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dc.contributor.advisorThorburn, Karin
dc.contributor.authorKristoffersen, Marcus
dc.contributor.authorSællmann, Sebastian
dc.date.accessioned2020-03-05T13:37:47Z
dc.date.available2020-03-05T13:37:47Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/11250/2645536
dc.description.abstractThis master thesis examines different aspects of projected synergies made by the management of acquiring firms. We study how synergies are estimated, how they affect bid premiums, and if disclosing acquirers are likely to avoid overpayment. From SDC, we retrieve information on the size of projected synergies and relevant financial data on acquirers and targets. We apply this data in three different OLS regressions. We hypothesise that targets with high expense levels are more likely to create cost synergies for acquirers. When testing the hypothesis, our regression suggests that the expense levels of targets seem to increase the projected synergies. Furthermore, our second hypothesis claims that the size of the projected synergies has a positive relationship with bid premiums. Our findings support the suggested hypothesis. However, disclosing acquirers seem to, on average, pay a lower premium than non-disclosing acquirers. This might be due to inherent differences in deal characteristics between disclosing deals and non-disclosing deals. Our last hypothesis is that the market believes that disclosing acquirers are less likely to overpay for the target. We find that acquirer CAR seems to increase with premiums paid by disclosing acquirers. One possible explanation for this is that the market believes that disclosing acquirers are more likely to avoid overpayment. However, there might exist other explanations since disclosing acquirers seem to pay lower premiums, and the reason for this is not clear.en_US
dc.language.isoengen_US
dc.subjectfinancial economicsen_US
dc.titleThe role of projected synergies in M&A : an empirical study on management projected synergies, and its implications for bid premiums and overpaymenten_US
dc.typeMaster thesisen_US
dc.description.localcodenhhmasen_US


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