Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Arsini, Lorenzo, Straccamore, Matteo, Zaccaria, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785018913408417792
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
work_keys_str_mv AT arsinilorenzo predictionandvisualizationofmergersandacquisitionsusingeconomiccomplexity
AT straccamorematteo predictionandvisualizationofmergersandacquisitionsusingeconomiccomplexity
AT zaccariaandrea predictionandvisualizationofmergersandacquisitionsusingeconomiccomplexity