We use bootstrap simulations to examine the properties of long-horizon U.S. stock market returns. We document the rate at which continuously compounded market returns converge toward normal distributions as we extend the horizon from 1 month to 30 years, and the rate at which dollar payoffs converge toward lognormal. We also verify that, though largely irrelevant at short horizons, uncertainty about the expected market return has a substantial impact on uncertainty about long-horizon payoffs.

Our goal is to develop insights about the maximum squared Sharpe ratio for model factors as a metric for ranking asset pricing models. We consider nested and non-nested models. The nested models are the capital asset pricing model, the three-factor model of Fama and French (1993), the five-factor extension in Fama and French (2015), and a six-factor model that adds a momentum factor. The non-nested models examine three issues about factor choice in the six-factor model: (1) cash profitability versus operating profitability as the variable used to construct profitability factors, (2) long-short spread factors versus excess return factors, and (3) factors that use small or big stocks versus factors that use both.