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Influence of Oil Price Crisis 2014-2015 on M&A Upstream Sector And Valuation Models of Upstream Assets

Student: Solovyev Andrey

Supervisor: Grigory Kantorovich

Faculty: International College of Economics and Finance

Educational Programme: Double degree programme in Economics of the NRU HSE and the University of London (Bachelor)

Year of Graduation: 2016

This work is not only a mini-study on the valuation of assets, but the consideration of the industry itself. In the first chapter of the work the M&A market was identified market together with discussion of the basic concepts and definitions and the causes of the companies’ activity on this market and methods of financing of such transactions have been examined. Further, the first chapter discussed the main trends in the upstream of the oil and gas during recent years of low oil prices. The second chapter of the work considered the specifics of the oil and gas industry and main methods of valuation of assets and alternative method was proposed. Further work is deepened in the industry itself, where the main features of the oil and gas industry were discussed. Finally, the third chapter suggested the model for the valuation of the upstream assets and companies. The Ordinary Least Squares method was taken for estimating the corresponding parameters. Since the OLS method was used, the issue of the fulfillment of Gauss-Markov conditions was considered to be very important. Hence, all the necessary conditions were checked after the estimation. In order to choose between log – log and lin – log model Zarembka Scaling was performed on the basis of which the double-log specification was used. All the coefficients showed reasonable signs and addition of the low oil price trend reverses the sign which was very expected. This proves my hypothesis, that there exists a relationship between the value of the upstream assets and specific to that assets factors and types of extraction and contribution of those factors and types depends on oil market price trends. The model was further validated through the out-of-sample analysis by the means of the different indices which allowed to claim the the model has good predictive power.

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