Introduction 2022
Chapter 1. Value-based management in oil and gas industry 4539
1.1. Value-based management concept 9
1.2. Fundamental value and residual income 11
1.3. Value drivers tree 13
1.4. Application of Dupont model: Empirical studies overview 15
1.5. Oil and gas companies in Russia, India and China 20
1.6. Specific characteristics of value creation in oil and gas 29
Chapter 2. Empirical Study 31
2.1. Methodology 31
2.2. Data 34
2.3. Research questions 43
2.4. Results of regression analysis 4576
2.5. Findings discussion 51
2.6. Managerial implications 53
2.7. Limitations and further research suggestions 56
Conclusion 57
Appendix 59
References 67
The concept of value-based management has been established as one of the key concepts in managing companies’ performance during the recent decades. Starting with the fundamental works by [Rappaport, 1986; Brayley and Mayers, 1981; Copeland, 1995] it has now become an integral part of strategic and operating decision-making in business. The principles of value¬based management which set the maximization of the value for the shareholders as a key goal also serve as a basis for the evaluation of the company’s performance and the identification of the value drivers.
This master thesis is devoted to the identification of the value drivers in oil and gas industry. The sector was chosen as one of the most influential in global economy and, due to a recent crisis, the one that requires a clear understanding of performance and value creation factors. The countries for the study - Russia, India and China - were identified as those that have been investigated much less than mature western ones. The identification of value drivers will be performed via Dupont Model with its operational components: many authors focus on DuPont analysis on the first level of the model, considering operating margin, assets turnover and leverage, but there is a limited number of works connected to the operational level which will be explored here.
Therefore, main goal of the research is to identify the main drivers of profitability and value creation in oil and gas companies in Russia, India and China.
Consequently, the objectives of the research are the following.
• Conduct a literature review on value-based management concepts, in particular residual income valuation model and Dupont model as well as specifics of oil and gas industry
• Collect the data and form the representative dataset for the analysis
• Carry out the regression analysis on the components of DuPont model and industry-specific factors across the countries
• Draw conclusions about the relationships of operational DuPont model components and industry-specific factors on the profitability and value creation in the countries of analysis
• Provide practical implications for managers and investors based on the research
The results of the work will be useful in the following situations:
• Firstly, the results of the research will be valuable for managers of oil and gas companies across countries studied. To get an adequate return on equity and assets, as well as control the value of the company, it is crucial to understand the drivers that contribute to the change and take into account the models that can be used for the analysis.
• Secondly, the results will be useful for investors and investment analytics that are making forecasts about the companies’ performance compared to the expectations of the market. While traditional multipliers are often used for the short-term analysis, it would be beneficial to understand how the components of DuPont model, coupled with industry-specific factors, could contribute to the explanation of profitability and value of the companies.
Value-based management has become one of the most important concepts for the evaluation of performance of companies and choice of particular value factors that are crucial to achieving profitability and shareholder value creation. DuPont model, as the comprehensive tool for analysis, can contribute significantly to value drivers’ identification up to operational level, especially coupled with industry-specific factors, although the number of studies in the field is quite limited for developing countries and particular industries. That is why it was decided to concentrate on value drivers’ identification of oil and gas companies in India, China and Russia: oil and gas industry is generally characterized by high dependency on macroeconomic factors and the question of how companies can control profitability and value despite the changes that cannot be managed is always topical. In addition, oil and gas companies in Russia, China and India play a crucial strategic role in the development of the economy and, considering recent crisis situation, are facing additional challenges that need to be addressed via the identification of crucial profitability and value creation factors.
The study was based on 10 Indian, 11 Russian and 3 Chinese companies, from year 2011 to 2015, in total 120 observations. The overall situation for oil and gas companies in the countries of the analysis was investigated, as well as a review on value-based management and existing studies on DuPont model application in value drivers’ identification conducted. Then, regression analysis was performed to identify the relationships between return on assets, return on equity, residual income, market value and fundamental value as dependent variables and DuPont second level components as well as industry-specific factors and composite indicators as independent variables. As a result, significant relationships were identified based on the models including combination of factors, providing managers of oil and gas companies and investors with the insights for decisions concerning the companies in the countries studied. Nevertheless, it is important to mention that the research was limited by the time and information available and serves as a base for further studies where more factors can be included as well as larger time frame and bigger number of countries considered to allow for more tools of the analysis to be used and discover the relationships (or their absence) that have not been identified in the current study.
