Summary
Innoseis is a start-up with an innovative solution for detecting oil and gas fields. This project is designed to answer how volatile energy market affects the value of the energy related start-ups in light of higher risk and uncertainty. According to the valuation techniques, the value of a company is determined by the present value of its future cash flow. Despite the widely held belief that the future cash flow of the oil related companies is highly dependent on risks and uncertainties pertaining to unstable oil price and volatility in energy market, this paper proves that the impact of oil market volatility is very limited. In order to the prove this assumption, the paper investigates the energy market in order to assess past and future trends in the market, based on which the risk and uncertainties for the oil related start-ups are identified. These uncertainties are incorporated into the valuation process to calculate the value of these start-ups.
OIL PRICE AND
START-UPS
AMSTERDAM BUSINESS SCHOOL MBA
NAHID JAFAROV
Table of Contents
Summary ... 1
List of figures and tables ... 3
1. Introduction... 4
2. Industry background ... 6
3. Case description ... 9
4. Research questions ... 13
5. Literature review... 13
5.1. Energy price volatility ...13
5.2. Valuation ...16
6. Theoretical framework ... 18
6.1. Supple - demand analysis and the volatility in the energy market ...19
6.2. Supply-demand equilibrium ...20
6.3. Valuation ...30
6.4. Exploitation vs. Exploration (March, 1991) ...33
7. Price sensitivity ... 34
8. Valuation under different scenarios ... 36
8.1. First Scenario: Worst case scenario ...39
8.2. Second Scenario: Constant price level ...40
8.3. Third scenario: Best case scenarios...40
9. Recommendation ... 43
9.1. Exploitation ...44
9.2. Exploration ...45
10. Conclusion ... 46
List of figures and tables
Figure 2. 1 Est. # Wireless Channels Sold in 2016 (thousands) ... 7
Figure 2. 2 Market composition ... 7
Figure 2. 3 Estimate – Installed base of equipment (channels)... 8
Figure 2. 4 Purchasing criteria of nodes ... 9
Figure 3. 1 Product specification of Innoseis………...10
Figure 3. 2 Market share of Innoseis ... 11
Figure 3. 3 Market segmentation ... 12
Figure 5. 1 Primary production and net imports of crude oil in the EU…….…..15
Figure 6. 1 Energy market volatility 19 Figure 6. 2 Hubbert's peak for US crude oil production (1956) ... 22
Figure 6. 3LOPEX oil price scenario at different discount rates ... 24
Figure 6. 4 LOPEX oil price scenarios with different resource bases... 24
Figure 6. 5 Yearly global oil supply prediction for 2012-2035 ... 26
Figure 6. 6 Yearly global oil price for 2015-2035 resulted from the model ... 27
Figure 6. 7 The impact of renewable energy on overall energy demand ... 29
Figure 6. 8 Future oil price scenarios ... 30
Figure 7. 1 Market share of the companies………..34
Figure 7. 2 Market volatility and revenue of Geospoce ... 35
Figure 8.3. 1 Value of Innoseis in different scenarios……….41
Figure 9. 1 Development strategy under different scenarios……….43
Table 6. 1 Energy market volatility ………..19
Table 6. 2 Frameworks for Discounted Free Cash Flow-Based Valuation ... 31
Table 7. 1 Summary output of regression analysis……….36
Table 7. 2 Regression statistics ... 36
Table 8. 1 Financial statement of Innoseis………...37
Table 8. 2 Free Cash Flow of Innoseis (thousand euro) ... 38
Table 8.1. 1 Future cash flow of Innoseis in worst case scenario………..39
Table 8.2. 1 Future cash flow of Innoseis in constant price………40
1. Introduction
Oil has been one of the main important commodities throughout the history of human being, because of which not only manufacturing industry has developed but also the balance of power among states has been highly affected by natural resources. As an important driving force of economy, the fluctuation of oil prices proved to have important effect on the economic development of a country (Rentschler, 2013). The fluctuation of oil prices may bring about a serious economic crisis, as the industrialized economy has been highly dependent on oil production. The economic crises inside the country can be easily translated into a political crisis, as politicians are bound to take full responsibility of the economic performance of their respected countries. According to Barro (1984), there are a number of channels that can transmit oil price shocks into a macro economic crisis, which are demand-side effect and supply-side effect. A positive oil shock leads to higher production cost and thus, it either constrains output level or increases prices.
On the other hand, at the macro level, oil price volatility has led to global financial crisis throughout history. According to Hamilton (1983), the most of economic crises between 1948 and 1980 were caused by significant increase of oil prices as a result of which the GDP of the United States decreased dramatically. It is worth mentioning that at micro level, the impact of oil price shock varies according to industries. As it mentioned by Rentschler (2013), industrial production companies carry a bigger share of burden than service-based industries. The overall impact of oil price fluctuation on economy lies in the fact that it creates uncertainty over the long-term planning process, since many companies find themselves under tremendous pressure and because of it they have to suspend promising projects (Bernanke, 1983).
Energy industry lays ground for the foundation of many start-ups who strive to bring innovation-based technology into the energy industry. These start-ups find the fluctuation of oil prices as the most serious problem in their business performance (Emily Gosden, 2017). These start-ups are hit harder in light of oil price volatility as the volatility increases uncertainties and risks. Given the fact that these companies are at their initial stage with the lack of historical performance records, a small percent
fluctuation in oil price may bring an end to a start-up by diminishing the value of a company or by discouraging potential investors. Taking the sensitivity of these companies to oil price fluctuation into account, the thesis discusses how the fluctuation affects the future value of start-ups, which share the same industry with oil production companies. In other words, the ultimate aim of this paper is to investigate, first, degree of sensitivity of oil related companies to the oil market volatility, and then estimate the value of these companies in the face of changing oil price.
The paper will consist of three parts. The first part is dedicated to the analysis of the energy market with the aim of estimating future trends of energy price. At this level, the casual factors behind the energy market volatility is investigated based on their relative probability of occurrence. The second part of the paper implies the valuation of the company – Innoseis. On this stage, scenario analysis model (Paula et al, 2002) is applied to calculate the present value of the company. Each scenario is assigned different probability based on which the future cash flow of the company is predicted. The third part of the analysis presents recommendation for Innoseis.
2. Industry background
The industry is called seismic survey with a number of companies competing over producing innovative method of extensively investigating the beneath of the earth surface for the exploration of petroleum, natural gas and other mineral deposits. The efficiency of the method depends on a number of important elements such as time interval that elapses between seismic wave that initiated at a selected point, the quality of seismic detectors, which detects the seismic waves as they travel through the different lays of the earth and seismic air guns that are mainly employed to initiate the seismic waves. The industry has flourished as the practice of exploding the underground with the explosive dynamite has become costly and less effective.
In the early face of the development of land seismic survey, hundreds of tons of equipment were required along with a few thousands of people who had to be widely deployed over vast areas for many months (Cocker, 2011). Seismic refraction served as a base for development of seismic reflection exploration method, which was devised first by Ludger Mintrop, a German mine surveyor, in 1914. This embryonic device was used to detect salt domes in Germany. In 1924 Ludger Mintrop founded the company Seismos and was hired to conduct seismic exploration to find oil in the US, which is known as the first commercial application of the seismic exploration method (Sheriff & Geldart, 1995).
