  We analyze characteristics' joint predictive information through the lens of
out-of-sample power utility functions. Linking weights to characteristics to
form optimal portfolios suffers from estimation error which we mitigate by
maximizing an in-sample loss function that is more concave than the utility
function. While no single characteristic can be used to enhance utility by all
investors, conditioning on momentum, size, and residual volatility produces
portfolios with significantly higher certainty equivalents than benchmarks for
all investors. Characteristic complementarities produce the benefits, for
example momentum mitigates overfitting inherent in other characteristics.
Optimal portfolios' returns lie largely outside the span of traditional
factors.
