不确定性的情况下，采用贝叶斯模型平均法分析样本证据对收益可预测性的影响。分析揭示了样本内和样本外的可预测性，并表明贝叶斯方法的样本外性能优于模型选择准则。我们发现期限和市场溢价是稳健的预测指标。此外，小盘价值型股票似乎比大盘型成长型股票更具可预测性。我们还从投资管理的角度探讨了模型不确定性的影响。模型不确定性比估计风险更重要，抛弃模型不确定性的投资者将面临巨大的效用损失。
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 predictors. 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. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.