This method required cable wires to connect point shops. Given the fact that the investigation for onshore exploration of natural resources covers a wide area, carrying and setting cables over the huge area required massive human capital and thousands of meters length of wires. To solve the complexity problem of the process led scholars and entrepreneurs come up with new method. This method replaced wires with batteries, which were attached to nodes to provide nodes with necessary electric power. To increase the lifecycle of batteries, companies started to produce larger ones, which required additional charger and also did not ease the cost burden of entrepreneurs. This was the main reason behind the invention and application wireless reflection method in the industry of seismic survey. Today, the industry is dominated
by a number of companies such as Ceospace, Zland, Autoseis, Unite, Hawk, Sercel, Inova, Wireless Seismic, Global and etc.
Figure 2. 1 Est. # Wireless Channels Sold in 2016 (thousands)
According to Figure 2.1, Geospace holds the biggest share in the wireless nodes market, since the company could sell 460 thousand wireless channels in 2016. Hawk comes at the end of the list with 50 thousand wireless channels sold in 2016. Despite the fact that wireless channels offer tremendous cost reductions to the oil & gas exploitation companies, cable nodes still dominate the market.
Figure 2. 2 Market composition
460 200 100 100 50 90 Geospace Unite Autoseis Zland Hawk Others
Figure 2.2 represents the share of wireless channels vis-à-vis cable nodes. Although 1000 wireless nodes were sold in 2016, cable channels are still rendered to be
successful method for onshore seismic survey since for 3 consecutive years 3500 cable channels were sold in the market.
Figure 2. 3 Estimate – Installed base of equipment (channels)
163 293 485 731 969 993 1000 2800 3000 3200 3500 3500 3500 3500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 2010 2011 2012 2013 2014 2015 2016 Cabled 1) Wireless 0 500 1000 1500 2000 2500 3000 3500 4000
ME Russia US South Am EUR AfricaRest of AsiaChina Total
Figure 2.3 represents the estimated number of both types of installed base of equipment over the world. In the Middle East, wireless nodes are more popular while in China cable channels dominate the market. The data represents the seismic survey market in 2016.
Figure 2. 4 Purchasing criteria of nodes
I think it is very important to assess the purchasing criteria of wireless nodes. According to the information granted from Innoseis, customers are assessing the quality of the product from a number of dimensions (Figure 2.4). As it was depicted on the Figure 2.4, there are 9 criteria over which the companies in the market are competing with each other. It seems that price comes first, as the customers are committed to cost reduction activities. Main customers of wireless nodes are BP, Shell, and Total.
3. Case description
Innoseis, for which the research project was developed, is a spinout company from Nikhef, the Dutch institute for sub-atomic physics. In partnership with Shell, Innoseis has developed the modern seismic sensing technology Tremornet to increase the efficiency of on-shore exploration of oil and gas. Given the long-range decreasing oil prices and growing population in the world, the project is designed to meet cost efficiency demand of market as the new technology is designed to bring down exploration cost considerably. Cost reduction effect of the new technology comes
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25% Wirelesswith the quality of data being collected by wireless nodes based on which oil and gas companies do not only conduct effective search for energy resources but also can monitor the existing fields to improve production and increase yield.
Figure 3. 1 Product specification of Innoseis
Figure 3.1 reflects the product specification of Innoseis along with the nine dimensions, which were presented in the previous subchapter. As we can see from the Figure 3.1, Innoseis performs well in the first five dimensions – price, data quality, Health-Safety-Environment (HSE), survey productivity and reliability, whereas the company is lagging behind the competitors in the dimension of harvesting.
Innoseis produces wireless nodes, which are used to create graphic representations of the earth's subsurface geologic structure. This graphic map allows exploration companies to accurately evaluate an area where they can conduct cost-effective exploitation of oil and gas by setting intense networks of wireless nodes. These networks of sensors stick to the surface of the investigation area and capture data from the ground below that is subsequently processed into the visual representation of the ground. These descriptions are then analyzed by experts to see where the pockets of oil or gas could be located, increasing the success rate and effectiveness of subsequent and expensive test drilling.
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Pr ic e Da ta qu al ity HSE Sur ve y pr odu ctiv i ty Re lia bil ity H ar ve stin g Ba tte ry lif e Si ze & w ei gh t Us e fle xib ili tyInnosies is hunting for an investor to finance €4M to upgrade the production process over two years. According to the representatives of the Company, €1M of this fund will be used for securing critical long lead-time components as result of which the orders are expected to be fulfilled on time. Further product development will attract €1.5M of this fund while the remaining fund will be used for strengthening global sales and marketing. According to the strategic plan of the company, Innoseis has targeted 1% market share of cable-less sensor sales in 2017, with a 24% market share by 2020. This strategy promises profitability in 2018 with revenues of over €100M in 2020 with an EBITDA of 52%+.
Figure 3. 2 Market share of Innoseis
Figure 3.2 reflects the current and perspective market share of Innoseis for 2020. The company expects to enlarge its market share by 60% by 2020 by attracting large amount of investment from different sources.
The market of wireless seismic surveys is segmented according to five dimensions such as cost, quality, simplicity of technology, difficulty of technology, and size (Figure 3.3) 0% 20% 40% 60% 80% 100% 2016 2022
Cabled Wireless
Figure 3. 3 Market segmentation
Figure 3.3 evaluates nodes, based on two dimensions: its usage on different terrains and its system capabilities such as quality of produced information and the cost of operation. Innoseis seems to produce nodes to address both nature-based challenges such as foothill, jungle and man-made issues like fences, constructions and ownership. It is interesting to see the position of traditional cable system, which scores higher on quality, and easiness, but the wireless nodes companies turn out to produce lower quality data. Having said that, the Figure 3.3 also shows how the traditional cable system is costly, which paves a solid ground for innovative wireless nodes.
As it was previously noted, the company intends to increase and upgrade its production process by attracting considerable level of investment from a number of sources. The volatility of energy prices casts a dense shadow over the optimistic forecast of the company. In this thesis project the future cash flow of the company will be analyzed in light of fluctuation of energy price.
4. Research questions
How volatile energy market affects the value of the energy related start-ups in light of higher risk and uncertainty?
5. Literature review
5.1. Energy price volatility
The negative impact of energy prices catches attention of Stephen Leeb and Donna Leeb (2004) who dedicated their book “The Oil Factor: Protect Yourself and Profit from the Coming Energy Crisis” to the impact of oil price volatility on investment opportunities. According to Stephen Leeb, U.S. economy is highly dependent on oil and the shortage of oil production increases the vulnerability in U.S. economy and makes financial and economic crisis inextricable part of the economy. That is why Leeb believes that US economy should decrease its dependency on oil resources over the long-turn. Coming to the investment decisions, Leeb recommended that investors should consider purchasing those stocks that have long and stable relations with oil prices. His recommendation based on 30-year statistical observation of oil prices and stock market. In their analysis, the authors analyzed the performance of different companies operating in diverse industries over 30 years and identified the industries that display weak sensitivity to the fluctuation of oil prices.
