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Prediction and visualization of Mergers and Acquisitions using Economic Complexity
Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering t...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069768/ https://www.ncbi.nlm.nih.gov/pubmed/37011046 http://dx.doi.org/10.1371/journal.pone.0283217 |
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author | Arsini, Lorenzo Straccamore, Matteo Zaccaria, Andrea |
author_facet | Arsini, Lorenzo Straccamore, Matteo Zaccaria, Andrea |
author_sort | Arsini, Lorenzo |
collection | PubMed |
description | Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or explore possible innovation strategies. |
format | Online Article Text |
id | pubmed-10069768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100697682023-04-04 Prediction and visualization of Mergers and Acquisitions using Economic Complexity Arsini, Lorenzo Straccamore, Matteo Zaccaria, Andrea PLoS One Research Article Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or explore possible innovation strategies. Public Library of Science 2023-04-03 /pmc/articles/PMC10069768/ /pubmed/37011046 http://dx.doi.org/10.1371/journal.pone.0283217 Text en © 2023 Arsini et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Arsini, Lorenzo Straccamore, Matteo Zaccaria, Andrea Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title | Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title_full | Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title_fullStr | Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title_full_unstemmed | Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title_short | Prediction and visualization of Mergers and Acquisitions using Economic Complexity |
title_sort | prediction and visualization of mergers and acquisitions using economic complexity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069768/ https://www.ncbi.nlm.nih.gov/pubmed/37011046 http://dx.doi.org/10.1371/journal.pone.0283217 |
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