  We construct the maximally predictable portfolio (MPP) of stocks using
machine learning. Solving for the optimal constrained weights in the
multi-asset MPP gives portfolios with a high monthly coefficient of
determination, given the sample covariance matrix of predicted return errors
from a machine learning model. Various models for the covariance matrix are
tested. The MPPs of S&P 500 index constituents with estimated returns from
Elastic Net, Random Forest, and Support Vector Regression models can outperform
or underperform the index depending on the time period. Portfolios that take
advantage of the high predictability of the MPP's returns and employ a Kelly
criterion style strategy consistently outperform the benchmark.