Fossil fuels do not come without environmental concerns and impediments. The environmental effect of the fossil fuels has been a hot topic for empirical research studies. According to Hester et al. (2003), the environment gets affected throughout different processes of energy production. Refining petroleum by transforming crude oil into final product imposes lasting damage to our environment. Moreover, the environment is very sensitive to greenhouse gases, which is released from burning gasoline (Liu et al., 2007). The last but not least source of the environmental pollution comes from oil spill occurring during different stage of the oil production. According to Smith et al. (2010), the damage of oil spills costs billions of dollars to the company responsible for such massive amount of release. In 2010, the famous Deepwater Horizon case costed BP $36.9 billion as the rig sank in the Gulf of Mexico after
explosion (Smith et al., 2010). It seems this is one of the main reasons why countries try to decrease their dependency on fossil fuels.
Tilak Abeysinghe (2001), a professor in the Department of Economics at the National University of Singapore, has slightly different approach regarding the impact of oil price on large economies. From his point of view, oil price does not offer any real threat to the economy of countries like the U.S. while the economy of small countries such as Thailand falls sharply under the impact of higher oil price. This was the conclusion of his article in which he analyzed the impact of oil price on economic growth in 12 countries. The analysis included oil export countries such as Indonesia and Malaysia where the impact of higher oil price was positive.
According to Fournier et al. (2013), oil prices had a sharp drop during 2008’s financial crisis since when crude oil price have been generally stable for years. The authors believe that demand and supply are the main factors driving the price of oil. Based on the assumption that economic growth of OECD and non-OECD countries would return to its pre-crisis level as a result of which the demand for oil would increase along with the price of Brent crude oil by 2020. This upward slope of oil price will be interrupted by the sudden changes that may occur in both demand and supply sides, which will exert significant effects on oil price in the short run. It has to be mentioned that the analysis presented by Fournier et al., failed to consider the impact of political intercourse among buyers and sellers on crude oil price as the price has declined twice since 2015.
Bassam Fattouh (2015), the Director of Oxford Institute for Energy Studies, run parallel between different oil crises that emerged in different period of time. According to Bassam, every oil price cycle differs with its special features and the end of this oil price cycle that has started since 2015 shares the same elements. These elements are the advent of the US shale revolution, the gradual shift from oil production and its export, the entry of new set of players with a new business model, the problem of excess suppliers, rising level of inventory, the overinvestment question, the relationship between OPEC and non-OPEC countries, the fundamental trade-off between maximizing revenues and maintaining market share and the dynamic of geopolitical sphere.
Despite the fact that countries try hard to lessen their dependency on fossil fuels by introducing renewable energies, the European countries are far from reaching success. In response to the Russian annexation of Crimea, the European Union launched the EU Energy Security Strategy in May 2014 aiming at mitigating the possible future risks may come from Russia (Summerton, 2016). In 2015, the EU introduced Energy Union strategy designed to improve energy efficiency by at least 27% by 2030, to decarbonise the economy by achieving 40% emission reduction by 2040 and to become a main driver in clean energy technologies (Ibid: 10).
Figure 5. 1 Primary production and net imports of crude oil in the EU
(2000-2015)
However, the Figure 5.1 presents the current level of dependency of the EU on oil imports. According to the Figure, the oil dependency of the EU was estimated by 88% for 2014. Furthermore, EU tries very hard to lessen its dependency on fossil fuel by adapting a number of documents such as EU Energy Security Strategy. The main aim of this strategy is to weaken the EU energy dependency mainly from Russia (Energy Security Strategy, 2014). Although the data does not reflect the current situation, to see the results of the adapted strategy, the EU needs a couple of more years.
The fluctuation of oil prices affects the economic performance of companies in different industries. According to Gogineni (2010), daily oil price changes do not impact only oil related industries but also those industries that are less dependent on
oil. In his analysis, the author identified two possible effects: cost side effect and demand side effect. As the oil price increases, it increases the cost of production of the company. At the same time, higher oil price has a negative effect on demand as the scale of demand shrinks. Lee and Ni (2002), argue that the effect of oil price shock comes from either demand or supply side depending on different industries. Oil price shock reduces supply of the industry that is highly depended on oil such as oil refinery and industrial chemicals whereas for many other industries, oil price shocks have a negative effect on demand. Automobile industry serves as an example for this category.
It has to be mentioned that a higher oil price does not always lead to economic crisis as it was highlighted by Tilak Abeysinghe. Oil exporting countries reap the benefit of higher oil prices as they earn much more money while oil importing countries carry the burden of the increasing energy prices. For this research, higher energy prices rendered promising since the company Innoseis is highly dependent on the economic performance of oil exploiting companies such as BP, Shell and etc. Once energy loses its value in the energy market, these companies seem unlikely to consider any investment opportunities to increase the volume of production or upgrade the production process in the coming future.
5.2. Valuation
According to the mainstream financial theories, the value of a firm is the present value of its future cash flow (Brealey, Myers, and Allen, 2007). It is worth mentioning that this simplest approach to the valuation of a company offers challenges for the valuation of a new venture as the company cannot provide necessary accounting and financial information required for the valuation process (Milloud et al, 2012). The most critical part of entrepreneurial finance is the valuation of a new venture with the lack of financial records. The valuation of a firm goes through very complex processes since it is not limited to the pure financial considerations of balance sheet, income statement and the financial forecast. Tyebjee and Bruno (1984) took the issue even further by saying that the venture capital investment is a well-defined staged process that starts from the origination of a deal and goes through the ending at the exit of investment. That is why Tyebjee and Bruno
(1984) found the valuation of an entrepreneurial firm as the most challenging part of this process, in which the interest of founders of a new venture and that of potential investors always clashes.
To address this challenge, three professors of Chambery Business School of France Tarek Milloud, Arild Aspelund and Mathieu Cabrol (2012) developed an integrated theoretical framework to assess the importance of the factors identified by strategy theorists for the valuation of new ventures. These factors include the degree of product differentiation of an industry, the growth rate of an industry, the industry, startup and top management experience of the founder of a new venture, higher team spirit in a new venture, complete management team, and network size of a new venture.
Joseph Bell (2014) argues that the enterprise value stems from a pre-money valuation, an investment amount, and a post money valuation. He summarizes pre-money valuation as the value of a company immediately after the investment, which has not yielded to any result whereas the investment is the actual cash invested at that particular time. The author proposes the following formula to calculate the post-money valuation:
Pre-money valuation + Investment amount = Post-money valuation
Based on the equation, we can conclude that the post money valuation is the sum of pre-money valuation and the amount of investment. Bell (2014) describes a very important point in his analysis. Given the fact that new ventures cannot be valued with the standard methods that are applicable only for traditional companies, the valuation of a developing companies assign an appropriate ownership percentage for the invested amount, which can be calculated as following:
Investment amount/Post money valuation = Investor ownership percentage According to Bell (2014), there is a negative correlation between the investment ownership percentage and the founder ownership percentage. The greater the investor ownership percentage, the lower the founder ownership.
