Applied Mergers and Acquisitions

Robert F. Bruner, Joseph R. Perella

A comprehensive guide to the world of mergers and acquisitions Why do so many M&A transactions fail? And what drives the success of those deals that are consummated? Robert Bruner explains that M&A can be understood as a response by managers to forces of turbulence in their environment. Despite the material failure rates of mergers and acquisitions, those pulling the trigger on key strategic decisions can make them work if they spend great care and rigor in the development of their M&A deals. By addressing the key factors of M&A success and failure, Applied Mergers and Acquisitions can help readers do this. Written by one of the foremost thinkers and educators in the field, this invaluable resource teaches readers the art and science of M&A valuation, deal negotiation, and bargaining, and provides a framework for considering tradeoffs in an effort to optimize the value of any M&A deal.

Business & EconomicsBusiness & Economics · medium confidenceBusiness & Economics81 signalsLlm Classifier: Business & Economics -> Business & EconomicsKeyword Classifier: Arts & Criticism -> Arts & CriticismAward Category: PROSE Award for Business, Finance, and Management -> Business & EconomicsScience & DiscoveryArt, Music & PerformanceEnvironment, Conservation & PollutionEssays & Cultural Criticism
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

  3. 03 Suggested argument

    M&A outcomes can be improved because deal success depends on managers’ care and rigor in developing deals that respond to turbulence, even though many M&A transactions fail materially.

    Model confidence 62%

  4. 04 Reading orientation

    35 / 100 academic

    Serious trade

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 72%.