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Master thesis in Finance and International Business
Investigation of optimal valuation
methods in the pharmaceutical
industry
Authors: Morten Vester & Andreas Holmgaard Petersen
Characters: 185.598
Supervisor: Özlem Dursun-de Neef
Aarhus University School of Business and Social Sciences
Department of Economics and Business
September 1st 2015
Abstract
This thesis has the purpose of determining the optimal valuation method for pharmaceutical
drug development projects. A literature review of valuation methods used by practitioners’
shows that more simple models, such as DCF valuation, are used, regardless of the industry
context. The motivation in this thesis is therefore to investigate the potential of using more
advanced models in the valuation of drug development projects. More specifically, one of the
objectives is to investigate the potential of using Real Option theory.
In order to fulfil the purpose of the thesis, it consists of both a theoretical- and practical
research approach. The theoretical part is an analysis and discussion of four valuation models;
Discounted Cash Flow, Decision Tree Analysis, Binomial-, and Quadranomial Real Option
approach. In order to investigate theoretical differences in each of the valuation models, four
evaluation criteria are constructed; Concept, Uncertainty, Strategy Flexibility, and Usability.
Each of the valuation models is then evaluated upon these criteria. The practical research
approach is a case study on a phase I pharmaceutical diabetes development project.
The thesis finds that the pharmaceutical industry is characterised by sequential dependent
clinical phases, which are uncertain in time-length, success, and cost. An optimal valuation
method must thus be able to comprehend and incorporate the uncertainty of each clinical
phase. We find that an optimal valuation model must be able to separate market and
technological uncertainty to provide strategic flexibility. From the evaluation of each criteria
and the practical implementation of the case study, the thesis finds that both the DTA and the
Quadranomial Real Options model have high potential. Both models can explicitly incorporate
future events i.e. the clinical phase success rates. The thesis further finds that the Real
Option approach in general has a good potential in the pharmaceutical industry, but is
challenged by the more complex practical implementation.
The thesis reaches the conclusion that Decision Tree Analysis is the best tool, when valuating
pharmaceutical drug development projects, from an external point of view. Since the findings
of this thesis are based on partly a practical research approach, it is likely that we influence
the result because of our pre-determined expectations.
Table of Contents
1 Introduction --------------------------------------------------------------------------------------------------------------------- 1
1.1 Methods of science ------------------------------------------------------------------------------------------------------- 3
1.2 Delimitations -------------------------------------------------------------------------------------------------------------- 5
2 Introduction to the pharmaceutical industry ------------------------------------------------------------------------- 7
2.1 Definition ------------------------------------------------------------------------------------------------------------------- 7
2.2 Size and sales ------------------------------------------------------------------------------------------------------------- 8
2.3 Research & development ----------------------------------------------------------------------------------------------- 8
2.4 Patents ---------------------------------------------------------------------------------------------------------------------- 9
2.5 Drug development ----------------------------------------------------------------------------------------------------- 10
2.6 Success rates, cost, and time ---------------------------------------------------------------------------------------- 12
2.7 Sum-up -------------------------------------------------------------------------------------------------------------------- 18
3 Valuation methods in the pharmaceutical industry -------------------------------------------------------------- 19
3.1 The Criteria -------------------------------------------------------------------------------------------------------------- 20
3.1.1 Concept -------------------------------------------------------------------------------------------------------------- 21
3.1.2 Uncertainty -------------------------------------------------------------------------------------------------------- 21
3.1.3 Strategic flexibility ---------------------------------------------------------------------------------------------- 21
3.1.4 Usability ------------------------------------------------------------------------------------------------------------ 22
3.2 Standard DCF model -------------------------------------------------------------------------------------------------- 23
3.3 Decision Tree Analysis ----------------------------------------------------------------------------------------------- 27
3.4 Real Options ------------------------------------------------------------------------------------------------------------- 31