Davila et al. (2003) investigates the impact of venture capital funding on the growth of investment. In their analysis, employee growth served as a dependent variable
representing the growth rate of startups. Based on the signaling theory, which is based on the assumption that one party or agent conveys a message to the other party, the authors measured the employee growth rate in startups and found out that the employee rate increases one month prior the funding and one month the funding. Due to the fact that the information about the financial situation of startups cannot be easily obtained, employee growth rate may reflect the development of the startup and may draw clear picture about its ambiguous financial situation. That is why, from the authors’ point of view, venture capital events are very important source of signal of growth for startups.
Melanie et al. (2013) argue that despite the fact that many startups may not possess any tangible asset at the initial stage, these new ventures worth as much as the market pays. On the other hand, the value of the startups depends how much investment it carries. This initial stage is very important for the future of startups. At this stage, the main challenge of the owner of the startup is to set an appealing price for his/her venture in order to be able to attract the interest of potential investors. To address this challenge, Melanie et al. (2013) recommends finding out market comparable, which will help you to conclude better deal with potential investors. Market comparable are widely used in valuation. Venture capitalist and the owner of a venture may find similar companies in the same industry and compare the financial indicators such as ROI and EPS. Once the similarities are detected, the price of that comparable company may serve as a benchmark price for the startup. Furthermore, according to the authors, the number of potential investors should not be limited as the higher the number of investors is the higher the chances are.
6. Theoretical framework
The project is based on a number of theories such as supply-demand, free cash flow, scenario analysis, monetary policy and oil market, and exploitation vs. exploration each of which will be analyzed based on their assumptions and main variables. Given the fact that the project consists of different topics, namely oil market analysis, valuation of a company, and recommendation for the company in question, each chapter is constructed on the bases of the assumptions of abovementioned theories. In more details, the volatility in the energy market has been introduced with the theories
– supply and demand, monetary policy and oil market whereas the valuation of Innoseis has been presented with the free cash flow model. Scenario analysis and exploration and exploitation serve as theoretical background for the last topic of this project, which is recommendation.
6.1. Supple - demand analysis and the volatility in the energy market To analyze the fluctuation of the energy prices, it is important to monitor the trend of energy prices over, at least, 10 years, with the understanding of important variables affecting the price volatility (Table 6.1).
Table 6. 1 Energy market volatility
Year Price Xi-Mean SD
1998 12.05 -43.63263158 1903.806539 1999 25.6 -30.08263158 904.9647227 2000 26.8 -28.88263158 834.2064069 2001 19.84 -35.84263158 1284.694239 2002 31.2 -24.48263158 599.399249 2003 32.52 -23.16263158 536.5075017 2004 43.45 -12.23263158 149.6372753 2005 61.04 5.357368421 28.7013964 2006 61.05 5.367368421 28.80864377 2007 95.98 40.29736842 1623.877902 2008 44.6 -11.08263158 122.8247227 2009 79.36 23.67736842 560.6177753 2010 91.38 35.69736842 1274.302112 2011 98.83 43.14736842 1861.695402 2012 91.82 36.13736842 1305.909396 2013 98.42 42.73736842 1826.48266 2014 53.27 -2.412631579 5.820791136 2015 37.04 -18.64263158 347.5477122 2016 53.72 -1.962631579 3.851922715
Table …, described the volatility in energy market, which is described by the standard deviation. The deviation explains the risk associated with the oil price fluctuation. We can assume that highly volatile market may promise extreme risk for potential investors, and thus, the interest rate in the oil related industry will be higher. To make the picture much clearer, the graphical representation of the table is presented below. Figure 6. 1 Energy market volatility
Figure 6.1 shows the volatility in energy market. As we can see from the Figure 6.1, the fluctuation of energy prices was large with radical changes from year to year. According to the Figure 6.1, the energy price declined radically in 2008 since when the price of fossil fuels seemed to have upward slope till 2013. The oil price was subject to considerable fluctuation over nine years as the price reached its peak of $130/barrel in 2007 and declined sharply one year after to $45/barrel in 2009. From 2011 to 2014 the oil price experienced minor fluctuation with the average over $75/barrel. The last phase of the oil price, which has been marked by the lower price ever since 1999 started at the end of 2014 and has led the price to decline below $50/barrel. These fluctuations raise relevant question about the reasons.
6.2. Supply-demand equilibrium
Supply and demand model is considered the bedrock of the market economy, where the price of goods and services is produced at the equilibrium point of demand and supply (Humphrey, 1992). Demand reflects the quantity of a production desired by buyers whereas supply represents the other side of the coin, how much the market can offer to buyers. These two variables meet at some point where the price of a good and service evolves. Furthermore, the behavior of both variables is well explained by the law of demand and the law of supply (Ibid, 1992).
From Alfred Marshall’s perspective (Marshall et al., 1961), a famous English economist, according to the law of demand, in light of all other factors remain equal, higher price will lead to lower demand for that good and service. The law indicates that higher price increases the opportunity cost for that particular good and service,
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and thus, people would avoid buying it. On the other hand, in contrast to the law of demand, the slope upward, indicating that higher price will motivate producers to produce more to increase their profit. These two variables meet at the equilibrium point at a certain price level (Ibid, 1961).
Despite the fact that the supply-demand model backs to nineteen centuries, it has preserved its validity and has been widely applied to the explanation of different phenomena. The basic explanation of this model lies in the equilibrium point in which demand equals to supply, which is mainly attuned by corresponding price level. The model consists of three variables: supply, demand and price each of which requires additional explanation.
Supply side
The supply side of the equilibrium is well presented by LOPEX model (Long-term Oil Price and EXtraction), which was introduced by Rainer Friedrich, a professor of the University of Stuttgart and Rehrl Tobias, a professor of The University of Applied Forest Sciences Rottenburg (2006). According to the model, it is possible to generate long-term scenarios about future world oil supply and corresponding price up to the year 2100. Despite the fact that the model concentrates on the oil production of OPEC countries, it also includes the effect of the oil production in non-OPEC countries, using Hubert curve. Hubert curve considers the discovery process as a process with logistic growth nature and presents the constraints associated with temporal availability of oil reserves (Hubert, 1956). In other words, according to Hubert, the oil production process cannot be continued forever and the production should have its peak after which exploitation of oil resources will decline. The analysis is based on the historical information on production the investigation of which allows estimating the future reserves of oil. It should be mentioned that this is one-side explanation of the oil production ignoring the impact of demand side effect, which may come with countries’ endeavor to lessen their dependence on oil, and increasing renewable energy supplies.
This model consists of three main elements:
2. Reaching to “Hubbert peak”, which represents the maximum capacity of the production process,
3. First gradual and then steep decline from the peak.
Figure 6. 2 Hubbert's peak for US crude oil production (1956)
It is worth mentioning that Hubbert’s curve was designed to predict the oil production of US where the historical data on oil production across the country was available. Figure 6.2 demonstrates the oil production in the US soil for 150 years with oil peak production in 1970. To find the cumulative production at its peak level:
𝑄𝑄(𝑡𝑡) =
𝑄𝑄
𝑚𝑚𝑚𝑚𝑚𝑚2
where Q(t) is the cumulative production, Qmax is total resources available in a region. Basically, we can calculate the Hubbert’s peak by dividing the world oil reserves by 2. According to the US Energy Information Administration (2016), the cumulative oil production from 1980 in the world is 2,73 billion barrels whereas the world oil reserves are estimated at 1,5 trillion barrels.
