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Predicting Mergers and Acquisitions using Graph-based Deep Learning

by   Keenan Venuti, et al.

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 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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