4 Case study - practical implementation -------------------------------------------------------------------------------- 49
4.1 Practical implementation of standard DCF model ----------------------------------------------------------- 52
4.2 Practical implementation of Decision Tree Analysis -------------------------------------------------------- 63
4.3 Practical implementation of the Binomial Lattice approach ---------------------------------------------- 68
4.4 Practical implementation of the Quadranomial Lattice approach -------------------------------------- 73
5 Evaluation & recommendation ------------------------------------------------------------------------------------------ 76
5.1 Evaluation of the DCF model --------------------------------------------------------------------------------------- 76
5.2 Evaluation of Decision Tree Analysis ---------------------------------------------------------------------------- 78
5.3 Evaluation of the Binomial and Quadranomial approach ------------------------------------------------- 79
5.4 DTA vs. ROA ------------------------------------------------------------------------------------------------------------ 81
5.5 Future research --------------------------------------------------------------------------------------------------------- 84
6 Conclusion --------------------------------------------------------------------------------------------------------------------- 85
7 References --------------------------------------------------------------------------------------------------------------------- 88
List of figures
Figure 1.1 Chapter structure of thesis
Figure 2.1 The drug development process
Figure 3.1 Criteria for evaluation of valuation methods
Figure 3.2 Standard discounted cash flow model
Figure 3.3 Cost of equity
Figure 3.4 Summary of DCF
Figure 3.5 Example of a decision tree
Figure 3.6 Summary of DTA
Figure 3.7 The intrinsic value of options
Figure 3.8 Payoff positions
Figure 3.9 Recombining binomial tree
Figure 3.10 Binomial risk-neutral formula
Figure 3.11 Option value formula in a binomial tree
Figure 3.12 Option value tree for a two period call option
Figure 3.13 Brownian Motion
Figure 3.14 Possibilities in quadranomial tree
Figure 3.15 Quadranomial formula
Figure 3.16 Direct and indirect volatility estimation methods
Figure 3.17 Logarithmic present value approach
Figure 3.18 Market and technological uncertainty
Figure 3.19 Summary of Real Option
Figure 4.1 Illustration of the development – and commercialisation phase
Figure 4.2 Practical steps in DCF valuation
Figure 4.3 Cost of equity for case project
Figure 4.4 Sensitivity analysis of input in cost of equity
Figure 4.5 Sensitivity analysis of various input
Figure 4.6 Overview of steps in DTA
Figure 4.7 DTA calculations
Figure 4.8 Sensitivity analysis of the components in the discount rate
Figure 4.9 Sensitivity analysis of the technological success rates…
Figure 4.10 Sensitivity analysis of the research and development cost…
Figure 4.11 Practical implementation of the binomial lattice approach
Figure 4.12 Asset tree in the binomial model
Figure 4.13 Binomial option value tree
Figure 4.14 Sensitivity analysis of volatility and risk free rate
Figure 4.15 Steps in the quadranomial approach
Figure 4.16 Quadranomial option value
Figure 5.1 DTA versus ROA
List of tables
Table 2.1 Clinical success rates
Table 2.2 Cost of drug development
Table 2.3 Clinical time length
Table 3.1 Difference between financial and real options
Table 3.2 Overview of selected types of real options
Table 4.1 Development cost assumed for NN9927
Table 4.2 Cost assumptions in the commercialisation phase
Table 4.3 Free Cash Flow of project NN9927
Table 4.4 Overview of Beta estimation
Table 4.5 Calculation of DCF value
Table 4.6 Probabilities used for in each of the decision nodes
Table 5.1 Evaluation of the investigated models
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1 Introduction
Pharmaceutical drug development is a rather cumbersome and costly affair. Cost estimates on
the development of a new approved drug in the United States go as high as exceeding US$ 2
billion (DiMasi, Grabowski, & Hansen, 2014). This combined with the substantial time effort
required to develop a new successful drug and the relatively low probability of making a
successful blockbuster-drug are well describing characteristics of the highly uncertain
business environment pharmaceutical companies operate within. The drug development
process is composed of several stages, in which the drug company gathers evidence to satisfy
government regulations that it can consistently manufacture a safe and efficacious form of the
compound for the medical condition it is intended to treat. At the end of each development
stage, the company uses the technological and market information revealed up to that point to
decide whether to abandon or continue development of the compound (Kellogg & Charnes,
2000). This high uncertainty calls for considering financial decision tools that can expand the
notion of a static environment and enable the possibility of evaluating the decisions
management faces as time progresses. To address these concerns, real option valuation models
have been suggested as a suitable way to include uncertainty in investment decisions (Myers,
1984), and this is where this thesis has its starting point.
