  We find economically and statistically significant gains when using machine
learning for portfolio allocation between the market index and risk-free asset.
Optimal portfolio rules for time-varying expected returns and volatility are
implemented with two Random Forest models. One model is employed in forecasting
the sign probabilities of the excess return with payout yields. The second is
used to construct an optimized volatility estimate. Reward-risk timing with
machine learning provides substantial improvements over the buy-and-hold in
utility, risk-adjusted returns, and maximum drawdowns. This paper presents a
new theoretical basis and unifying framework for machine learning applied to
both return- and volatility-timing.
