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Determinants Of Mergers And Acquisition In The Oil And Gas Industry

Posted on:2019-02-06Degree:MasterType:Thesis
Country:ChinaCandidate:Giovanni PERINIFull Text:PDF
GTID:2481305891985059Subject:Business Administration
Abstract/Summary:PDF Full Text Request
The purpose of this paper is to analyze the determinants of mergers and acquisitions in the oil and gas industry.In particular,it aims to identify the distinctive financial characteristics of acquiring and target companies.Indeed,the previous literature has not studied in isolation the oil and gas industry dynamics,and therefore this paper contributes in increasing the knowledge of this sector under a corporate finance perspective.This study uses a quantitative deductive approach in order to arrive to empirical results.The observation sample comprises 342 deals that took place solely in the oil and gas industry throughout the period 2008-2018.A binomial logistic regression model is employed in order to determine which firm characteristics are statistically significant for both acquiring and target companies.The research has found that:(1)overvalued companies are likely to become acquirers,while undervalued firms are likely to be target companies;(2)companies with high availability of free cash flow are likely to become acquirers;(3)large companies are more likely to become acquirers,while small firms are more likely to be targets;(4)acquiring companies are more likely to have low leverage,while target companies present a higher leveraged capital structure.As this paper does not take into account the different oil price environments oil and gas companies could face,it is recommended for future studies to examine whether firm characteristics change during periods where oil prices are relatively high or low.Moreover,a multinomial regression model could be used to take into account other takeover features that could impact the results,such as type(friendly/hostile)and direction(horizontal/vertical).
Keywords/Search Tags:mergers and acquisitions, oil and gas, logistic regression
PDF Full Text Request
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