Both recent research and previous studies have shown that certain valuation methods are
more commonly used by practitioners than others. Particularly Discounted Cash Flow
methods and Relative Valuation are widely and intensively used by practitioners in valuation,
while Real Option valuation is hardly ever used (Bancel & Mittoo, 2014; Block, 2007;
Hartmann & Hassan, 2006; C. V. Petersen & Plenborg, 2012). In their articles (Bancel &
Mittoo, 2014; Demirakos, Strong, & Walker, 2004) more investigate what kind of theoretical
financial models financial analysts use in terms of valuation in different industries. Their
findings show that the only industry, in which a Real Option valuation approach is applied, is
the pharmaceutical industry. About 10 per cent of the participating analysts answered that
they use or have used a Real Option valuation approach to pharmaceutical project valuation.
The study showed that often not a single method is used but several methods are combined to
reach conclusions and recommendations on valuation.
Given the characteristics of the pharmaceutical business environment and the fact that
practitioners have preference towards simpler and more static valuation methods, the
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objective of this thesis is to investigate the differences between fundamental cash flow
valuation- and Real Options Valuation (ROV) models both in theory and in practical use.
Another objective with this thesis is to evaluate the potential of using real option theory in
valuation of pharmaceutical development projects, when having an external point of view. The
focus of the thesis is to evaluate the relative differences between the valuation models, and not
to analyse any mispricing. The focus is on the theory behind the different valuation models
which assumption they are based on, and how they can be implemented in practice, when
having the characteristics of the pharmaceutical industry in mind.
Based on the above and the authors’ curiosity the following research questions are examined.
What are the main theoretical differences between fundamental valuation models and real
option valuation and their underlying assumptions with focus on the pharmaceutical industry?
How do the different models differ in regards to practical implementation, considering their
ease of use and strategic opportunities?
What is the potential, with the case study and theoretical framework in mind, of using the Real
Option valuation in the pharmaceutical industry?
In order to answer the problem statement we have structured the thesis as seen in figure 1.1
below. The thesis will include both a theoretical and a practical research approach. The
theoretical approach will consist of a review and evaluation of different valuation methods,
having the pharmaceutical industry in mind. The theoretical evaluation will be based upon
four defined criteria, which are used to analyse the theoretical differences and help structure a
more in depth theoretical comparison of each valuation model. Each criterion is further used
to assure that the underlying assumptions of each valuation model are discussed in relation to
our research questions. In order to exemplify the discussed theory and differences in approach,
the thesis includes a case study of a Novo Nordisk pipeline project. The case study is a
practical approach, which will enable and ease the discussion of the different valuation
approaches. Before a more in depth evaluation of each valuation approach, it is important to
understand both the theoretical and practical differences between each valuation model.
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Figure 1.1: Chapter structure of thesis
Source: Own creation
As seen in figure 1.1, this chapter is followed by an introduction to the pharmaceutical
industry before the focus is on the evaluation of different valuation methods. In order to
investigate the implementation of the valuation methods, a case study is conducted next,
ultimately making it possible to answer the research questions proposed and making a
conclusion. A more detailed explanation of, how the chapters are structured, will appear in the
beginning of each chapter.
1.1 Methods of science
To improve the arguments and choices made throughout this thesis, the following section is an
explanation of our research approach and method of science. The purpose of this section is to
improve the general understanding of our assumptions and clarify the limitations of our
general research approach.
Economic science is in general build on the basic of the positivistic paradigm1, and financial
theory is grown upon the concepts of this philosophical position. The positivistic paradigm is
based on empiricism, the idea that observations and measurements are the essence of
scientific endeavour, and that research produces facts that correspond to an independent
reality (Eriksson & Kovalainen, 2011). The positivistic paradigm is based on strong
1 Positivism is a term coined by Auguste Comte (1898-1857), refers to an assumption that only legitimate knowledge can be found
from experience (Eriksson & Kovalainen, 2011).
