  This work aims to deal with the optimal allocation instability problem of
Markowitz's modern portfolio theory in high dimensionality. We propose a
combined strategy that considers covariance matrix estimators from Random
Matrix Theory~(RMT) and the machine learning allocation methodology known as
Nested Clustered Optimization~(NCO). The latter methodology is modified and
reformulated in terms of the spectral clustering algorithm and Minimum Spanning
Tree~(MST) to solve internal problems inherent to the original proposal.
Markowitz's classical mean-variance allocation and the modified NCO machine
learning approach are tested on financial instruments listed on the Mexican
Stock Exchange~(BMV) in a moving window analysis from 2018 to 2022. The
modified NCO algorithm achieves stable allocations by incorporating RMT
covariance estimators. In particular, the allocation weights are positive, and
their absolute value adds up to the total capital without considering explicit
restrictions in the formulation. Our results suggest that can be avoided the
risky \emph{short position} investment strategy by means of RMT inference and
statistical learning techniques.