𝑄𝑄(𝑡𝑡) =
1,5 𝑡𝑡𝑡𝑡𝑡𝑡
2
= 745,6 𝑏𝑏𝑡𝑡𝑏𝑏/𝑏𝑏
Based on the equation, discounting the effect of renewable energies, we can argue that the world oil production has not reached its peak as the cumulative production is far below the peak level.
According to Uhler (1976), the oil discovery process is affected by available information and depletion effect, which form the logistics growth feature of oil discovery. On the other hand, Hubbert’s curve is based on the self-regulative cost dynamics by neglecting the effect of profitability (Rainer & Rehrl, 2006). This was one of the main reasons behind the introduction of LOPEX model in which the profitability effect is considered. Rainer and Rehrl point out that profitability creates multiple cycle of production process as profitability influences discovery and production. In other words, even though the territory is abundant with oil resources, profitability encourages different stakeholders to engage costly process of oil extraction. Furthermore, the model incorporated three interrelated variables such as production, demand and price path:
𝐷𝐷(𝑡𝑡) = 𝑑𝑑_𝑡𝑡𝑟𝑟𝑟𝑟(𝑡𝑡) �
𝑝𝑝_𝑡𝑡𝑟𝑟𝑟𝑟(𝑡𝑡)�
𝑃𝑃(𝑡𝑡)
𝜀𝜀where D (t) stands for demand, P(t) is price and d_ref(t) reflects demand path whereas p_ref(t) is price path. The power ε stands for price elasticity as the model is designed for estimating long-term price level. Based on Rainer & Rehrl’s regression analysis of the world oil demand, the value of ε is equal:
𝜀𝜀 = 1 − 𝜆𝜆 = −0.458𝛼𝛼
According to the equation, the demand depends on the long-term price level, which is subject to fluctuation. The interesting part of this model lies in its ability to predict the future price level of oil by concentrating on supply. That is the reason why it is called supply model. Given the fact that demand of oil is represented with the data on world
oil consumption in the previous year, it affects together with the oil price in the current year the world oil consumption:
ln 𝑤𝑤𝑤𝑤𝑤𝑤 (𝑡𝑡) = 𝑤𝑤 + 𝑎𝑎 ln 𝑝𝑝(𝑡𝑡) + 𝜆𝜆 ln 𝑤𝑤𝑤𝑤𝑤𝑤(𝑡𝑡 − 1) + 𝓊𝓊(𝑡𝑡)
where woc (t) represents the world oil consumption in year (t), p(t) stands for the oil price in the current year and woc(t-1) represents the world oil consumption in the previous year. C is constant, a is the short-term price elasticity, λ is the lag parameter, which is a prediction of current values of a dependent variable stemming from the current values of an explanatory variable and its lagged values (Cromwell, 1994). The estimated error is presented by u(t). If we plug the data of Rainer & Rehrl’s analysis to our model, the following scenarios of the world oil price will be produced (Figure 6.3).
Figure 6. 3LOPEX oil price scenario at different discount rates
Figure 6.3 presents different price path till the year 2100 with different discount rates. It is very difficult to forecast the discount rate for the long period of time. This is the main reason why the analysis concentrates on the future price rates produced by the LOPEX model based on different resource base (Figure 6.4).
Figure 6.4 represents the oil price path based on different resource base. According to Rainer and Rehrl (2006), there are four resource bases such as increased recovery (IR), No Shale, BGR (data source), and ASPO (data source). Increased recovery is based on the assumption that the increase of oil production may result from the recovery of oil resources. No Shale scenario excludes the possibility of the production of shale resources. BGR and ASPO are data sources from where the model borrowed data on oil reserves and production level. It is striking to see how the model reflects the current price level as the oil price between 2010 and 2020 was estimated at roughly $50/b.
Having said that, the model itself seems outdated as the interest rate and the level of technological development were not adequately presented and even the model is quite old to consider the possibility of Iranian penetration into the oil market as a result of lifting the sanctions imposed by US. For that reason, the analysis considers the updated version of LOPEX model, which was developed by a group of authors - A. Masoumzadeh, D. Möst, S. C. Ookouomi Noutch. In their analysis “Partial equilibrium modelling of world crude oil demand, supply and price (2017), the authors developed four scenarios – “cartel”, “oligopoly”, cartel” and “mixed-oligopoly”.
All these scenarios are distinguished based on different group of producers. “Oligopoly” consists of OPEC member states with the interest of maximizing their individual profits whereas “cartel” takes OPEC members as a group with the interest
0 100 200 300 400 500 600 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
of maximizing their joint profit. The next two scenarios are based on the first two scenarios. “Mixed-cartel” and “mixed-oligopoly” are based on the assumption that in these two scenarios oil producing countries make a strategic decision on the production quantities once they address their domestic demand as a cartel and as oligopoly players Masoumzadeh et al. (2017). The new model retains all elements of the LOPEX model including different effects such as technology, recovery level, demand and discount rate. The only difference between these two models lies in the fact that the new model is based on recent information about all abovementioned variables. These different scenarios produce different levels of oil production and price paths.
Figure 6. 5 Yearly global oil supply prediction for 2012-2035
Figure 6.5 presents diverse production levels based on different scenarios. This Figure shows wide gap between the production level of “oligopoly” and that of “cartel” scenario. The main reason behind this difference is that in “cartel” scenario, the producers are interested in decreasing the production level to increase the price whereas in “oligopoly” scenario, OPEC member states take active part in intense competition among each other over their market share as a result of which the level of
oil production rises. Chart 12 affects the oil price as the increasing production presumably exceeds demand and thus the price of oil decreases. It is worth mentioning that the model takes only the supply side and assumes that the price of oil is attuned mainly by production. On the other hand, the demand for oil is assumed to be stable over time and for that reason the impact of demand for oil on the determination of price level becomes limited, strengthening the position of oil producing countries.
Figure 6. 6 Yearly global oil price for 2015-2035 resulted from the model
Figure 6.6 shows the forecast of oil prices for twenty years. In different scenarios, the oil price fluctuates at different rate. The combination of Figure 6.5 and Figure 6.6 reveals significant correlation between production and oil price. In “cartel” scenario, as producers constrain the production, the price will increase above $100/b after 2020 whereas for “mix-cartel” and “mix-oligopoly” scenarios the price becomes flatted. Interestingly, the Chart 13 reflects the current situation in a way that the price of oil fluctuates around $50/b, which seems that the production operates under “mix-oligopoly” and “mix-cartel” scenarios.
