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White Nights and Polar Lights: Investing in the Russian Oil Industry

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White Nights and Polar Lights: Investing in the Russian Oil Industry
1. The valuation process firms undergo when looking to acquire a company is very complex, but when a company, especially a foreign one, makes the conscious decision to enter another foreign market is even more complex and tricky. In this case three western oil firms the neophyte Philbro; the legacy Mobil; and, the middle weight Conoco all have to determine if and how they want to enter the newly open Russian Oil market. The Russian oil market is characterized as high risk for potentially high rewards. High risks include but are not limited to obsolete and poor infrastructure; murky and opaque governmental (and subsequently economic) policies; unreliable Russian geologic surveys; and, etc. Furthermore, Russian inflation is soaring and the value of the ruble is plummeting.

Western (primarily US) oil firms realize that (at the time of this case) the US is lacking in oil reserves and will need petroleum for their behemoth economy, hence there are tremendous opportunities to earn enormous sums of money in Russia. Geologic experts believe that Russia has the 5th largest supply of oil and the largest natural gas reserves in the world and they need money. Russia is the world’s largest single producer of crude. Russia is also situated between Japan and Europe

Philbro, Mobil, and Conoco have to negotiate all of these issues and include them into their valuation to determine if there is value in Russian oil.

With such complexity, I would tend to favor utilizing the Monte Carlo method (MCM) rather than just a straight Net Present Value (NPV) calculation. MCM will essentially allow a company to construct a probabilistic financial model that will analyze this project’s net present value (NPV) against cash flow component uncertainty (e.g. fluctuating Russian tax rates and ruble devaluation). Essentially, an MCM by incorporating random element and values, e.g. uncertainty, into a NPV calculation, where each set of values for uncertainties, can be generate multiple and

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