Introduction Introduction to industry
Criteria anddifferent valuation methods
Case study of pharmaceutical project NN9927
Evaluation oftheory and
practical implementation
Conclusion
•Introduction
•Research questions
•Structure of thesis
•Methods of science
•Delimitation
•Industry definition
•Size, R&D, patens
•Development phases
•Succes, costs and time data
•Project NN9927
•Development
•Commercialisation
•DCF
•DTA
•Binomial
•Quadranomial
•Conclusion
•Future research
•Concept
•Uncertainty
•Strategic flexibility
•Usability
•DCF
•DTA
•Binomial
•Quadranomial
•Evaluation of
•DCF, DTA, ROA
•Quadranomial vs.DTA
•Tradable vs. risk
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assumptions about empirical requirements and theoretical independence2, which are often
subject for criticism (Holm, 2011). These assumptions are difficult to satisfy in reality, and the
assumptions may be violated to some extent. Based on our theoretical framework, chosen
paradigm, and assumptions, it is likely that we influence our empirical data to verify our pre-
expectations. Our following results may therefore be biased by our own influence and should
be interpreted in the context of the stated limitations and data input.
The research approach, used in this thesis, is based on both the inductive and deductive
research approach. Deduction is a form of reasoning, in which particular conclusions are
formulated from general premises, and the inductive research logic concludes from the
particular (Eriksson & Kovalainen, 2011). Both research logics are used in different parts of
the thesis, and must be considered as combined through the research process. A research
study based on both the inductive and deductive method is defined as the abduction logic
method3. The abduction research logic is used, when the research process consists of various
forms of reasoning and logic exploratory (Eriksson & Kovalainen, 2011). Our research
approach is using the deductive logic method in the answer of the first problem questions
regarding the theoretical differences between the valuation methods. We use general financial
theory to evaluate the specifics of the pharmaceutical industry and the stated criteria. The
inductive method is primarily used in the case study of development project NN9927, in which
we seek to evaluate upon the theory and the theoretical use of each valuation model.
The financial framework used in the thesis is based on important assumptions about market
efficiency and human rational behaviour. In the context of the positivistic approach, our study
should be based on valuation models that aim to reach the assumptions of the positivistic
requirements regarding valid empirical data and mathematical arguments.
Our case study is based on the development of a single pharmaceutical product. The case
study is built of both project specific knowledge and a large amount of industry data in order
to answer our research question in the best way possible. In relation to our research questions,
we focus on the theoretical differences and potential of each valuation method. The case study
2 Theoretical independence requires that observations must be unbiased (Holm, 2011). 3 The abduction research logic mentioned by Charles Sanders Peirce can be considered as the logic of exploratory data analysis
(Eriksson & Kovalainen, 2011).
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design is thereby inspired by the extensive case study approach4. This design emphasise the
theoretical differences between the different valuations models, rather than analysing the
specific case project. As mentioned, it is likely that we influence our findings, which also
influence the question if the case study can be generalized to similar studies. Based on our
input data, we believe our findings can be generalised and used for other pharmaceutical
project, since our data consists of mostly generalisable industry data.
1.2 Delimitations
Before proceeding, we find it important to state the delimitations in the thesis. A clear
explanation of these will help narrow the otherwise large field of study and give the reader a
more precise indication of, where the focus of the thesis is.
Of the many traditional valuation models used in corporate finance, we have chosen to focus
on a few specific models. We have chosen to focus on the standard Discounted Cash Flow
model (DCF), Decision Tree Analysis (DTA), and lastly Real Option Theory. The focus of the
Real Option theory is predominantly on the binomial- and quadranomial lattice models rather
than formula based methods, since these are the most used methods in the practical literature
(Bogdan & Villiger, 2010; Mun, 2002).
The thesis focuses on the American Food and Drug Administration (FDA) regulation of the
pharmaceutical industry. The reason for this choice is mainly because of the importance and
size of the U.S. drug-market. It is the largest market for most pharmaceutical drugs. In the
European Union, the European Medicines Agency (EMA) conducts regulation and approval of
new drugs. In the broad perspective the requirements and regulation are similar to FDA
regulation, hence it would not make any noticeable difference to conclusions of the thesis if the
focus was any different.
