Optimal portfolio choice is the central problem of equity portfolio management, asset allocation, and financial planning. Common optimality criteria such as the long-term geometric mean, utility function estimation, and return probability objectives have important theoretical or practical limitations. A portfolio choice framework consisting of resampled efficient portfolios and geometric mean analysis is a practical alternative for many situations of investment interest. Mean–variance optimization, the typical framework for defining an efficient portfolio set in practice, is estimation error sensitive and exhibits poor out-of-sample performance characteristics. Resampled efficiency, a generalization of mean–variance efficiency, improves out-of-sample performance on average and has important additional practical benefits. Geometric mean analysis gives the distribution of the multiperiod financial consequences of single-period efficient investments to clearly visualize the tradeoffs between risk and return and for assessing an appropriate level of risk. While Monte Carlo financial planning is a more flexible framework, geometric mean analysis may be less error prone, theoretically justifiable and convenient. Controversies that have limited geometric mean analysis applications are resolvable by improved understanding of distributional properties and rational decision-making issues.