GRIP: Graphical Models Revealing Insights for Portfolio Replication - A Learning Approach

Abstract

This paper presents a new and effective methodology for decoding strategies in the context of investment portfolios. The proposed approach relies on Dynamic Bayesian Graphical Models, which are powerful tools for capturing complex relationships and dependencies in data over time. Using these models, we can accurately decode the hidden strategy within the investment universe. By leveraging Dynamic Bayesian Graphical Models, we calculate dynamic weights that exhibit the most stable allocation rules. Through extensive experimentation on various investment scenarios, we demonstrate that our approach achieves high accuracy in decoding strategies. The method’s reliance on Dynamic Bayesian Graphical Models enables it to effectively uncover hidden patterns and relationships within the investment data, leading to improved portfolio allocation decisions and robust generalization across different market conditions.

Keywords: Graphical Models, Portfolio allocation

JEL Classification: G11, G13