Once the future price level of oil is available, it will be more reasonable to estimate the future revenue of the oil related companies such as Innoseis. Based on the Chart 13, the rest of the analysis is grounded on the four scenarios:
1. Oligopoly, 2. Cartel,
3. Mix-oligopoly, 4. Mix-monopoly.
Demand side
In contrast to the supply side of the equilibrium, the demand side offers skeptical views on the future of oil price. According to Christiane Baumeister and Lutz Kilian (2016), despite the fact that the dramatic decline in oil price in 2014 has been associated with positively oil supply shocks, the radical changes in oil price is proved to be attributed to the cumulative effect of adverse demand shocks stemming from slowing global economic development and positive oil supply shocks, and shocks to expected oil production in 2014.
The demand side has been seriously affected by the penetration of renewable energies such as solar, wind, wave and similar kinds of energy systems into fossil fuel production. From BP’s perspective (BP, 2016) renewable energy is growing at an average annual rate of 6.6%, and this trend continues to keep the same rate over the next two decades. As far as I know, renewable energy industry suffers from a number of challenges one of which is the lack of backup capacity that makes it difficult for governments to rely fully on these kinds of energy suppliers (EnerNex Corp, 2011). Backup capacity refers to the difficulties of storing renewable energy supply. According to Foster et al. (2017), enormous amount of resources has been invested to increase the backup capacity of renewable energy and the result of which has not been satisfactory.
On the other hand, investment in renewable energy proved to be more profitable than in fossil fuel production as a result of which government and private investors present
strong interest towards this industry. This inclination shapes the demand for fossil fuel, and thus, affects the price of oil (Figure 6.7).
Figure 6. 7 The impact of renewable energy on overall energy demand
Figure 6.7 based on the abstract consideration of the demand of fossil fuel for power generation that is constant and of the absence of renewable energy (Baumeister and Kilian, 2016). Hypothetically, if the demand for energy remains constant, the penetration of renewable energy to the energy market would decrease the demand for fossil fuels. On the other hand, introduction of renewable energy into the energy market may lead to the reduction of the price of fossil fuel as a result of which the demand for energy would increase.
However, there are contradicting information about the world future oil demand. According to OPEC reports (world oil outlook 2016), oil demand is forecast to increase up to 99.2 md/d by 2021 whereas in the long-term the demand will reach 109.4 md/b. The current oil demand is 93 md/b. Based on the OPEC report, global oil demand stems mainly from the road transportation sector, petrochemicals, and aviation. Even though many countries, mainly OECD members such as France, Norwey, Germany, the United Kingdom, the Netherlands have already set a target for banning sales of diesel and petrol cars by 2040 (some of them intends to realize the shift by 2025) and India by 2030, experts assume that this gap will be compensated by developing countries (Sofia News Agency, 2 July 2017)
Considering the demand-side and supply side effects, we can design three scenarios for future oil prices:
1. Best-case scenario. In this scenario, the oil price will increase 5% over the coming years. This corresponds to our supply-side effect;
2. Constant price level. I assume that the price will not change over the coming years, even though I could not find supporting literature;
3. Worst case scenario. In this scenario, we estimate 5% decrease of oil price. This comes from the demand side effect as the introduction of renewable energy dwindles the demand for oil, and thus, the price of oil will decrease.
Figure 6. 8 Future oil price scenarios
6.3. Valuation
Valuation is a set of techniques applied to estimate the value of a company. The reason why I used the word estimate rather than calculate is that it is more or less art work rather than pure mathematical calculation (Rogers, 2004). According to Koller et al. (2015), the value of a company is driven mostly by its return on invested capital and by its ability to grow both of which lead higher cash flows. As is mentioned in the literature review part, there are three types of valuation techniques each of which has its advantages and disadvantages. Free cash flow as one of these valuation techniques consists of a number of frameworks such as enterprise discounted, discounted economic profit, adjusted present value, capital cash flow, and equity cash flow (Exhibit 1). Depending on companies and the capital structure of different entrepreneurs, one of these models can be used to assess the value of a company.
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Table 6. 2 Frameworks for Discounted Free Cash Flow-Based Valuation Model Measure Discounted
factor Assessment Enterprise discounted cash flow Free Cash Flow Weighted average cost of capital
Works best for projects, business units, and companies that manage their capital structure to a target level
Discounted economic profit Economic profit Weighted average cost of capital
Explicitly highlights when a company creates value Adjusted present value Free Cash Flow Unlevered cost of equity
Highlights changing capital structure more easily than WACC-based models
Capital cash flow Free Cash Flow Unlevered cost of equity
Compresses free cash flow and the interest tax shield in one number, making it difficult to compare operating performance among companies and over time
Equity cash flow Cash flow to equity Levered cost of equity
Difficult to implement correctly because capital structure is embedded with the cash flow. Best used when valuing financial institutions
Source: McKinsey and Company, Koller, T., Goedhart, M., & Wessels, D. (2015).
Table 6.2 presents most available models for company valuation based on discounted free cash flow. Because of the fact that the company Innoseis has already managed its capital structure at a targeted level, the project the whole valuation process on discounted cash flow model. As was mentioned by Koller et at. (2015:105-106), the valuation of a company should follow the four-step setting:
1. “Value the company’s operations by discounting free cash flow at the weighted average cost of capital. To find the WACC, we have to find CAPM model based on which we can estimate the equity cost of capital. This model requires the data on risk free rate, beta, and market premium.
Risk free rate is calculated based on global average rate on government bonds which is 0.98% (www.bloomberg.com), whereas the beta of the seismic survey industry is 0.81 (www.finance.yahoo.com)1. The expected market return is 10.3%. which is obtained using S&P compounded returns from 1926 to 2006 (Hartman, n.d.).
So,
𝑡𝑡
𝑒𝑒= 0.98 + 0.81(10.3 − 0.98)
Based on the formula,𝑡𝑡
𝑒𝑒 equals to 8.53%.Given the fact that the capital structure of Innoseis includes debt financing, the Weighted Average Cost of Capital may represent better the return rate.
𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 =
𝐸𝐸 + 𝐷𝐷 𝑡𝑡
𝐸𝐸
𝑒𝑒+
𝐸𝐸 + 𝐷𝐷 𝑡𝑡
𝐷𝐷
𝑑𝑑× (1 − 𝑡𝑡)
If we plug the data of Innoseis into the formula,
𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 =
185 0.0853 +
85
100
185 0.06 × (1 − 0.25)
Thus, the WACC equals to 6.35%.It has to be mentioned that WACC also suffers from a number of shortcomings. According to Krüger et al. (2015), the main component of WACC – the cost of equity – is not constant, as many expert report it differently.
All data that were plugged into the formula has been taken from the financial statement of Innoseis.
2. Identify and value nonoperation assets, such as excess marketable securities, nonconsolidated subsidiaries, and other equity investments. Summing the value of operations and nonoperation assets gives enterprise value.
1
The beta of Schlumberger Limited is used to illustrate the beta of the industry. https://finance.yahoo.com/quote/SLB?p=SLB
3. Identify and value all debt and other non-equity claims against the enterprise value. Debt and other non-equity claims include fixed-rate and floating-rate debt, unfunded pensions liabilities, employee options, and preferred stock.
4. Subtract the value of non-equity financial claims from enterprise value to determine the value of common equity. To estimate price per share, divide equity value by the number of current shares outstanding”.
