OF UPSTREAM OIL AND GAS FIRMS T HE IMPACT OF VALUE DRIVERS ON DCF VALUATIONS

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T

HE IMPACT OF VALUE DRIVERS ON

DCF

VALUATIONS

OF UPSTREAM OIL AND GAS FIRMS

by

Benjamin Suichies

Wilson HTM Investment Group Level 11, 8 Exhibition Street, Melbourne VIC 3000 Australia

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T

HE IMPACT OF VALUE DRIVERS ON

DCF

VALUATIONS

OF UPSTREAM OIL AND GAS FIRMS

Benjamin Suichies

Student number: 1713604 Dopheide 28 8471 VG Wolvega +61 (0)420 396 720 s1713604@student.rug.nl

Supervisors University of Groningen:

Mr. Dr. W. Westerman (supervisor)

Mr. Prof. Dr. L. Karsten (referent)

Supervisor Wilson HTM:

Mr. J. Young (senior research analyst)

Abstract

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Preface

This thesis is written in the context of my MSc program International Business & Management (IB&M). Throughout my academic career, studies and courses mainly focused on theories and its relevance to the particular field of interest. As a result, the practical applicability of these theories was sometimes limited, which was one of the main reasons to apply for an internship. In this internship I wanted to combine the first two words ‘International’ and ‘Business’ from the program’s title, as I believe such an internship is a valuable experience and completion of the MSc IB&M.

Wilson HTM (Melbourne) gave me this opportunity, for which I’m very grateful. Therefore, I would like to thank Mr. J. Young in special for his help and guidance throughout the process and for providing me with an interesting and challenging experience, which was more than I could hope for. Moreover, I wish to pay special thanks to Mr. Dr. W. Westerman for a pleasant collaboration and the helpful advices especially when I was struggling to combine the theoretical viewpoint of the University with the more practical context of my research at Wilson HTM.

Furthermore, thanks to the colleagues of Wilson’s Research Department for their time and support during my internship, and for providing me with a congenial and interesting work experience. Also thanks to Mr. P. Rubens for arranging this internship.

Finally, a massive word of gratitude to my family, in particular my parents, for their mental and financial support throughout my study life.

Although my academic career has come to an end, I’m looking forward to finally put the skills I’ve learned into practice. However I realize that one always has to keep learning.

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Introduction

As one of the main pillars in financial literature, the valuation issue is under constant research and therefore keeps evolving. Various valuation concepts are available and there are even more financial models to apply these theories. In this context, as an Australia-based investment group, WHTM is also constantly reviewing and developing its valuation models and this research hopes to be a valuable contribution, by helping WHTM further unravel the ‘valuation puzzle’.

Research background

WHTM has observed that the share prices of ASX-listed mid-cap oil and gas firms have generally traded below their net present value (NPV) estimates. WHTM’s valuation estimates have been achieved on occasion, but less often than one would expect. In addition, WHTM believes its valuations and target prices have often been below those of its competitors. These potential valuation gaps between WHTM valuation estimates, actual market prices and market consensus (competitors) are merely WHTM’s presumptions and therefore they should be tested. This presumed gap between WHTM valuation and market price may be due to various value drivers, e.g. commodity price assumptions, discount rates, risk factors or to a structural gap between valuation practice and market behaviour. In sum, this research hopes to contribute by further improving WHTM’s overall valuation methodology.

Moreover, the majority of oil firm valuation based studies have used samples of large US and European oil firms. This thesis contributes to the field of valuation research by examining a sample of firms in a different region (i.e. Australia) and with a different firm size (i.e. mid-cap). In particular, the research objective is to quantify and explain the relationship between value of ASX-listed mid-cap oil and gas firms ascribed by the equity market and by WHTM’s valuation estimates, in order to identify opportunities to further improve the valuation methodology of WHTM. As the research applies a practical financial/economical approach it focuses on financial valuation issues and puts less emphasis on the technical facets of the upstream oil and gas industry. In order to achieve this research objective the following questions should be answered;

Main research question:

To what extent can the presumed valuation gap between WHTM’s valuation estimates and the actual share prices of ASX-listed mid-cap oil firms be attributed to the impact of WHTM’s value drivers?

Sub questions

- Sub question 1: Which specific characteristics/factors in the upstream oil and gas market impact the valuation of oil firms?

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- Sub question 3: To what extent do WHTM’s valuations differ from its competitors and the actual share price?

- Sub question 4: How can the presumed valuation gap be explained by the value drivers of the WHTM’s valuation model?

- Sub question 5: How can the presumed valuation gap be explained by a structural difference in value drivers between WHTM and other market parties?

- Sub question 6: In what respects is an adjustment of the WHTM’s DCF valuation methodology desirable, based on the outcome of the previous sub questions?

Although this research puts little emphasis on the technical traits of the upstream oil and gas industry, some technical base is required. The next section will therefore discuss several features of this industry.

The upstream oil and gas industry

Being the most important single commodity, oil is seen as a fundamental driver of (financial) markets and has often a significant impact on international economies. Oil and gas commodities (hereafter the term oil will be used to include oil and gas), sometimes labelled hydrocarbons, have a very specific nature and possess some unique characteristics e.g. in their geology, exploration and drilling methods. Oil is a term for a substance of deep-rooted hydrocarbon compounds, which contains varying amounts of organic materials. As oil is formed due to the decomposition of the organic remains, at elevated temperature and pressure, it can only be found in specific geologic formations. Natural gas has a similar geological process and is therefore often found in comparable deposits (Finsia, 2006). If a deposit consists of a large amount of natural gas, it is generally known as a natural gas field, and deposits rich in oil are called oil fields. Since no two oil/gas accumulations are identical, due to the independent geological processes, there are several classifications of crude oil, all with a different mixture of substances. For example, light sweet crude oil, which is the most preferred form of crude oil, contains a limited amount (< 0.5%) of sulphur and has a low proportion of heavy (long chain) hydrocarbons and is therefore easier to process.

The process of an upstream (exploration and production) oil firms tend to have five common stages. Since it goes beyond the scope of this research to fully discuss the different methods in each stage of an oil project, a simplification of the several phases is presented.

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determine a ‘lead’, a potential structure which may hold oil and/or gas. Finally if the lead has been fully evaluated by an oil firm it matures into a ‘prospect’.

§ Appraisal: The phase immediately after a successful exploration drill (prospect) is called the appraisal stage. During appraisal, the exploration phase is assessed in order to decide whether or not to proceed with the project and a final investment decision (develop or not) is made.

§ Development: After the appraisal, but before the production, a strategy is designed how to effectively and efficiently exploit the oil field, e.g. the construction and drilling of development/production wells.

§ Production: Once the development of the oil field is completed, oil wells start to drill up the oil. Since the production methodology heavily depends on the structure of the oil/gas field, there are numerous techniques to develop the resource.

