  We examine a variety of graphical models to construct optimal portfolios.
Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and
structural learning can capture the time varying patterns in the covariance
matrix and allow the creation of an optimal and robust portfolio. We compared
the resulting portfolios from the different models with baseline methods. In
many cases our graphical strategies generated steadily increasing returns with
low risk and outgrew the S&P 500 index. This work suggests that graphical
models can effectively learn the temporal dependencies in time series data and
are proved useful in asset management.
