Machine learning-based similarity measure to forecast M&A from patent data
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929309532291072 |
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| author | Albora, Giambattista Straccamore, Matteo Zaccaria, Andrea |
| author_facet | Albora, Giambattista Straccamore, Matteo Zaccaria, Andrea |
| contents | Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_07179 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning-based similarity measure to forecast M&A from patent data Albora, Giambattista Straccamore, Matteo Zaccaria, Andrea Physics and Society Statistical Finance Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making. |
| title | Machine learning-based similarity measure to forecast M&A from patent data |
| topic | Physics and Society Statistical Finance |
| url | https://arxiv.org/abs/2404.07179 |