§ Abandonment: The last stage in an oil project is abandonment, as every oil field has a finite amount of reserves, at some point there is insufficient oil/gas reserves to economically justify further production. The field may run out of reserves, the production rights (lease, contract etc.) expires or it becomes too expensive to drill up the (last) quantities in the deposit, making it economically unviable to keep producing and the oil/gas field is abandoned. Finally, this abandonment and restoration of the field can be a very expensive and complex process.

Hence, before the drilling takes place the value of the accumulation heavily depends on several estimates, e.g. potential (commercially) recoverable oil reserves, development costs and production rates. However as the project matures, risks (chance of failure) and uncertainties (range of outcomes) are significantly reduced, although an exact economic performance is not available until abandonment. These risks and uncertainties play a vital role in the industry as we will see in the remainder of this thesis.

Since oil reserves are the main assets of an oil firm, it is important to define how reserves are ranked and classified. In general there are three types of reserves/resources;

§ Reserves ‘are those quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions. Reserves must further satisfy four criteria: they must be discovered, recoverable, commercial and remaining based on the development project(s) applied’ (Young, 2007, p.1).

§ Contingent Resources ‘are those quantities of petroleum estimated to be potentially recoverable from known accumulations, but the applied project(s) are not yet mature enough for commercial development (e.g. no viable markets, recovery dependent on technology, evaluation at an early stage)’ (Young, 2007, p.1).

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Resources have both an associated chance of discovery and a chance of development’ (Young, 2007, p.1).

These three types of reserves/resources are further divided into the probability of producing a definite quantity of hydrocarbons. The most commonly used approach to quantify these probabilities is the SPE/WPC/AAPG/SPEE1 reserves classification system. Appendix A presents this thorough representation of this resource classification system. In sum, due to the specific characteristics of the upstream oil industry, there are more than a few uncertainties, complicating the valuation process of a firm in this sector.

The next chapters will build up as follows. First an overview of the literature is presented; this section will provide some theoretical background on the discounted cash flow valuation methodology and applies it to the oil industry as well as WHTM. Thereafter, the methodology of the research is presented in which the data collection, sample and methodology is outlined. Next, the results of the analysis are presented, emphasizing on the valuation gap and the value drivers within WHTM valuation methodology. Finally, the conclusions of the research are described, as are, the limitations, recommendations and suggestions for further research. Hence the structure of this thesis is arranged in a similar outline as the research questions.

1_{SPE/WPC/AAPG/SPEE, stands for Society of Petroleum Engineers/ World Petroleum Council/ American Association of}

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Literature Review

This literature review starts with a discussion of the Discounted Cash Flow (DCF) valuation technique. Subsequently, the impact of the oil industry on valuation issues is examined. Finally, a review of WHTM’s valuation methodology is presented. Hence, this chapter takes a commonly used valuation tool (DCF) and narrows it down, by applying and discussing it first on an industry level (oil) and thereafter firm level (WHTM).

DCF Valuation

Within the field of finance numerous valuation models are reviewed, Damodaran (2005) discerns four general approaches to valuation; the discounted cash flow valuation, the liquidation and accounting valuation, the relative valuation and finally the contingent claim valuation. However this section will emphasize on DCF valuation model, which is seen as the most essential tool in the field of valuation (Koller et al., 2010; Damodaran, 2005; Rappaport, 1986).

Having its origin in studies of compounding interest, the discounted cash flow method is seen as the most appropriate valuation technique. The essence of a DCF analysis is to calculate the present value of an asset/project/firm by discounting the expected cash flows using a discount rate that reflects the risks of the expected cash flows (Damodaran, 2006). Hence, the expected cash flows should be discounted for the time value of money and a risk premium. The basic DCF formula reads as follows (Damodaran, 2005):

∑

∞ =

+

=

1

(

1 )

t t t

r

CF

NPV

Where t designates the time period of the expected cash flow, CF stands for expected cash flow, and finally the r describes the discount rate. Generally, the Weighted Average Cost of Capital (WACC) is seen as the appropriate indication of the discount rate. As the net present value (NPV) is one of the main instruments in the DCF analysis, besides the Initial Rate of Return (IRR), it basically sums all of the discounted cash flows (PV) and subtracts the purchase price or initial investment. Hence, if the NPV turns out to be positive, the investment will add value.

According to Fernandez (2010) the term Weighted Average Cost of Capital is misleading as it is not actually a ‘cost’. In fact, this WACC is a weighted average of two different subjects, first the cost of debt and second the required return to equity (ke). Since ke is often referred to by cost of equity, this is where the misconception sets in as it is actually a required return. The formula of the WACC is (Koller et al., 2010):

(

m

)

d e

k

T

V

D

k

V

E

WACC



×

−











+

×













=

1

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of financing that is debt. Finally, Tm is the company’s marginal income tax rate. The primary tool in order to calculate this required return (ke) is the Capital Asset Pricing Model (CAPM) (Koller et al., 2010):

(

m f

)

f

e

r

k

=

+

β

−

Where ke is the required return (cost of equity), rf is the risk free rate. β stands for the Beta or market sensitivity of the security and rm is the expected market return. It basically means that investors should be compensated for the risk they take and this risk (or expected return) equals the rate on a risk free security plus an adjusted risk premium. Hence, the investment should only be commenced if this expected return is higher than the required return of the investor, in other words the WACC can be seen as an opportunity cost.

The DCF methodology is popular in the valuation literature and practice due to its thorough but straightforward approach and its ability to include numerous factors that may influence the value of a firm. Rappaport (1986) labelled these factors, ‘value drivers’. He describes seven valuation parameters (or value drivers); sales growth rate, operating profit margin, income tax rate, working capital investment, fixed capital investment, cost of capital and forecast duration (planning horizon). Consequently, the DCF model requires more inputs than most other valuation techniques. Therefore an unforeseen shift in these inputs, e.g. commodity price can have significant effects on the valuation of firms (Smith, 2003). So, the power of the DCF valuation technique carries some pitfalls, implying that the valuation is only as good as its inputs.

Valuation of oil and gas projects

Within the field of valuation, substantial research has been done on the relationship between financial markets and valuation of commodity firms (Quirin et al., 2000; Damodaran, 2009). The characteristics of the upstream oil industry heavily impact the valuation. In most markets, for example the real estate market, an investor can actually see and touch the assets and directly measure the attributes of that asset. Yet, in the upstream oil industry it is more complicated to measure these attributes, since the oil is hidden underground making it difficult to estimate the exact volume of the accumulation. Studies have shown that reserve estimates can change significantly over time, sometimes as much as fourfold over a project’s life time (Demirmen, 2005). These specific facets of an oil project also vary throughout its stages to maturity. The risk (possibility of failure) and uncertainty (range of outcomes) decline considerably as the project matures. In other words, projects in the exploration phase consist of more risks (e.g. geological) compared to production projects.

