Using available return data, many multi-factor asset pricing models present impressive in-sample Sharpe ratios, significantly surpassing that of the market portfolio. Such a performance, however, contradicts the conventional wisdom in finance. Investors cannot realistically attain the in-sample Sharpe ratios. They obtain the out-of-sample Sharpe ratios, which are significantly lower. Estimation risk is one reason for this performance deterioration. We theoretically study the effect of estimation risk by obtaining the exact distributions of in-sample and out-of-sample Sharpe ratios, and argue that such effect needs to be considered in model comparisons.

中文翻译：

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多因素资产定价模型的样本内和样本外夏普比率

使用可用的回报数据，许多多因素资产定价模型呈现出令人印象深刻的样本内夏普比率，显着超过市场投资组合。然而，这样的表现与金融界的传统观念相矛盾。投资者实际上无法达到样本内的夏普比率。他们获得了样本外夏普比率，该比率明显较低。估计风险是这种性能恶化的原因之一。我们通过获得样本内和样本外夏普比率的精确分布，从理论上研究了估计风险的影响，并认为在模型比较中需要考虑这种影响。