In the case study there will only be a limited focus on the strategic analysis in regards to the
DCF valuation of the project. We are aware that a thorough and detailed strategic analysis is
necessary in a useful valuation, but due to the fact that the DCF is limited to a single project
and not a main part of the problem statement, the strategic analysis is reduced to a minimum.
4 Extensive design aims at elaboration, testing or generation of generalizable theoretical constructions by replicating or comparing
cases (Eriksson & Kovalainen, 2011).
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The case study takes start in one of Novo Nordisk’s pipeline products. The product is called
NN9926 and is an oral GLP-analogue focused on treating Type II diabetes. The product is
currently in Clinical Phase I. The sales forecast and cost data will be based on a literature
walkthrough of pharmaceutical industry sales and costs data.
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2 Introduction to the pharmaceutical industry
The following will introduce some very general characteristics of the pharmaceutical industry.
Further greater focus is pointed towards the drug development process, and the chapter will
serve as reference point throughout the thesis. The chapter will last discuss cost, success rates
and time in relation to the industry, which will be used in the practical implementation of
each valuation method.
2.1 Definition
First, a definition of the pharmaceutical industry will be necessary for the further analysis
and data collection. In order to understand and define, what the pharmaceutical industry is,
one must look at two closely related terms – the pharmaceutical industry and the biotech
industry. The two terms are not straightforward and are often used indiscriminately.
Therefore, it can also be fairly difficult and rather confusing to precisely classify these terms.
Some make a strict distinction, while others do not distinguish between the two terms5. In
order to understand the different terms, it is important first to look at how pharmaceutical-
and biotech firms develop their respective drugs. Biotech companies use biotechnology6 in the
drug development process, whereas conventional pharmaceutical companies predominantly
rely on chemical-based synthetic processes to develop new drugs (Ferrara, 2011). Secondly, the
size and scope of operations are often different. Pharmaceutical companies usually have the
resources and capabilities to produce new drugs at large scale and successfully market them
thereafter. This is often not the case for biotech companies, since they are generally smaller
and more specialized in the research and development process. After initial development of a
new drug they typically sell or license the rights to produce and sell that drug to a larger
pharmaceutical company (Ferrara, 2011). Another difference is related to the threat of generic
products7. Biotech firms generally face less generic competition due to the fact that it is
usually more difficult, time consuming, and costly to develop a new drug using biotechnology
than using a traditional drug development process (Ferrara, 2011).
Even though there are some clear differences between pharmaceutical and biotech firms, a
strict separation of them may not be as forthright as one should think. This is mainly because
5 For statistical purposes no separating of the terms are often used. 6 Biotechnology is the manipulation of microorganisms (such as bacteria) or biological substances (like enzymes) to perform a
specific process. 7 Generic competition: Competition from look-a-like drugs that roughly offer the same efficacy but at a much lower price.
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many larger firms, which generally are perceived as pharmaceutical firms, i.e. Pfizer, Novo
Nordisk, and Eli Lilly use biotechnology in the development of new products as well. In sense
having a biotech company “in-house”8. This fact makes a separation of the two types of firms
into two completely separate industries rather unnecessary, since the development of new
drugs face the same regulations and requirements, and most data available do not distinguish
between the two terms. A common and simple definition of the pharmaceutical industry is an
industry comprised of firms engaged in the discovery, manufacturing, and sales of drugs,
biologics, vaccines and medical devices (Ferrara, 2011). In the following sections, when the
industry is mentioned, it is based on the just stated definition of a pharmaceutical company.
