  Deep learning has shown remarkable results on Euclidean data (e.g. audio,
images, text) however this type of data is limited in the amount of relational
information it can hold. In mathematics we can model more general relational
data in a graph structure while retaining Euclidean data as associated node or
edge features. Due to the ubiquity of graph data, and its ability to hold
multiple dimensions of information, graph deep learning has become a fast
emerging field. We look at applying and optimising graph deep learning on a
finance graph to produce more informed clusters of companies. Having clusters
produced from multiple streams of data can be highly useful in quantitative
finance; not only does it allow clusters to be tailored to the specific task
but the culmination of multiple streams allows for cross source pattern
recognition that would have otherwise gone unnoticed. This can provide
financial institutions with an edge over competitors which is crucial in the
heavily optimised world of trading. In this paper we use news co-occurrence and
stock price for our data combination. We optimise our model to achieve an
average testing precision of 78% and find a clear improvement in clustering
capabilities when dual data sources are used; cluster purity rises from 32% for
just vertex data and 42% for just edge data to 64% when both are used in
comparisons to ground-truth Bloomberg clusters. The framework we provide
utilises unsupervised learning which we view as key for future work due to the
volume of unlabelled data in financial markets.