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market valuation of an oil firm. These two value drivers explain most of the value ascribed by the market and simple financial metrics (such as ROA2) are of less importance.

Moreover, the DCF method is widespread in the oil industry and seen as the central methodology for analysts to value their projects. According to the Society of Petroleum Evaluation Engineers (SPEE) the DCF valuation method is preferred by almost 90% of the respondents of SPEE’s survey (SPEE, 2008). Smith (2003) believes that the DCF model is adaptable in oil property valuation although the technique has some limitations. He states in his article that to discount future cash flows accurately, it requires an accurate forecast of the oil/petroleum prices and a good expertise of the various risk factors. Due to, for example, the high volatility of oil prices and geological and exploration uncertainties it is difficult for investors and analysts to develop such proper forecasts (Smith, 2003).

Hence, there are several issues between the DCF analysis and the valuation of oil projects. According to Damodaran (2009), commodity firms (e.g. oil firms) show some particular characteristics which affect their valuation. First he refers to the role of commodity price (e.g. oil price), which is seen as one of the main value drivers that can have a big impact on the valuation of commodity firms. Second, the volatility of cash flows and earnings is important; as commodity firms tend to have high operating leverages (high fixed costs) they are less capable of matching their ‘costs’ with the commodity price. For example, an oil firm with high fixed costs may have to keep its projects running even with low oil prices. Third, since commodity firms often need high starting capital, many firms have significant debt financing. So the volatility in operating income (as mentioned before), intensifies itself in even greater swings in net income. Fourth, Damodaran states that even the good performing commodity firms are significantly dependent on the commodity price as they have little control over it. Finally, the limited amount of resources counts much, as every oil field has a limited amount of oil reserves. Whereas every oil company can explore for a new oil field, it cannot ‘create’ oil and therefore the finite amount of oil reserves needs to be taken into account in forecasting expected cash flows. Although Damodaran presents some clear issues in the valuation of commodity firms, some points are more applicable to the upstream oil industry than others. E.g. many small exploration firms in the upstream oil industry do not have access to debt financing and often turn to equity markets to raise capital.

In addition, Moore (2009) describes eight different issues in the DCF methodology when applying the methodology to upstream oil projects. The most relevant issues follow. First he identifies the so called ‘subsurface evaluation’, due to the fact that the main assets (oil reserves) of an oil firm are largely found underground, it is complex to estimate the true value of such assets. Since no oil deposit is comparable to another, historical and relative data (e.g. on production rates of ‘similar’ projects) is often irrelevant. Therefore, the assessment of such risks depends heavily on expertise of the oil

2_{Return On Assets is a performance ratio to indicate how profitable a company is relative to its total assets and is a calculated as:}

Assets Total

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expert/analyst. Although statistical and probabilistic methods are available to include these risks in the valuation of the reserves, there is still a debate on which risk factors to apply in order to account for these risks3. Second, Moore (2009) labelled ‘surface risk’ to illustrate a range of environmental, commercial, logistical and political issues which influence an oil firm’s operations, e.g. the recent floods in Queensland impacting various oil projects and the Deepwater Horizon Oil Spill of a BP’s drilling well in the Gulf of Mexico. Although such events have a low probability they can have disastrous impacts on the oil firm, stakeholders and environment. Hence it is preferred to incorporate these surface risks into the DCF valuation analysis; however it is almost impossible to predict the timing and magnitude of such events. Third, he illustrates the impact of oil price, inflation and FOREX forecasts on the DCF valuation. Since oil prices are very volatile and subject to numerous factors, including the proper estimated prices into the DCF analysis is extremely complicated. Moreover, since oil prices markers, as the Western Texas Intermediate (WTI) and Brent Crude are denominated in US dollars, the impact of the exchange rate can have a significant impact on the revenues in the local currency.

In sum, although there are several issues impacting oil firm valuations, the DCF methodology is still the most widely appreciated valuation tool for upstream oil firms (Moore, 2009; SPEE, 2008).

WHTM Valuation methodology

This section will emphasize WHTM’s valuation methodology. WHTM focuses on mid-cap oil firms and since valuation studies mainly concentrated on large international oil firms, the DCF valuation models described in the literature differ to some extent to WHTM methodology. Quirin et al. (2000) and Osmundsen et al. (2006) studied the valuation of large international oil firms and also defined some ‘key performance indicators’ in their research which are less applicable to the small or mid-cap oil firms. An example of these is the Reserves Replacement Ratio (RRR), which indicates the capability of an oil firm to replace its production with new proven reserves. Since large international oil companies have often numerous different projects in diverse stages of maturity (exploration to production) it is important to evaluate this ratio. In the contrary, small and mid-cap oil firms often have only a few projects at a time, sometimes even in the same stage (e.g. exploration) and due to their limited financial and human/material resources they cannot keep exploring and finding new oil fields. As result these mid-cap firms show more volatility in their exploration and production results. So the RRR would be heavily biased by the small number of projects, resulting in misleading outcomes. Hence, the RRR is more functional for the valuation of large international oil firms compared to the mid-cap firms. The same reasoning applies to the unit finding and development costs (defined as the sum of costs for exploration and development operations divided by the total proved reserves).

Generally, WHTM tries to predict the stream of future cash flows over the life time of a project, discounts these cash flows using a discount rate, and after that sums up all the projects in order to get

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the value of the firm. This modelling is done in a powerful EXCEL spreadsheet to compute a valuation and target price (TP)4. Generally, the following value drivers are most important in WHTM’s financial modelling:

§ Oil/Gas Production/Reserves: For each project of an oil firm WHTM forecasts the oil production (or gas, condensate etc) it can generate over its presumed life time. These numbers are based on estimated production rate, estimated number of drilling wells in the oil field etc. However the main indicator of future production is the estimated reserves (normally 2P reserves) of a particular project, since all impending production forecasts should reflect the total reserves of a project/oil accumulation. Naturally, higher production/reserves forecasts should indicate higher valuations (NPV and TP).

§ Oil/Gas Price: There are several worldwide benchmarks or markers for crude oil prices, of which the “WTI” (West Texas Intermediate) or “Brent” are primarily used. For US gas prices, the most common benchmark is the “Henry Hub”. As both markers are stated in USD, the exchange rate (FOREX) between USD/AUD also has a significant influence on an Australian oil firm, cf. the recent upswing of oil prices was offset by a strong Australian Dollar (versus the US Dollar). WHTM forecasts various benchmarks of which the WTI, Henry Hub and Australian (natural) gas prices are mainly used in WHTM’s financial models.

§ Operating Expenditures/Capital Expenditures: Operational expenditures (OPEX) result from ‘day-to-day’ business operations and are ongoing costs for running an oil project. A few examples of OPEX are payments of employee wages, rent and utilities. As the oil industry is seen as a capital intensive sector, the capital expenditure (CAPEX) is often higher compared to other industries (e.g. service sector). CAPEX is especially high up front - and lower throughout the production stage and can be substantially high again at the abandonment/restoration at the end of the project’s maturity.

