Minimum-allocation robustness conjecture in portfolio optimization
Establish whether imposing a minimum per-asset allocation constraint (for example, requiring each selected stock to receive at least 5% of the portfolio) increases the robustness of optimal portfolios in portfolio optimization models, measured by reduced sensitivity of allocations to small perturbations in return and covariance inputs, relative to unconstrained formulations such as the Maximum Drawdown linear program and classical Markowitz models.
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Therefore, it seems that a simple way to increase the robustness of any given portfolio optimization model is to require a minimum allocation for each selected stock (at the expense of turning the portfolio optimization model into a MILP, but it seems this is not necessarily an issue for problems with a size and structure similar to ours, especially with today's solvers and computational power). Naturally, the previous claim would require a deeper analysis to be more conclusive, but it seems at least a reasonable conjecture in light of the evidence we have and the intuitive connection with overfitting.