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We compare the mean\u2010variance utilities that result when a risk\u2010averse agent uses the predictions of the different methods in an investment decision. Our application of this framework to the S&amp;P 500 shows that rule\u2010based methods are preferable for (in\u2010sample) identification of the state of the market, but Markov switching models for (out\u2010of\u2010sample) forecasting. In\u2010sample, only the mean return of the market index matters, which rule\u2010based methods exactly capture. Because Markov switching models use both the mean and the variance to infer the state, they produce superior forecasts and lead to significantly better out\u2010of\u2010sample performance than rule\u2010based methods. We conclude that the variance is a crucial ingredient for forecasting the market state. 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