# Predicting Mergers and Acquisitions using Graph-based Deep Learning

arXiv: https://arxiv.org/abs/2104.01757 · HTML: https://mlsanity.com/paper/2104.01757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction](https://mlsanity.com/paper/1902.08329/) (2019)

## Abstract (arXiv)

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.