Graphical Models for Financial Time Series and Portfolio Selection
This work addresses portfolio selection for asset management, but it appears incremental as it applies existing graphical models to financial time series.
The authors tackled the problem of constructing optimal portfolios by applying graphical models to capture time-varying patterns in covariance matrices, resulting in strategies that generated steadily increasing returns with low risk and outperformed the S&P 500 index.
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.