§ Risk factor: Due to several uncertainties on whether or not a project will reach commercialization, a risk factor is applied to the different stages of maturity. Logically, a mature project (e.g. production) often relates to a lower risk factor compared to for example a project in the exploration stage. WHTM applies the following range of risk factors throughout the life of a project;

Stage in project’s maturity WHTM’s Risk factor (%)5

Production 90-100

Development 60-80

Appraisal 20-60

Exploration 5-30

4_{TP is WHTM’s forecasted value of a firm, normally twelve months forward, however in some cases this time period can vary in}

a longer or shorter forecast.

5_{These are the generally applied risk factors, in some cases an adjusted factor is used. Moreover, risk factors are fractional}

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Normally this risk factor is based on the technical (e.g. is the organization familiar with the production technology or not) and engineering maturity (e.g. is the project only a desktop study or is it in operation). Since little research has been done on how to calculate these risk factors and it is difficult to compare different oil projects due to their specific characteristics, this risk factor is often a subjective judgment of the oil expert/analyst.

§ Discount rate: In order to discount the future cash flows of an oil firm, WHTM’s main indicator is the nominal after-tax Weighted Average Cost of Capital (WACC). Currently WHTM is discounting future cash flows at a 12% discount rate, as it was recently upgraded from 11.5%.

It is important to see the distinction between the discount rate and the risk factor. Future cash flows are discounted back using the 12% discount rate and then the NPV project value is multiplied by a risk factor in order to control for the project specific implied ‘discount rate’. Finally, the sum of all these risk adjusted projects results in a firm valuation.

Hence, although WHTM’s valuation models consist of a wider range of financial parameters (e.g. FOREX, cash position), this study emphasises on the main value drivers in WHTM valuation methodology as described above. Most of these variables show similarities of the seven value drivers of Rappaport (1986), e.g. the reserves/production forecast combined with the oil price forecast is considered to be a proxy for the value driver; sales growth (rate). Even though not all Rappaport’s valuation parameters (e.g. tax rate, planning horizon) are discussed in this thesis, WHTM does include them in its financial modelling.

Crystal Ball

Since future cash flow are estimations which are subject to many uncertainties, especially in the oil industry as stated above, these estimates become less certain. Moreover, whereas DCF valuations often have a single set of future cash flow estimates, WHTM estimates some of these uncertainties using an EXCEL-software package, called ‘Crystal Ball’. Crystal Ball is a simulation program that uses a Monte Carlo analysis, which performs multiple simulations/calculations through a re-sampling process (French and Gabrielli, 2005). For every simulation a factor or valuation parameter (e.g. oil price) is chosen from a range of values (a probability distribution) in order to produce a value. This is repeated a thousand times, each time using a different set of random values from the probability functions. This result in a probability distribution of output values from which statistics can be derived. This is done for the following value drivers; reserves (risk), oil price volatility, FOREX, operational costs, risk factor and exploration. Hence, as al these parameters are heavily exposed to uncertainties, WHTM tries to incorporate and control for these risks using the Crystal Ball/Monte Carlo simulations.

French and Gabrielli (2005), present the Monte Carlo simulation process as follows:

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Source: French and Gabrielli, 2005, p. 82

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Research methodology

The research analysis is divided in two stages, first a study is conducted on the valuation estimates of WHTM. In order to examine WHTM’s valuations, its estimates are studied and compared with the actual value ascribed by the market (share price) and the valuation estimates of its competitors. The resulting gap between these valuations will be the starting point of the second step of the research. Hence, the results of the first part are used to review WHTM’s valuation technique with an emphasis on a couple of facets in the valuation methodology. This chapter will first introduce the methodology of step one and two, after which the sample for both analyses is presented. Next, the selected data and their calculations are discussed. Finally, an overview of the research is presented.

Methodology step one

First, a time-series analysis is conducted, examining and comparing the historical data of WHTM’s DCF estimates and TPs with the estimates of its competitors and the actual share prices. The time frame starts on 01/01/2008 and ends on 25/02/2011. This time period is chosen as it incorporates most valuation data of the oil firms currently covered by WHTM and because there are limited valuation estimates available prior to 2008.

Since the TPs set by WHTM (and its competitors) are an estimation of where the share price should be usually in twelve months time, a time shift of twelve months is necessary; this means that TPs set on 01/01/2010 are compared with the actual share prices on 01/01/2011. Opposed to the TPs, DCF valuations have a more present character and are therefore compared with the share price on the report date. Due to the use of daily share prices, the results of the analysis depend heavily on the share price on one particular date. If share prices show a lot of volatility during a period of time, the use of daily share prices may result in an inaccurate representation of the market price. However, since the DCF and TP estimates are also based on information on a report date, the analysis will compare these ‘daily estimates’ with daily share prices. A DCF valuation is associated with the share price on report date and a TP estimate is compared with the share price 365 days after the report date.

Finally, by averaging the TPs set by other financial service firms, the market consensus is calculated. With this market consensus, the analysis can examine WHTM’s presumption that its estimates are generally below market consensus and also review WHTM’s TP estimation performance compared to its competitors. In addition the spread of the highest and lowest TP set by other financial firms is also included in the analysis.

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2010). The data for this analysis utilizes the same time period (01/01/2008-25/02/2011) as in the first step of this analysis.

Figure 2: Valuation estimates vs. Share price

The 45° line resembles a perfect positive correlation between these two variables (share prices and valuation estimates) which indicates that the valuation estimations of WHTM are equal to the value ascribed by the market. Although this line represents a perfect correlation, it is worth mentioning that the main goal for WHTM (as for every investor) is to find the undervalued firms and subsequently generate profits as prices converge on value. Hence, it is more important that WHTM’s TPs are close to the 45° line, since a good forecast of the share prices can help WHTM pick the undervalued firms. This analysis will provide a good impression whether or not WHTM is able to valuate the mid cap oil firms close to actual price ascribed by the market. It needs to be noted that this analysis has no time element and can therefore be seen as a general review of WHTM estimates (DCF and TP) compared to the actual value ascribed by the market over the last three years. As stated before, TPs set by WHTM and its competitors are compared with share prices twelve months forward. Lastly, a linear regression is carried out to find the slope of the line that best fits the set of data. This line will show the relationship of WHTM’s valuations and market consensus compared to the share prices. In order to present the statistical power (goodness of fit) of the firm analysis, the R2 of each regression is also included.

