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Title: The effects of credit ratings in mergers and acquisitions
Author: Karampatsas, Nikolaos
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2013
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This thesis studies the effects of the credit ratings in mergers and acquisitions (M&As). The first chapter establishes that credit ratings affect the choice of payment method in mergers and acquisitions. I find that bidders holding a high rating level are more likely to use cash financing in a takeover. I attribute this finding to lower financial constraints and enhanced capability of highly rated firms to access public debt markets as implied by their higher credit quality. The second chapter investigates the effect of the proximity to credit rating changes on the acquisition decisions of the bidding firms. I apply different measures to proxy for a potential credit rating change and 1 find a non-linear association between firms' real credit rating levels (cred it quality) and acquisition decisions. Furthermore. I show that all my proxies for future credit rating upgrades (downgrades) are positively (negatively) associated with acquisition decisions. Overall the findings in this chapter support my hypotheses and specifically. document the real impact of CRAs' ratings and opinions on firms' takeover policies. The third chapter re-examines the shareholder wealth effects around the announcements of mergers between bidders and targets that complement each other on the levels of debt capacity and growth opportunities, when high degrees of information asymmetry prevail. In sum I find that this type of merger transactions creates value for the combined firms as also for the bidding firms. Regarding the target firms there is some evidence of value destruction which nonetheless. comes at the benefit of the combined and bidding firms as the latter firms avoid overpayment. Additionally. the significant effect of the complementary fit on synergy, bidding and target firm returns is mainly driven by the group of target firms that operate under a high information asymmetry environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available