Reasoning with Cases and Knowledge Graphs to Uncover Relationships in Financial Markets
The stochastic nature of financial markets reflects a complex network of interactions, making them a challenging target for analysis and prediction. Within this application domain, identifying meaningful relationships between financial assets is a difficult but important problem for various financial applications, including portfolio optimization, benchmarking company performance, identifying peers and competitors and quantifying market share. However, with recent research, particularly those using machine learning (ML) and deep learning (DL) techniques, focused mostly on returns forecasting, the literature investigating the modelling of asset correlations has lagged somewhat. To address this, the focus of this work is on developing novel ML and DL frameworks to successfully uncover relationships between financial assets. These frameworks will leverage multiple data modalities, and the efficacy of the learned relationships will be demonstrated on several downstream tasks in the financial domain, including portfolio optimization, returns forecasting and sector classification.