Methodology step two

Subsequent to the analysis of step one, the presumed gap between WHTM (TP) valuations and the actual market value is examined. This second step of the research starts with a study of several value drivers of WHTM’s DCF valuation methodology. The impact of these valuation parameters on the TP valuation is examined in order to identify the causes of the valuation gap. Moreover, to conduct this analysis, two valuation models are selected, first the General Evaluation Model Oil and Gas (GEMOG) version 3 as of 22 March 2010 and the second model is GEMOG 4 finished on 25 March 2011. As

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WHTM constantly updates and improves its financial modelling, this analysis consists of two different model versions. Although both models are comparable and similar in most aspects, some alterations have been made in the 2011 model.

In order to explain the gap, the analysis starts with implementing financial data/forecasts of the March 2011 model into the March 2010 model. For example, if commodity prices increased between the two valuation models, it is likely that WHTM had a more optimistic forecast in the 2011 GEMOG model and so it is expected that the reported TP set in 2010 should have been higher. Hence, this should explain why the TP set in 2010 does not match the actual share price twelve months later. By doing this for several value drivers the presumed gap should shrink. However, since the models are slightly different and highly complex, it is only possible to include 2011 market data6 into the 2010 model, the impact of firm specific variables (e.g. reserves) is done by calculating the difference in NPV/share (after tax) between both models. The main issue is to calculate the impact of these forecast changes in a single denominator (NPV in AUD per share). This way the several variables can be presented in a so called waterfall chart, in which a ‘value bridge’ is presented between the reported TPs set in March 2010 and the actual share price in December 2010.

So, whereas step one of the analysis is a continuous time series analysis between 01/01/2008 and 25/02/2011, the analysis of step two differs in the way that it makes a comparison between two points in time (22/03/2010 and 31/12/2010). Moreover, conversely to the TPs in step one which are compared with the share price twelve months forward, in step two TPs are compared with the share price nine months later. The reason for this is that WHTM forecasts what the expected share price (TP) should be at the end of each year. Moreover, TPs set in March 2010 should reflect the share price at the end of 2010.

Additionally, since the TP is a forecast of where the share price should be at the end of 2010, the NPV is calculated using data from 01/01/2011 onwards and the 2010 data is excluded, as these are sunk costs regarding the TP valuation for 31 December 2010. Hence, the analysis of step two implies some time issues and therefore some broader assumptions are required. Furthermore, since oil firms continuously adjust their project portfolio this may increase/decrease the valuation. Therefore the comparison can be biased by new or abandoned projects in GEMOG 4 compared to GEMOG 3. Therefore the analysis is done on a project to project basis, and the project portfolio in the 2010 model is adjusted and compared to an identical portfolio in the 2011 model. Hence, the NPV impacts of reserves/CAPEX/OPEX are calculated with similar project portfolio. The value of potential new projects is controlled for by adding the total project’s value separately. This way the comparison of e.g. OPEX/CAPEX and Reserve numbers is not biased by new projects.

6_{Market data, such as CPI, FOREX and commodity prices apply to every firm, whereas firm specific data (e.g. reserves) differ}

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After these two analyses, two overviews are presented in which the results are discussed. Subsequently, in order to study a potential structural gap between WHTM methodology and the actual market price, the research discusses valuation issues in the methodology and compares them with the oil industry practices (see appendix B). Finally, fig. 3 represents the overall outline of the research.

Figure 3: Outline of research

Sample

The majority of oil firm valuation based studies have used samples of large US and European oil firms. This thesis contributes to the field of valuation research by examining a sample of firms in a different region (i.e. Australia) and with a different firm size (i.e. mid-cap).

WHTM Research covers approximately twelve ASX listed mid-cap oil firms.7 The number of firms which are covered by WHTM Research changes over time mainly due to mergers/acquisitions. In the first step of the analysis the following eight oil firms will be examined:

Table 1: Sample firms

7_{Although there are many different classifications on what the exact size of a mid-cap firm should be, according to WHTM a}

mid-cap firm has a market capitalization of approximately A$ 100 million –A$ 2,000 million.

Value ascribed by the market

WHTM

Valuation

Impact of

value drivers

Presumed valuation gap

WHTM adjusted

valuation

Potential structural valuation gap Firm specific indicators Market indicators

S

TEP ONE

S

TEP TWO

D

ISCUSSION

(APPENDIX B)

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Company name ASX Code

Core line of business Market

Cap(on 28/02/11) (A$ Millions) Number of reports contributing to WHTM valuation estimates Number of brokers contributing to market consensus Oil Gas Coal Seam Gas

Arrow Energy Ltd. _AOE _x _3,4378

46 4

AWE Ltd. _AWE _x _x ₈₆₁ ₃₂ ₁₁

Beach Energy Ltd. _BPT _x _x _1,033 ₂₆ ₆

Bow Energy Ltd. _BOW _x ₃₉₄ ₁₇ _-

Comet Ridge Ltd. _COI _x ₄₉ ₁₀ _-

Horizon Oil Ltd. _HZN _x _x ₃₉₅ ₃₈ ₄

Molopo Energy Ltd. _MPO _x _x _x ₂₄₆ ₃₄ ₃

Nexus Energy Ltd. _NXS _x _x ₄₆₄ ₃₀ ₈

Total 5 5 4 6,881 233 409

Although WHTM covers a couple of additional mid-cap gas firms, there is insufficient valuation data in order to include them in the analysis, since these firms are recently covered by WHTM. Furthermore, WHTM stopped covering AOE as it was delisted from the ASX in August 2010, due to a joint acquisition of AOE by Shell and PetroChina. However, AOE is included in the analysis since it was seen as a prominent firm in the stock coverage of WHTM and was covered until recently (30-07-2010). These eight firms portray a good representation of the Australian mid-cap oil market, as it is a balanced combination concerning the firm’s core line of business as well as its market cap (see table 1).

This sample consists of eight firms, for which were 233 company reports were consulted in order to study WHTM’s TPs and DCF valuations, averaging 29 reports per firm. No market consensus data was found for BOW and COI. Since WHTM’s initiation of coverage of COI was in December 2009, a small amount of data was found on WHTM’s valuation estimation (10 valuation reports, with 5 revisions). Furthermore, only 17 valuation reports (with 10 revisions) on BOW were examined. As a result COI and BOW were excluded from the analysis due to insufficient data. A satisfactory amount of valuation reports and market consensus data was found of the remaining six firms.

The sample in step two differs slightly from the sample used in step one as the valuation model GEMOG 3 (22 March 2010) differs from GEMOG 4 (25 March 2011). As stated before, AOE was acquired by Shell and PetroChina in August 2010, therefore it is not in the 2011 model and is excluded from the sample. Moreover, although BOW and COI are not in the analysis of the first step, they are included in this step of the analysis as they might contribute to the overall assessment of the valuation gap. That is, BOW and COI are valuated in both valuation models, therefore comparing their valuations

8_{Market capitalization of AOE on 25/08/2010}

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(with its value drivers) enhances the valuation gap analysis. Hence, the sample of step two includes seven of the initial eight sample firms of step one.

