To improve accuracy for estimating long-term expected returns, this study evaluates different estimation frameworks and input proxies within each framework. Analysis shows that using a three-component model can improve Sharpe ratios more than 50%.
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Abstract
Estimating long-term expected returns as accurately as possible is of critical importance. Researchers typically base their estimates on yield and growth, valuation, or a combined yield, growth, and valuation (“three-component”) framework. We run a horse race of the abilities of different frameworks and input proxies within each framework to estimate 10- and 20-year out-of-sample returns. The three-component model based on the TRCAPE valuation proxy outperforms estimates based on historical mean benchmark returns, with mean square error improvements exceeding 30%. Using this approach in asset allocation decisions results in an improvement in Sharpe ratios of more than 50%.