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Title: In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models Authors:  Xiaolu Wang - Iowa State University (United States) [presenting]
Raymond Kan - University of Toronto (Canada)
Xinghua Zheng - HKUST (China)
Abstract: For many multi-factor asset pricing models proposed in the literature, their implied tangency portfolios have substantially higher sample Sharpe ratios than that of the value-weighted market portfolio. In contrast, such a high Sharpe ratio is rarely delivered by professional fund managers. One reason that real-world investor cannot attain the high sample Sharpe ratios of the multi-factor models is estimation risk. We study the effect of estimation risk on the out-of-sample Sharpe ratio of a multi-factor asset pricing model by obtaining the finite sample distribution of the out-of-sample Sharpe ratio conditional on the observed in-sample Sharpe ratio. For an investor who does not know the mean and covariance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio. After taking into account estimation risk, many of the multi-factor asset pricing models no longer outperform the value-weighted market portfolio.