Zohrehvand, Amirhossein;
Doshi, Anil R;
Vanneste, Bart S;
(2024)
Generalizing event studies using synthetic controls: An application to the Dollar Tree–Family Dollar acquisition.
Long Range Planning
, 57
(1)
, Article 102392. 10.1016/j.lrp.2023.102392.
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Abstract
Event studies, which have significantly advanced mergers and acquisitions (M&A) research, obtain excess returns based on a theory linking a firm's shareholder returns to those of the market. For outcomes lacking such a theory, we propose an empirical approach using a synthetic control method with machine learning to link outcomes for the acquirer or target to those for a group of comparison firms. We discuss the method's assumptions, its close parallel to event studies, and its difference in weighting comparison firms (based on data versus derived from theory). We provide an illustration of Dollar Tree's acquisition of Family Dollar, by analyzing shareholder returns (to demonstrate consistent results with an event study), realized cost and sales synergies, and customer sentiment (derived from more than 52 million Twitter messages). We highlight this method's potential—for M&A and other areas of strategy research—to open up new lines of inquiry.
| Type: | Article |
|---|---|
| Title: | Generalizing event studies using synthetic controls: An application to the Dollar Tree–Family Dollar acquisition |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.lrp.2023.102392 |
| Publisher version: | https://doi.org/10.1016/j.lrp.2023.102392 |
| Language: | English |
| Additional information: | © The Author(s), 2024. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Event studies analysis, Mergers and acquisitions, Synthetic control method, Longitudinal design, Regularized regression, Elastic net, Tweets |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10176569 |
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