Data

Data step one

In the first step of this research, three concepts are examined; the valuation estimates of WHTM, market consensus and share prices. In order to study these concepts the following variables are analysed:

§ Valuation estimates of WHTM

In order to get a comprehensive view on the valuation estimates of WHTM, two variables are included; the DCF valuations and TPs.

- DCF Valuation: WHTM is constantly monitoring and revaluing the firms it covers. WHTM’s Intranet records all these historical company and market reports. Whenever WHTM revalues a firm, the report is uploaded to the intranet. These company reports as well as the ‘Quarterly Commodity Price Reviews’ are consulted to find the historical DCF valuations (in AUD per share) on the sample firms over the last three years (depending per firm). All of these valuations are utilized and compared to the share price of the firm on the report date. - Target Price: The TPs set by WHTM are stated in the same reports as mentioned in the previous section; therefore the same reports are used to obtain the TPs (in AUD per share) of WHTM on the sample firms over the last three years (depending per firm). Unlike the DCF valuations, the TPs are compared with the share prices of the firm twelve months forward.

§ Market Consensus

The average TP of other financial firms, besides WHTM, is considered to be the market consensus.

- Target Price Consensus: Although there is no database which keeps track of all (DCF) valuations from all financial firms, Bloomberg10 keeps record of the TPs set by analysts of various financial firms. WHTM is left out of this market consensus, since the analysis wants to compare WHTM performance compared to other financial market parties. These TPs (in AUD per share) are consulted and averaged in order to find the consensus of the TPs among other financial firms on the sample firms over the last three years (depending per firm). Each time a financial service firm adjusts a TP, it is seen as a revision and therefore a new market consensus appears. Hence, whenever the market consensus is revised, the average TP on the ‘revision date’ is related to the share price of the oil firm twelve months forward. Finally, it needs to be noted that there might be more financial firms contributing to the market consensus but these firms are not included in the consensus estimate, as these firms do not publish or restrict their TPs in Bloomberg (see appendix C for a complete list of firms contributing to the target price consensus).

§ Share Price

10_{Bloomberg is a global financial software, media and data company. Primarily as a specialist in providing financial information}

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- Share Price: The historical daily (closing) share prices (in AUD) of the sample firms over the last three years are found in the database of IRESS11 and consulted to compare them with the valuation estimates of WHTM and its competitors.

Data step two

The data studied in the second step contains several input variables or ‘value drivers’ of WHTM valuation methodology, share prices and TP data. As mentioned before, the TP set in March 2010 is a forecast of the share price as of 31 December 2010; therefore this valuation is compared with the share price at the end of 2010. Moreover, as daily share prices are highly volatile and can bias the comparison between TP and the actual share price, the share price is averaged (the closing prices of the last five trading days in 2010) to overcome this bias. Since the TP is also stated in price per share, the analysis has to compute the value drivers also in price (NPV in AUD) per share. These financial parameters can be divided into two fields of indicators: market indicators that apply to all firms (e.g. commodity price forecasts) and firm specific indicators (e.g. reserves). Step two examines the following valuation parameters;

Market indicators

§ CPI; CPI is used as a proxy for inflation. CPI is used to adjust several prices (e.g. oil price) in the future. E.g. oil price forecasts are at some points (about from five years forward) multiplied by the inflation rate estimates, in order to predict the future oil prices. Although its contribution in the valuation is not as significant as the other parameters, the CPI is examined to study its impact on the valuation gap by including WHTM’s forecast of the 2011 model within the 2010 model.

§ FOREX; FOREX is an important forecast, as most of the firms operate in an international environment. Moreover the majority of the sample firms report their financial statements in AUD while oil/gas price markers are denominated in USD12. To analyse the impact, FOREX forecasts of GEMOG 4 are included into GEMOG 3.

§ Commodity price forecasts; WHTM estimates the future oil and gas prices by forecasting the WTI and Henry Hub prices respectively. Other crudes such as Brent and Tapis are linked to WTI. The effect is measured in similar approach as the previous two parameters.

Hence, the impact of these three value drivers on the TPs of the sample firms is calculated by including WHTM’s forecasts in the GEMOG 4 model into the 2010 model.

Firm specific indicators

§ Reserves/production: being the most important assets of an oil firm, a down-/upgrade in reserves can have a significant impact on valuation of a firm. As WHTM’s valuation models forecast

11_{IRESS Market Technology Limited supplies share market and wealth management systems, which include real time data,}

analytics and news primarily used by stockbrokers, financial institutions and research analysts.

(23)

expected production, the proxy for reserves is the total future production, in BOE13. This is because what a firm expects to produce should reflect its reserves namely. To calculate the NPV/share impact of the possible change in reserves between the two models the sales revenue (which is stated in NPV) is divided by the total forecasted production. This way the NPV per BOE is calculated and multiplying this by the change in oil reserves between the two models presents the NPV of the reserves delta. Additionally since the sales revenue is a pre tax number, the analysis controls for a 30% tax rate. Finally, in order to calculate the NPV per share, this number is divided by the number of (diluted) shares. The impact of adjusted reserves of firm i is calculated as follows:

(

) (

)

shares diluted of Number Taxrate TP TP TSR valuation on reserves impact Gemog Gemog NPV i         − × ∆ ×         ∆ 1 : 2010 2010

Where TSR denotes the NPV of the total sales revenue in AUD and TP describes the total forecasted production in BOE14.

§ CAPEX/OPEX: Since the oil industry is a capital intensive sector, capital and operational expenditures can have a significant impact on the earnings. An insufficiently conservative outlook can result in an excessively high valuation and too high estimates will end up with a too soft valuation. Both expenditures are examined in a similar approach. WHTM’s CAPEX and OPEX forecasts in 2010 are excluded from the forecast of the 2011 model. After that all the deltas of the forecasted expenditures are discounted by the discount rate used in the 2010 GEMOG model (11.5%) to find the NPV of both expenditures and summed up. Since WHTM uses NPV after taxes in their whole DCF methodology, the analysis makes the assumption of a tax rate of 30%. Finally, the NPV (after tax) is divided by the number of diluted shares, to end up with the NPV/per share. The formulas for CAPEX and OPEX are presented below;

(

)

shares

diluted

of

Number

Taxrate

Capex

Total

valuation

on

Capex

of

impact

t t t i













−

×













+

∆

=

∆

∑

∞ =

1 )

115 .

0

1 (

1

(

)

shares

diluted

of

Number

Taxrate

Opex

Total

valuation

on

Opex

of

impact

t t t i













−

×













+

∆

=

∆

∑

∞ =

1 )

115 .

