# Graphical Models for Financial Time Series and Portfolio Selection

arXiv: https://arxiv.org/abs/2101.09214 · HTML: https://mlsanity.com/paper/2101.09214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)

## Built on by (most cited first, 1 of 1)

- [Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics](https://mlsanity.com/paper/2412.18594/) (2024)

## Abstract (arXiv)

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.