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Analytics of financial data is inherently a Big Data paradigm, as such dataare collected over many assets, asset classes, countries, and time periods.This represents a challenge for modern machine learning models, as the numberof model parameters needed to process such data grows exponentially with thedata dimensions; an effect known as the Curse-of-Dimensionality. Recently,Tensor Decomposition (TD) techniques have shown promising results in reducingthe computational costs associated with large-dimensional financial modelswhile achieving comparable performance. However, tensor models are often unableto incorporate the underlying economic domain knowledge. To this end, wedevelop a novel Graph-Regularized Tensor Regression (GRTR) framework, wherebyknowledge about cross-asset relations is incorporated into the model in theform of a graph Laplacian matrix. This is then used as a regularization tool topromote an economically meaningful structure within the model parameters. Byvirtue of tensor algebra, the proposed framework is shown to be fullyinterpretable, both coefficient-wise and dimension-wise. The GRTR model isvalidated in a multi-way financial forecasting setting and compared againstcompeting models, and is shown to achieve improved performance at reducedcomputational costs. Detailed visualizations are provided to help the readergain an intuitive understanding of the employed tensor operations.

Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling