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arXiv · 2104.01757

Predicting Mergers and Acquisitions using Graph-based Deep Learning

Keenan Venuti
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Abstract

The graph data structure is a staple in mathematics, yet graph-based machine learning is a relatively green field within the domain of data science. Recent advances in graph-based ML and open source implementations of relevant algorithms are allowing researchers to apply methods created in academia to real-world datasets. The goal of this project was to utilize a popular graph machine learning framework, GraphSAGE, to predict mergers and acquisitions (M&A) of enterprise companies. The results were promising, as the model predicted with 81.79% accuracy on a validation dataset. Given the abundance of data sources and algorithmic decision making within financial data science, graph-based machine learning offers a performant, yet non-traditional approach to generating alpha.

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Keenan Venuti. 2021-04-05. Predicting Mergers and Acquisitions using Graph-based Deep Learning. https://arxiv.org/abs/2104.01757

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