Once the free cash flow of the company is identified for five consecutive years, the present value model is applied the identify the present value of future cash flow.
6.4. Exploitation vs. Exploration (March, 1991)
According to the theory of rational search, which was intensely discussed by Radner and Rothschild (1975), most companies are facing with a number of alternative investment opportunities with a different level of probability distribution over initially unknown returns. These alternatives lead to a trade-off between the investment in future projects by allocating part of the investment to diversification of the investment or upgrading the current production (March, 1991). These choices are not always easy to make due to unstable probability distribution or dependency on others’ choices. From Cyert and March’s perspective (1963), the choice between exploration, which includes activities associated with search, risk taking, experimentation, discovery and innovation and exploitation, which considers refinement, efficiency and improvement of current production strategy depends on the intentions and strategic vision of the top management of a company who has to make the final decision on resource allocation.
The main assumption of this model can be summarized as following: the most preferred alternative, which is above the well-defined target prevent further search as the company enjoys increasing return from its current production strategy. On the other hand, if the current policy is not adequate and does not produce any margin for the company, the company becomes highly motivated to allocate resources for investigating the possibilities of other alternatives. İn other words, the performance of the company serves as a driving force behind either of both options: exploitation or exploration. That is the main reason why the recommendation part of the analysis,
which is based on the exploration vs. exploitation model is grounded on the assumption that the company should explore if its profit dwindles and exploit if the company gets higher margin under different scenarios.
7. Price sensitivity
The current value of Innoseis is determined by the future revenue of the company. Taking into account the fact that the company is a start-up with the lack of historical data regarding the company’s performance, it is challenging to predict its future revenue. Having said that, it is possible to set a benchmark company based on which the future cash flow of Innoseis can be predicted. It is worth mentioning that historical data cannot be simply taken and included to the analysis. The historical data should serve as an investigatory input that we can use to understand the trend in the performance of a company. In other words, the historical data can enlighten us about the reasons behind the variation of revenue of a company. Our first mission is to find a related company with the historical data, hinge on which we can predict the future cash flow of Innoseis. According to the information provided by Innosies, Geospace Company is a strong competitor of Innoseis (Figure 7.1).
Figure 7. 1 Market share of the companies
Figure 7.1 shows the market share of different companies where Geospace holds 46% percent of the market. Given its market share, we can use the historical data of Geospace to analyze the price sensitivity of the market. For that reason, I run regression analysis to find how the oil price affects the performance of Geospace based on which it will be possible to predict the future cash flow of Innoseis.
46% 20% 10% 10% 5% 9%
Figure 7. 2 Market volatility and revenue of Geospoce
Figure 7.2 shows the 18-year fluctuation in oil price and its effect on Geospace’s revenue. Between 1998 and 2007, till the beginning of world financial crisis, oil price had an upward slope with slight fluctuation whereas in 2008, the price experienced sharp decline. This drop reflects the sudden downturn of the world economy. Between 2009 and 2013, the oil producing countries enjoyed increasing oil prices and the world economy almost accomplished its recovery stage. The last reduction of oil prices, which has been noticed since 2013 is not connected with economic crises. The current decline of oil prices is partly explained by Trump’s political and economic decisions, which include building more pipelines like Keystone XL and making available more federal lands and Deepwater prospects for oil exploitation (Krauss, 2017, June 14). On the other hand, experts believe that the lifting of sanctions against Iran has caused the injection of additional hundreds of thousands of fewer barrels a day on the world market.
Comparison of oil prices with the revenue of Geospace unfolds the correlation between these two variables as the increasing oil prices promise higher revenue for the companies operate in this industry. Figure 7.2 demonstrates the fluctuation of the revenues of Geospace between 1998 and 2016. According to the Figure 7.2, the revenue increased in 2007 when the oil price reached the peak of its cycle. Moreover, the decline of oil prices from $95.98 to $44.6 in 2008 led to reduction of company’s revenue, as the revenue declined from $27,162,933 to $22,702,000. The recovery period of oil prices is reflected in the increasing revenue of the company, since from 2009 to 2014 the company enjoyed the expansion of its market as a result of which its gross revenue increased from $22,702,000 to $45,266,000. The same level of
0 10.000.000 20.000.000 30.000.000 40.000.000 50.000.000 60.000.000 0 20 40 60 80 100 120 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Price Revenue
connection between oil price and earning can be noticed between 2004 and 2016 since when the oil price has decreased along with the earning of the company.
To measure the correlation statistically, the relations between two variables is investigated with regression analysis. The regression analysis covers 18 years of historical data of oil price and the revenue of Geospace. The data about Geospsace is collected from the website of the company whereas the information about the oil price fluctuation is taken from www.bloomberg.com.
Table 7. 1 Summary output of regression analysis
Coefficients Standard Error t Stat P-value
Intercept 775124.311 51.9115341 1.493163945 0.154 X Variable 1 28324.2779 0.83117012 3.407759406 0.003
Table 7.1 presents the result of regression analysis. Based on the information, taking into account the P-value, which is far below the required 0.03 level, we can argue that oil price affects the revenue level of oil related companies.
Table 7. 2 Regression statistics
Multiple R 0.6370
R Square 0.40586
Adjusted R Square 0.37091
Standard Error 10237.3
Observations 19
Table 7.2 shows the level of impact of oil price on the revenue of the seismic survey companies. According to the Table 2, the impact of oil price on the performance of Geospace is 37.09 %, which is not very high. Despite the higher correlation between both variables (Figure 7.2), the impact of oil price on the revenue of an oil related company seems to be limited. These two tables lead to the following equation:
Revenue =
775124.311+28324.2779
*oil price
This equation makes it difficult to predict the future cash flow of startups such as Innoseis and establish the value of these companies accordingly.
The value of a company can be determined by a number of ways such as multiples, asset valuation and capitalization of cash flows (Rogers & Makonnen, 2009). As it was mentioned earlier, this section is grounded on the third method, which is capitalization of cash flows. It should be mentioned that each method has its advantage and disadvantage depending on a number of factors. On the other hand, the true value of a company depends on the conclusion of intense negotiation between buyers and sellers.
As was mentioned, the company Innoseis does not possess any historical data on financial statement, based on which we can estimate its performance indicators. To address this issue, different scenarios of future oil price and price sensitivity of the seismic survey market serve as a ground for the estimation of the future cash flow of Innoseis. To forecast the free cash flow of the company within all scenarios, the current performance of the company should be analyzed based on the financial indicators from 2016
Table 8. 1 Financial statement of Innoseis
Income statement 2016 Sales Product sales 551,500 Other income 50,000 Total sales 601,500 Operating expenses
Cost of products sold (266,093)
Depreciation (3,000)
Selling, general, and administrative expenses (315,264)
Interest expense (6,000)
Interest income 5,430
Earnings before income taxes 16,573
Provision for income taxes (6,845)
Profit after taxes 9,728
Table 8.1 shows the income statement of Innoseis, which is one of the most important indicators of the performance of the company. According to the Table 8.1, at the current price level ($49 = €41.71) the total sales for 2016 was 601,500, which serve as
the base for future analysis of the free cash flow of the company. In other words, for the next five years from 2017, €41.71 is a departure point from which the change of oil price is calculated.