2.2 Size and sales
In the United States and Europe the pharmaceutical industry plays a major role in the society
and economy. The pharmaceutical industry is the second largest US export sector and a major
employer, estimated to directly provide jobs to 655,000 people in the US. In total, directly and
indirectly, the sector supports over 3.1 million jobs nationwide in the US (Ding, Eliashberg, &
Stremersch, 2014). The concentration of global sales is another clear indication of the
importance for these regions. In 2008 the US accounted for 48 per cent of the total global
sales, while Europe accounted for 29 per cent, between them sharing roughly 80 per cent of
the total global sales of pharmaceuticals drugs (Boldrin & Levine, 2008). The US drug market
and the appertaining medical legislation of FDA thereby have a major influence on the global
pharmaceutical industry. Putting these percentages into perspective, the global sales potential
of the pharmaceutical market was in 2009 estimated to be US$ 837 billion and today
estimates go as high as US$ 1,1 trillion (Ding et al., 2014). The pharmaceutical market is
estimated to be worth close to US$ 1,6 trillion in 2020 (PWC, 2012)9. The rapidly growing and
aging world population is the main driver for the increased demand for pharmaceutical drugs.
2.3 Research & development
A unique characteristic of the pharmaceutical industry is the amount of resources allocated to
Research and Development hereafter (R&D). Some estimates indicate that pharmaceutical
firms are accountable for 19 per cent of all R&D spending worldwide and that US
pharmaceutical R&D spending alone make up 36 per cent of the total pharmaceutical R&D in
8 In-house meaning that a part of the company is doing research by using biotechnology and combining the two approaches. 9 See appendix 5 for industry forecast.
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the world (Ding et al., 2014). These figures give a clear picture of an industry that is heavily
engaged in R&D processes but also indications of an industry that is extremely reliable on the
R&D process. According to the PhRMA, the Pharmaceutical Research and Manufacturers of
America, members of the organisation currently have close to 3,000 drugs in different stages
of development in their pipelines. Of the 3,000 different drugs under development naturally
most of pipeline-drugs lay within the groups that have some of the largest sales in the US. The
top three categories are oncologics, respiratory agents, anti-diabetics and lipid regulators10
(Ding et al., 2014). Industry measures on cost, time, and success rates of drug development
will be presented later in this chapter.
2.4 Patents
Innovation and R&D is undoubtedly connected with patents and intellectual property rights.
The close connection between the two is fairly obvious, when looking at the resources
pharmaceutical companies allocate to drug development and the threat from generics.
Pharmaceutical companies are of course then dependent on the protection that patents offer.
This thesis will not go in to detail about different patent-systems and their mechanics, but
instead focus on some few specifics about the industry. Pharmaceutical companies are, as
other industries, protected by 20 years of patent protection but beyond that there are some
specific characteristics about the industry (Clift, 2008). Unlike other R&D-intensive
industries, pharmaceutical firms do not have the option first to disclose their findings at the
final stage of development, but have to reveal their discovery very early in the development.
This is mainly due to requirements from government agencies and the fact that much
development involves human trails, as will be elaborated later (Lehman, 2003). The lengthy
R&D time period, which is also presented in the following sections, also constitute a special
case for the pharmaceutical industry. The time between filing the patent and releasing the
product to the market reduces that exclusivity a patent otherwise provides. In 1984 the US
government introduced a special act called the Hatch-Waxman Act, which enabled the
extension of a pharmaceutical patent with up to five years (Clift, 2008). The median or mean
peak sales of pharmaceutical drugs are undoubtedly connected with their patent periods.
Research suggests that the average percentage decline in sales the first four years after patent
expiry is 31, 28, 20, and 20 per cent respectively (Grabowski, Vernon, & DiMasi, 2002).
10 Lipid regulators are regulators that affect the levels of lipid, such as cholesterol and fat, in the blood.
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2.5 Drug development
In order to understand how pharmaceutical companies develop new drugs and some of the
challenges they face, the next section will describe a typical development process of a new
drug. It is worth mentioning that the process described follows the regulation of the US Food
and Drug Administrations (FDA).
“Importantly, creating new drugs in the twenty-first century is no longer a series of accidental,
serendipitous breakthroughs. Instead, a long and systematic process requiring steadfast commitment,
diligence, and meticulous work has taken the place of the previous haphazard experimentation” (Ding et
al., 2014, page 26)
In order to get an overview of the development process figure 2.1 is used to describe the
process of development in different steps. It shows in crude terms the different drug
development stages – from discovery of lead compounds through clinical phases (CP) and final
approval of the drug.
