Li, Y;
Shou, J;
Treleaven, P;
Wang, J;
(2021)
Graph neural network for merger and acquisition prediction.
In:
Proceedings of the ICAIF 2021 - 2nd ACM International Conference on AI in Finance.
(pp. pp. 1-8).
ACM (Association for Computing Machinery)
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Abstract
This paper investigates the application of graph neural networks (GNN) in Mergers and Acquisitions (M&A) prediction, which aims to quantify the relationship between companies, their founders, and investors. M&A is a critical management strategy to decide if the company is to grow or downsize, and M&A prediction has been a challenging research topic in the past few decades. However, the traditional methods of predicting M&A probability are only based on the company's fundamentals, such as revenue, profit, or news. Instead, GNN takes full advantage of those relationship data to expand feature dimension and improve the prediction result. Our M&A prediction solution integrates with the topic model for text analysis, advanced feature engineering, and several tricks to boost GNN. The approach achieves a high Area-Under-Curve score (AUC) 0.952, which is better than the previous record 0.888. The true positive rate is 83% with a low false positive rate 7.8%, which performance is better than the previous benchmark record 70.9%/10.6%.
| Type: | Proceedings paper |
|---|---|
| Title: | Graph neural network for merger and acquisition prediction |
| Event: | ICAIF'21: 2nd ACM International Conference on AI in Finance |
| ISBN-13: | 9781450391481 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3490354.3494368 |
| Publisher version: | https://doi.org/10.1145/3490354.3494368 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151274 |
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