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Merger and Acquisitions' biases and the introduction of data-driven environment to limit value destruction.

Andrea Rocco

Merger and Acquisitions' biases and the introduction of data-driven environment to limit value destruction.

Rel. Riccardo Calcagno. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2022

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Abstract:

Mergers and Acquisitions are among those operations able to launch a career of a manager or make it fall into the deepest chasm. Despite the mere meaning in the eyes of managers, the outcome in terms of value creation of these operations is tied to a lot of different endogenous and exogenous factors like country, the market where the firms are operating and more in general, by the performances of worldwide economic conditions. Going through the subject matter is then discussed the tender offer and, the other two most common ways the buyer negotiates with the seller, Negotiation one-to-one and auction. To increase the level of detail of the analysis is important to understand the fundamentals of behavioral theory and the concepts of financial and operating synergies meant to be drivers when dealing with M&A. However, those cited above are just theories, and the reality proves that different biases are added to the decision process that leads the Board to decide whether to close the deal or not. They usually have to do with attributes of acquiring firm, the CEO’s relationship with the target’s board and financial constraints due to the firm’s capital structure. The result is that 60% of recorded operations in the last decade destroy value. Business Intelligence comes as a solution to overcome these common biases thanks to the data sharing within the organization in a way that the data themselves tell a story and bring useful insights for the board in a new data-driven decision process.

Relators: Riccardo Calcagno
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 55
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/25405
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