# Mixing Portfolio Strategies

Given a set of $N$ assets, the amount of strategies proposed in literature to diversify the investors wealth in order to find the 'optimal' portfolio is overwhelming. However, for example DeMiguel et al point out that estimation error is large in general, leading to bad out-of-sample returns for many sophisticated portfolio strategies.

In my mind it should be a straightforward idea to combine several promising allocation candidates such that the estimation risk is 'diversified' as well. In other words, given a set of portfolio weights proposed by $k$ distinct strategies $\{\omega_1,\ldots,\omega_k\}$, it could be beneficial to compute a vector $c\in\mathbb{R}^k, \left( \sum\limits_{i=1}^{k} c_i =1\right)$ based on some criteria and then investing into $\omega^*:=c_1\omega_1+\ldots +c_k\omega_k$.

I am aware of some ideas combining two portfolios via shrinkage approaches (see for example Posterior Inference for Portfolio Weights and the appendix of Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?)

but I am would like to learn more about those ideas. Any reference or further comment directing in this direction is welcome to answer my question: What are the benefits and gains from combining several allocation strategies?