Figure 2.1: The drug development process
Source: Own creation
Discovery: The first step in the development of a new pharmaceutical drug is the study of a
disease at a molecular and cellular level. The discovery process is a complex process and
requires both the involvement of chemists and biologists. This stage is highly time-consuming
and uncertain, which results in a large amount of abandoned entities (Kellogg & Charnes,
2000). More detailed, the stage involves the analysis of basic cellular processes at both a
healthy and pathologic state, and by comparing the two different states several disease-
responsible actors are identified as possible drug targets. Before any of the discovered
compounds can be tested in a human body, several tests in vitro and in vivo, test in tubes, and
in living cells must be conducted. Furthermore, researchers must find the most appropriate
and safe dose of drug for the further tests in animals (Ding et al., 2014). Several candidate
drugs that seemed successful are often abandoned due to problems of low efficiency, toxicity,
Discovery CP I CP II CP IIINDA -
ApprovalPost
Approval
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or poor absorption (Bogdan & Villiger, 2010). Upon completion of the drug discovery process
researchers must prepare for the more critical stages in the innovation process – drug
development through clinical trials on humans. Before the candidate drug can be tested on
humans, the researchers must hand in an Investigational New Drug application (IND) to the
national drug agency. The application must include all evidence of the previous steps and
must proof that the candidate drug fulfils all present requirements and standards.
Clinical phases: In clinical phase I (CP I) the candidate drug is tested in humans for the first
time, and the study is usually conducted on a group of volunteers of 20 to 100 healthy persons
(Bogdan & Villiger, 2010). The test is conducted to establish a more precise dosage and to
gather more documentation about the absorption, distribution, embolic, and excretion effect of
the human body. Short-term side-effects are studied and the desired effects of the drug are
compared to the established treatments to determine if the drug provides a better alternative
(DiMasi & Paquette, 2004; Ding et al., 2014). If this is fulfilled, then it will move on to clinical
phase II.
In phase II a larger group of 100-300 people having the target disease or condition are tested.
The purpose is to define the most appropriate dose and further to prove the effectiveness of
the drug. The drug must proof its effectiveness, since the tested group of people in phase I
were healthy people (Bogdan & Villiger, 2010). Researchers strive to understand if the drug
has good efficacy, and whether the drug has any short-term side effects. Further, the drug
must prove a clear benefit over existing treatments in terms of efficacy, safety, and delivery
(Bogdan & Villiger, 2010; DiMasi & Paquette, 2004).
If the clinical phase II of the drug is successful, it is taking forward to a large-scale test of 500-
20.000 patients having the target disease, the clinical phase III. A large and diversified group
of people, often from different nations, are included as a diversified test group is necessary for
the following studies (Bogdan & Villiger, 2010). The aim of the large-scale phase III is to
confirm and significantly prove the effectiveness of the treatment under different conditions.
For establishing a significant evidence of efficacy and safety, the drug must be comparatively
tested against different placebo options and other standard treatments. If the drug succeeds in
being safe and effective the pharmaceutical company can file a New Drug Application (NDA)
to the drug agency requesting approval (Ding et al., 2014).
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NDA: The NDA must include all data, documentation, and statistics of the testing and proof of
the safety and efficacy of the drug. The approval must also include proposals for specific
labelling and manufacturing. A NDA may consist of more than 100.00 pages of data and
evidence. According to Bogdan & Villiger (2010) it is very likely that the regulatory authority
asks for further clinical trials or even rejects the marketing approval.
Post approval: After the pharmaceutical drug has been approved by the drug agency and sold
to the market, the test and research process has to continue. Pharmaceutical companies must
continue to monitor and observe carefully for newly found adverse and long-term side-effects
(Bogdan & Villiger, 2010). The company completes periodic reports to the drug agency on
quarterly basis the first three years and annually afterwards (Ding et al., 2014).
To sum-up the process of developing a new pharmaceutical drug, it is a complicated time-
demanding process affected by high uncertainty in each development stage. The next section
will present a litterateur walkthrough of some of the latest research within this field.
2.6 Success rates, cost, and time
The two previous sections have introduced the pharmaceutical industry and explained the
clinical phases of pharmaceutical drug development. The following section will investigate and
discuss the available data of clinical success rates, R&D costs, and time effort in developing
new drugs. This section is essential for the thesis, since these findings will be the source of the
industry data used in the later case study. Each section will start with a list of relevant
authors and continue with an explanation of these and conclude with a table that summarises
the findings.