0

1 (

1

§ Cash etc; WHTM also forecasts cash positions, equity raisings and franking credits. E.g. if a company is expected to raise capital during a year this should be reflected in the modelling/valuation. Since the forecasts of these three parameters are already stated in NPV/per share the 2010 forecasts are deducted from the 2011 numbers in order to present their impact on the TP valuation.

13_{BOE stands for Barrels of Oil Equivalent}

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§ Exploration; As oil companies often have projects in several stages of maturity (exploration to production), WHTM also values projects in early stages of maturity (exploration/appraisal). Since it is unsure wether or not these projects will actually generate future cash flows (reach commercialization), often there is a factor applied to account for these risks. The exploration forecast (which is stated in NPV/share) of 2010 is subtracted from the estimates in 2011, to calculate the influence of exploration.

§ Risk factor; Risk factors are attributed to the projects of an oil firm and in order to account for potential risks, a shift in these factors influences the valuation of a particular firm. First an assessment of the various projects is conducted in order to find projects in which an adjustment in risk factor has been made. The ratio between the risked project valuation (as NPV/share) and the unrisked project valuation (as NPV/share) of both models is calculated. Next the delta between both ratios is multiplied by the unrisked valuation of the 2010 GEMOG model, to find the impact on TP valuation (see formula below).

(

Gemog2011 Gemog2010

)

Gemog2011

i

RF

UV

valuation

on

RF

of

impact

∆

=

−

×

RF stands for risk factor and UV denotes the unrisked valuation of the production projects.

§ Discount rate; since WHTM applies an 11.5% discount rate in the 2010 GEMOG model and a 12% rate in the 2011 GEMOG model, the impact of this change is calculated by implementing a 12% discount rate in the 2010 GEMOG model, which should imply that the TPs will decrease due to a higher rate.

The adjustments of these value parameters should imply that the TPs set in March 2010 for 31 December 2010 converge to WHTM’s DCF valuation (at 31 December 2010) and to the share price at the end of 2010.

The analysis in some of these value drivers, especially firm specific indicators, makes some broad assumptions to calculate the NPV/share impact on the reported TP set in March 2010 (e.g. the assumed tax rate in the calculations of CAPEX and OPEX numbers). Moreover, the value drivers are calculated separately, rather than in a sequential order, hence some compound effects may be missed.

BOW and COI

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BOW’s and COI’s (3P15) reserves, and the pricing and risking of the reserves between GEMOG 3 and 4 is examined.

The impact of the change in reserves or resource volume of the exploration projects between both models is used as a proxy for reserves impact and is calculated as follows:

(

)

(

)

shares

diluted

of

Number

RV

Volume

shift

volume

of

impact

_i

=

Gemog2011

−

Gemog2010

×

Gemog2010

RV denotes the risked value of the projects in AUD per GJ and Volume stands for the total 3P reserves in PJ.16 Furthermore, as risk factors especially in exploration projects can change substantially over time, the impact of a change in risk factor between both models is calculated using the following formula;

(

)

(

)

shares

diluted

of

Number

UV

RF

shift

risk

of

impact

_i

=

Gemog2011

−

Gemog2010

×

Gemog2011

RF described the risk factor17, UV is the unrisked value of the projects. Finally, the adjustment in (unrisked) value of projects is controlled for, using the formula:

(

)

(

)

shares

diluted

of

Number

Volume

UV

shift

value

of

impact

_i

=

Gemog2011

−

Gemog2010

×

Gemog2011

Hence, these three calculations are connected, and therefore by including these parameters into the analysis the impact of WHTM changed views on the valuation of the exploration projects of BOW and COI can be studied.

15_{See appendix A or the reserve classification system}

16_{PJ stands for PetaJoule, a common measurement unit to identify the quantity of CSG resources.}

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Results

In this chapter the results of the analysis of step one and two are presented. The first step tries to verify and explain WHTM’s presumption that the sample firms have generally traded below WHTM valuations and that WHTM’s TP were lower compared to its competitors. Subsequently the presumed gap is examined by studying various value drivers in the WHTM valuation methodology in order to quantify some potential under- or overvaluation in this methodology (step two).

Results step one

In this section the sample firms are examined in a time series analysis and WHTM’s as well as its competitor’s valuation estimates are plotted against the actual share price in order to detect potential structural under- or overvaluation issues.

Arrow Energy Ltd.

Graph 1 shows the historical valuation estimates and share prices of AOE with a 12 months time shift for the TPs of WHTM and its competitors (market consensus). The analysis of AOE had two data problems. First some DCF valuations of WHTM during 2008 and 2009 are excluded from the analysis. The reason for this exclusion is a change in analyst coverage and methodology during this time period, as WHTM used an unrisked DCF valuation technique meaning that WHTM did not control for some project specific risks (e.g. reserve risk). As a result the valuations were excessively high compared to the market price and previous valuations and are therefore left out of the analysis. Second, the market consensus data starts at the end of 2009 and due to the twelve months time shift of the market consensus, should be compared to the share prices at end of 2010. However, since AOE was delisted from the ASX in August 2010, it is not possible to compare the ‘shifted’ market consensus with the actual share prices.

Graph 1 illustrates that WHTM’s DCF valuation estimates were significantly high compared to the share price, although it improved over time. The sudden rise of AOE share price in the middle of 2010 was due to speculations of a joint acquisition of AOE by Shell and PetroChina. Between January 2008 and March 2010, WHTM had a “buy” recommendation in almost all of their company valuation reports.18 Ideally, the share price converged to the valuation when the acquisition took place. The TPs set by WHTM show a reasonable accurate estimation power.

The regressions in graph 2 present a R² of 0.47 and 0.61 for WHTM’s DCF and TP valuation estimates respectively. Although this is insufficient to draw any significant conclusions, the data points are in line with graph 1 as WHTM’s DCF estimates were consistently above the actual market price. On the other hand, WHTM’s TPs are substantially closer to the 45° line compared to the DCF valuations, implying that the TPs set by WHTM were a fairly good forecast of the actual share price twelve months forward.

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Graph 1: Time series analysis AOE AOE $-$1 $2 $3 $4 $5 $6 $7 $8 J a n /0 8 M a r/ 0 8 M a y /0 8 J u l/0 8 S e p /0 8 N o v /0 8 J a n /0 9 M a r/ 0 9 M a y /0 9 J u l/0 9 S e p /0 9 N o v /0 9 J a n /1 0 M a r/ 1 0 M a y /1 0 J u l/1 0 S e p /1 0 N o v /1 0 J a n /1 1 M a r/ 1 1 M a y /1 1 J u l/1 1 S h a re P ri c e - TP Consensus Spread TP Consensus DCF WHTM Share price TP WHTM

Graph 2: Share price vs Target Price and Discounted Cash Flow AOE

AOE y = 0.527x + 1.1821 R2 = 0.4733 y = 0.6359x + 1.0419 R2 = 0.6057 $-$1 $2 $3 $4 $5 $6 $7 $8 $- $1 $2 $3 $4 $5 $6 $7 $8 TP/DCF S h a re P ri c e DCF WHTM TP WHTM Linear (DCF WHTM) Linear (TP WHTM) AWE Ltd.

