  This work proposes a novel portfolio management technique, the Meta Portfolio
Method (MPM), inspired by the successes of meta approaches in the field of
bioinformatics and elsewhere. The MPM uses XGBoost to learn how to switch
between two risk-based portfolio allocation strategies, the Hierarchical Risk
Parity (HRP) and more classical Na\"ive Risk Parity (NRP). It is demonstrated
that the MPM is able to successfully take advantage of the best characteristics
of each strategy (the NRP's fast growth during market uptrends, and the HRP's
protection against drawdowns during market turmoil). As a result, the MPM is
shown to possess an excellent out-of-sample risk-reward profile, as measured by
the Sharpe ratio, and in addition offers a high degree of interpretability of
its asset allocation decisions.