Revenue of Innoseis =
775124.311+28324.2779
*∆ in oil price
Given the fact that the oil price in 2016 was around €41.71, the formula notices no change in oil price and that is why it takes the oil price effect 0.
The change of oil price is calculated based on the following formula:
∆Oil price = 1+ (new oil price – 41.71)/41.71
Thus, revenue of Innoseis for 2016 is:Revenue of Innoseis=775124.311+28324.2779*1= €803,448.589
We estimate that the oil price for 2016 did not change, and that is why we take it as 1. Based on the income statement and balance sheet, the free cash flow of the company for 2016 was as following (Table 8.2):
Table 8. 2 Free Cash Flow of Innoseis (thousand euro)
2016
EBITDA 21,206
Subtract interest income, net of taxes (3,187)
Subtract increase in current assets (84,000)
Add back increase in current liabilities 124,860 Subtract increase in property and equipment at cost (10,583)
Free cash flow 48,296
The information depicted in the Table 8.2 was provided by Innoseis. As we can see from the Table 8.2, the free cash flow of the company for 2016 was €48,296. This is the reference point for the analysis of the future cash flow of the company for different scenarios. In other words, the impact of future oil price is analyzed based on the free cash flow of the company for 2016.
8.1. First Scenario: Worst case scenario
Before the analysis, it is worth mentioning that for all scenarios, all costs (except the cost of goods sold) are taken as fixed. Furthermore, I estimate that the revenue of the company will grow constantly at 3 percent Based on Masoumzadeh’s analysis, the Table 8.1.1 shows the different price levels of crude oil with the estimated free cash flow of Innosies.
Table 8.1. 1 Future cash flow of Innoseis in worst case scenario Year Price of oil
(euro/b)
D Oil Price Revenue Net Profit FCF
2018 41.71 1 803448.5889 491536.5889 530104.5889
2019 39.6245 0.95 802032.375 490120.375 528688.375 2020 37.643275 0.9025 800686.9718 488774.9718 527342.9718 2021 35.76111125 0.857375 799408.8388 487496.8388 526064.8388 2022 33.97305569 0.81450625 798194.6124 486282.6124 524850.6124
The only thing we have to do is to plug the numbers into the Present Value (PV) formula that presented below:
𝑃𝑃𝑃𝑃 =
𝐶𝐶𝑡𝑡 (1+𝑟𝑟)𝑡𝑡 Thus,𝑃𝑃𝑃𝑃 =
(1 + 0.0635) +
530104.59
(1 + 0.0635)
528688.375
22+
(1 + 0.0635)
527342.972
33+
526064.8388
(1 + 0.0635)
44+
524850.6124
(1 + 0.0635)
55+
524850.6124 ∗ (1 + 0.03)
0.0635 − 0.03
= 10,491483.8
The formula tells us that the present value of the sum of future cash flows of Innoseis is €10,491483.8. In other words, in the first scenario, the formula produced the current value of the company.8.2. Second Scenario: Constant price level
In this scenario, we assume that the price of oil will stay unchanged over the coming years.
Table 8.2. 1 Future cash flow of Innoseis in constant price Year Price of
oil (euro/b)
D Oil Price Revenue Net Profit FCF
2018 41.71 1 803448.5889 491536.5889 530104.5889
2019 41.71 1 803448.5889 491536.5889 530104.5889
2020 41.71 1 803448.5889 491536.5889 530104.5889
2021 41.71 1 803448.5889 491536.5889 530104.5889
2022 41.71 1 803448.5889 491536.5889 530104.5889
Table 8.2.1 shows how constant price level affected the free cash flow of the company.
𝑃𝑃𝑃𝑃 =
530104.5889
(1 + 0.0635) +
530104.5889
(1 + 0.0635)
22+
530104.5889
(1 + 0.0635)
33+
530104.5889
(1 + 0.0635)
44+
530104.5889
(1 + 0.0635)
55+
530104.5889
0.0635 − 0.03
∗ (1 + 0.03)
= 10585039.6
As we can see, higher oil price significantly increased the value of the company from € 10,491483.8 to €10,585,039.6. The difference is 93,555.88. It means that 5% change in the oil price will cost the company to lose € 93,555.88 of its value.In this scenario, OPEC member states decide to produce oil adequately to market demand only after meeting their internal demand. The scenario assigns greater importance to internal demand rather than to common decision on production. According to Masoumzadeh et al. (2017), despite the fact that the OPEC member states enjoy great flexibility and freedom under the mix-oligopoly scenario, lack of control among member states leads to lower price level.
Table 8.3 1 Future cash flow under the best-case scenario Year Price of oil
(euro/b)
D Oil Price Revenue Net Profit FCF
2018 41.71 1 803448.5889 491536.5889 530104.5889 2019 43.7955 1.05 804864.8028 492952.8028 531520.8028 2020 45.985275 1.1025 806351.8274 494439.8274 533007.8274 2021 48.28453875 1.157625 807913.2032 496001.2032 534569.2032 2022 50.69876569 1.21550625 809552.6478 497640.6478 536208.6478 Thus,
𝑃𝑃𝑃𝑃 =
(1 +
530104.59
0.0635
) +
(1 +
531520.80
0.0635
2)
2+
(1 +
533007.83
0.0635
3)
3+
+
(1 + 0.0635)
534569.20
44+
(1 + 0.0635)
536208.65
55+
536208.65 ∗ (1 + 0.03)
0.0635 − 0.03
= €10,693,097.5
Under the best-case scenario, the value of the company is estimated at roughly €10,693,097.5, which is €108,057.8752 higher than the previous scenario.
Based on the supply side scenarios, which are oligopoly, cartel, mix-oligopoly and mix-cartel, the value of Innoseis is presented by Figure 8.3.1
Figure 8.3.1 is a graphical representation of the results of abovementioned calculation under supply-side scenarios. In “oligopoly” scenario, the OPEC member states get actively involved in intense competition over their market share and thus, this competition leads to higher oil price. Cartel scenario is based on the assumption that the OPEC member states become united and decide unilaterally on the production level that tailed to their common interest. Taking into account the fact that higher price is the common interest that is shared by all OPEC member states, lower production level paves a solid ground for the higher price. In the scenario of mix-oligopoly, the OPEC member states decide to produce oil adequately to market demand only after meeting their internal demand. This scenario assigns greater importance to internal demand rather than to common decision on production. Under mix-cartel scenario, the OPEC member states tend to utilise their own market power to increase their market share rather than to contribute to the common interest (Masoumzadeh et al., 2017).
As we can see, supply-side scenarios produce more diverse outputs regarding the value of the company. Bearing in mind the results of the above-mentioned calculations under different scenarios, the companies should closely follow the decision-making process of OPEC to predict the future possible price scenario of the final decision. In other words, close collaboration among OPEC members seems to produce higher oil price based on which start-ups may value their companies even if the historical financial records do not exist.
0 200000 400000 600000 800000 1000000 1200000 1400000 1600000
