The reasons why maximum diversification is better than minimum risk, including in terms of risk

Abstract In well-defined experimental settings, we evaluate the out-of-sample performance of two asset allocation paradigms: minimum risk and maximum diversification. Specifically, for each given risk measure, we compare the optimal minimum risk allocation with the allocation obtained by maximizing a portfolio diversification measure induced by the same risk measure. The experiment is performed in an out-of-sample, long-only framework, accounting for proportional transaction costs and different lengths of both the estimation window and the holding period. The strategies are compared in terms of numerical stability, return, the Sharpe ratio, and risk, as measured through the same risk measures used for calculating the optimal allocation: variance of returns, mean absolute deviation, value at risk, and expected shortfall at significance levels of 1% and 5%. We show that the maximum diversification strategies are highly competitive, if not generally superior, to the risk minimization allocations. This result supports well-known empirical findings of naive investment strategies that are difficult to beat in practice. Risk minimization strategies require highly accurate forecasts of future returns to perform well. Moreover, these strategies exhibit extreme numerical instability, where even infinitesimal variations in the inputs can dramatically alter the optimal allocation. Therefore, implementation costs are high, significantly impairing performance. In contrast, maximum diversification strategies are less sensitive to minor changes in the input parameters, providing stable allocations that are less affected by transaction costs. Furthermore, these strategies do not require accurate predictions of future returns and are effective in controlling investment risk.