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share price. WHTM had a “buy” recommendation in almost all of its valuation reports, despite the enormous market correction of the Global Financial Crisis (GFC).

The GFC had a substantial impact on these valuation and TP estimates of WHTM and its competitors. Since both parties did not foresee the GFC, the subsequent fall in oil prices and its impact on the oil industry in Australia, the TPs set before and during the GFC were significantly high compared to the share prices afterwards. However, although WHTM’s TPs for AWE were high, WHTM adjusted its estimates more rapidly to the market conditions compared to its competitors. This applies throughout the whole time series analysis, as WHTM adjusted its TPs faster to market up- and downswings compared to the averaged TP of the other eleven financial firms covering AWE. Finally, the drop in AWE share prices in the middle of 2010 was the result of poor exploration results.

Graph 4 shows that the DCF valuations of WHTM are reasonably close to the 45° line. Concerning the TPs of WHTM and market consensus, these valuation estimates are substantially below the 45° line. The regression between these variables is very weak, with a R2 of 0.12 and 0.36 respectively. Even if the data that is affected by the GFC is excluded and only data between 01/01/2010 till 25/02/2011 is used, the explanatory power remains weak for both variables. However it can be concluded by examining graphs 3 and 4, that despite the relationship being weak, most TPs set by WHTM are closer to AWE’s share prices in comparison to the averaged TP of other brokers.

Graph 3: Time series analysis AWE

AWE $-$1 $2 $3 $4 $5 $6 J a n /0 8 M a r/ 0 8 M a y /0 8 J u l/ 0 8 S e p /0 8 N o v /0 8 J a n /0 9 M a r/ 0 9 M a y /0 9 J u l/ 0 9 S e p /0 9 N o v /0 9 J a n /1 0 M a r/ 1 0 M a y /1 0 J u l/ 1 0 S e p /1 0 N o v /1 0 J a n /1 1 M a r/ 1 1 M a y /1 1 J u l/ 1 1 S e p /1 1 N o v /1 1 J a n /1 2 S h a re P ri c e - TP Consensus Spread

TP Consensus Share Price

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Graph 4: Share price vs Target Price and Discounted Cash Flow AWE AWE y = 0.7629x + 0.3135 R2 = 0.6431 y = 0.2285x + 1.4561 R2 = 0.1225 y = 0.6777x - 0.2397 R2 = 0.3609 $-$1 $2 $3 $4 $5 $6 $- $1 $2 $3 $4 $5 $6 TP/DCF S h a re P ri c e DCF WHTM TP WHTM Average TP

Linear (DCF WHTM) Linear (TP WHTM) Linear (Average TP)

Beach Energy Ltd.

Since WHTM initiated coverage BPT at the end of the GFC (18 December 2009), the impact of the financial crisis is limited. Graph 5 represents the time series analysis in which WHTM outperforms the market consensus regarding its estimations. Throughout the dataset WHTM’s TPs are consistently closer to the share prices compared to market consensus. Opposed to its competitors, WHTM increased its TP in the beginning of 2010. This is mainly due to improved production guidance and a more positive outlook of oil prices 12 months earlier (when the actual TP was set). Unfortunately the market did not assign additional value to these more optimistic views.

Although WHTM’s DCF estimates were reasonably flat throughout the time series, it resembled a good valuation compared to the share price. WHTM’s valuation reports also indicated an appropriate recommendation, since WHTM suggested buying before the small peak in the beginning of 2010, and had a more conservative recommendation (hold) afterwards.

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Graph 5: Time series analysis BPT BPT $-$0.2 $0.4 $0.6 $0.8 $1.0 $1.2 $1.4 $1.6 $1.8 $2.0 Ja n /0 8 A p r/ 0 8 Ju l/ 0 8 O c t/ 0 8 Ja n /0 9 A p r/ 0 9 Ju l/ 0 9 O c t/ 0 9 Ja n /1 0 A p r/ 1 0 Ju l/ 1 0 O c t/ 1 0 Ja n /1 1 A p r/ 1 1 Ju l/ 1 1 O c t/ 1 1 Ja n /1 2 S h a re P ri c e - TP Consensus Spread

DCF WHTM TP WHTM

Graph 6: Share price vs Target Price and Discounted Cash Flow BPT BPT y = 0.3194x + 0.4944 R2 = 0.2569 y = -0.2766x + 1.046 R2 = 0.144 y = 0.0754x + 0.6714 R2 = 0.0431 $-$0.2 $0.4 $0.6 $0.8 $1.0 $1.2 $1.4 $1.6 $1.8 $2.0 $- $0.2 $0.4 $0.6 $0.8 $1.0 $1.2 $1.4 $1.6 $1.8 $2.0 TP/DCF S h a re P ri c e DCF WHTM TP WHTM TP Consensus

Linear (DCF WHTM) Linear (TP WHTM) Linear (TP Consensus)

Horizon Oil Ltd.

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the second half of 2009 due to positive reserves and exploration results. The recommendations of WHTM had a more cautious advice (hold) during the downfall and an optimistic recommendation (buy) during the upswing of HZN share price. Furthermore, WHTM reacted faster than other market parties on the GFC and the rise of HZN.

Regarding WHTM’s DCF valuations, they were in line with e.g. AOE and AWE as they were consistently above market prices, although it improved at the end of 2009. The upswing in share price and valuations during this period was mainly the result of favourable project control shifts.19

If the analysis excludes the impact of the GFC, by including only data after 01/01/2010, the regressions of the TPs of WHTM and its competitors improve although with a still very low R2. Due to the inconsistency of WHTM’s TP and market valuation estimates compared to the share price, no inference can be drawn on whether or not WHTM estimates were better than its competitors.

Graph 7: Time series analysis HZN

HZN $-$0.1 $0.2 $0.3 $0.4 $0.5 $0.6 $0.7 $0.8 J a n /0 8 A p r/ 0 8 J u l/ 0 8 O c t/ 0 8 J a n /0 9 A p r/ 0 9 J u l/ 0 9 O c t/ 0 9 J a n /1 0 A p r/ 1 0 J u l/ 1 0 O c t/ 1 0 J a n /1 1 A p r/ 1 1 J u l/ 1 1 O c t/ 1 1 J a n /1 2 S h a re P ri c e - TP Consensus Spread

DCF WHTM TP WHTM

19_{HZN’s sold part of its PNG assets which provided additional cash to develop the Stanley condensate project and also gave}

OF UPSTREAM OIL AND GAS FIRMS T HE IMPACT OF VALUE DRIVERS ON DCF VALUATIONS

T

HE IMPACT OF VALUE DRIVERS ON

DCF

VALUATIONS