  Changes in market conditions present challenges for investors as they cause
performance to deviate from the ranges predicted by long-term averages of means
and covariances. The aim of conditional asset allocation strategies is to
overcome this issue by adjusting portfolio allocations to hedge changes in the
investment opportunity set. This paper proposes a new approach to conditional
asset allocation that is based on machine learning; it analyzes historical
market states and asset returns and identifies the optimal portfolio choice in
a new period when new observations become available. In this approach, we
directly relate state variables to portfolio weights, rather than firstly
modeling the return distribution and subsequently estimating the portfolio
choice. The method captures nonlinearity among the state (predicting) variables
and portfolio weights without assuming any particular distribution of returns
and other data, without fitting a model with a fixed number of predicting
variables to data and without estimating any parameters. The empirical results
for a portfolio of stock and bond indices show the proposed approach generates
a more efficient outcome compared to traditional methods and is robust in using
different objective functions across different sample periods.
