In Defense of Optimization: The Fallacy of 1/N

Previous research has shown that equally weighted portfolios outperform optimized portfolios, which suggests that optimization adds no value in the absence of informed inputs. This article argues the opposite. With naive inputs, optimized portfolios usually outperform equally weighted portfolios. The ostensible superiority of the 1/N approach arises not from limitations in optimization but, rather, from reliance on rolling short-term samples for estimating expected returns. This approach often yields implausible expectations. By relying on longer-term samples for estimating expected returns or even naively contrived yet plausible assumptions, optimized portfolios outperform equally weighted portfolios out of sample.