Анализ реальных опционов как метод оценивания ИТ проектов. Кейс российской нефтяной компании.

Today, there is a variety of methods that companies use for the valuation of investment projects in order to make profitable decisions. Such methods can be grouped into three types: static, dynamic and alternative ones (see Figure 1 below).
Figure 1. Methods of investment projects financial valuation
Source: compiled by the author
Static approaches include calculation of Accounting Rate of Return (ARR), Payback Period (PP). The dynamic methods are analysing the project’s Net Present Value (NPV), Discounted Payback Period (DPP), Internal Rate of Return (IRR), Profitability Index (PI). Together with static methods, dynamic ones are considered to be traditional and most used ones, but for some projects (e.g. with the high level of uncertainty) they can be not enough. Also, there are alternative ways of investment project valuation (e.g. estimation of Economic Value Added (EVA), Real Option Valuation (ROV)), which are far more complex but help to take into account more factors influencing investment decisions.
The aim of this master thesis is to propose and to test the alternative method of investment project valuation for one of the Russian largest oil companies, Gazprom Neft. Also, this research is aimed at valuation of not all company investment initiatives, but only of IT projects of Gazprom Neft Upstream (Exploration and Production Block, BRD in Russian).
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Investment project is “a set of investment modules, united by organizational, territorial, functional or other principle in order to effectively implement investments”1. And IT project is defined by the company as “a set of project stages, as a rule, defined as costs, aimed at the development and implementation of information systems and means of automation of activities and telecommunications in the divisions of the company (at mining and production facilities, as well as in administrative centers)”. So, the object of this master thesis is the IT project of the Gazprom Neft Upstream, which is currently needed to be evaluated for the investment attractiveness in order to be continued or to be terminated. And the tasks are to conduct the “as is” analysis of the IT projects valuation process in Gazprom Neft; to find the gap in the “as is” process; to elaborate the model.
And when it comes to the valuation of IT projects, traditional approaches (static and dynamic, see the Figure 1 above) might not work. The reason is that they either ignore or cannot properly capture the need of management to be flexible and to adapt/revise the decision based on uncertainties following every IT project. IT projects are lying very closely to R&D ones, as both of groups are characterized by the high rate of volatility, innovativeness and uncertainty. Currently, Gazprom Neft Upstream uses DCF method to evaluate its projects. The managers responsible for making the investment decision miss the opportunity to evaluate the project from probabilistic point of view, which often leads to “false negative” or “false positive” result: many projects happen to be less or more promising that they really are. It leads to the wrong investment decision making and, as a consequence, the company is missing out on potential benefits. Among alternative methods of investment valuation, EVA is not the best tool to use in this case, as IT projects of the company are usually evaluated through implementation costs. So, ROV is the instrument of analysis that could bring the company closer to making more accurate investment decisions in case of highly volatile IT projects.
So, this master thesis observes and analyzes the opportunity of valuation of Upstream IT projects portfolio of one of the biggest Russian oil company “Gazprom Neft” based on the method of Real Option Valuation. The paper proposes the model based on the ROV (using Black-Scholes model as a basis). The format of the thesis is the applied project, so the results of the work might be used by the company’s managers in order to assess the investment IT projects with the higher prediction chances. All the data used in the work is provided by the company or taken from the public sources.