From our research, we find that only few studies have investigated costs and success rates of
the pharmaceutical industry and that they differ significantly. The literature is further
complicated by the reluctance of the industry to publish confidential industry data of drug
development. Most data in the literature is derived from the Tufts Centre for the Study of
Drug Development (CSDD), where data is provided by big unnamed pharmaceutical
companies (Bogdan & Villiger, 2010). According to Light & Warburton (2011), the CSDD has
received substantial industry funding for years and is a repository, where companies submit
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their closely guarded figures on R&D. As several of the following studies are based on data
from CSDD, the studies of cost estimates are critically examined.
2.6.1 Success rates
The definition of the success rate is the chance that a pipeline-drug entering a development
phase reaches the next phase (Bogdan & Villiger, 2010; Ding et al., 2014). In the following the
average cumulative success rate is defined as the success rate from “first in man” to
marketing of the drug (Kola & Landis, 2004). The publication by Ding et al. (2014) advocates
that success rates are complex to understand and interpret upon, as the success rates
associated with passing each stage are different among different drug candidates. Meaning,
that probabilities of success vary quite substantially within different pharmaceutical classes.
Some of the most cited authors in the literature and industry are the publications by DiMasi
& Grabowski (2007) and DiMasi, Feldman, & Wilson (2010), who both are associated with the
before mentioned CSDD. The publication from DiMasi & Grabowski (2007) finds the success
rates of each clinical phase to be the following; CP I 71 per cent, CP II 44,2 per cent and CP III
68,5 per cent. Based on these, the clinical cumulative success rates is 21,5 per cent that a new
drug would be able to reach the market.
The later publication by DiMasi et al. (2010) studied a larger sample of drugs. The sample
consisted of 1.738 different compounds, from the 50 largest pharmaceutical companies in the
US11. They find that approximately one in six or 17 per cent of all new drugs were approved in
the period of 1994-2009. Their results show that failures occurred earlier in the clinical
phases. The success rate in each clinical phase was; CP I 67 per cent, CP II 41 per cent, and
CP III 63 per cent, and 90 per cent for approval.
The most recent study on pharmaceutical drug development success rates is an article
published by DiMasi et al. (2014) on behalf of CSDD. The study present the following success
rates; CP I 59,52 per cent, CP II 35,52 per cent, CP III 61,95 per cent, and last the approval
phase 90,35 per cent. The cumulative success rate is 11,83 per cent, which is much lower,
compared to the previous studies. Compared to the two previously mentioned studies the
11 The size was measured in sales numbers
Page 14 of 92
success rate of clinical phase I and II is substantially lower. The above results are presented in
table 2.1.
Table 2.1: Clinical success rates
Discovery CP I CP II CP III NDA Total Authors / Succes per cent
DiMasi & Grabowski, 2007 71% 44,2% 68,5% - 21,5 %
DiMasi et al., 2010 67% 41% 63% 90% 15,58%
DiMasi et al., 2014 59,52% 35,52% 61,95% 90,35% 11,83%
Source: Own creation
As seen in table 2.1, the studies of success rates show some different results. The differences
in percentage are caused by the different measurements, pharmaceutical classes or other
statistical estimations used by the authors. Given that the approval phase is not a part of the
total success rate in DiMasi & Grabowski (2007) the estimations is higher compared to the two
other studies. If we adjust the estimate with the approval phase from DiMasi et al. (2010) and
DiMasi et al. (2014), which is around 90 per cent, we estimate a total success rate of 19,3 per
cent.
2.6.2 Costs
Several studies have analysed the development process of the pharmaceutical industry but
only few of them have studied the costs of the R&D process. A significant variation in the cost
estimations among the studies complicates the findings. Through the literature we find that
drug development costs range from US $75mio to $4billion dollars (PWC, 2012). Most studies
lean towards the higher end of the range, as seen in the table below. The significant cost
variation makes it necessary to examine the following literature critically, as mentioned in the
introduction.
The significant cost variation can often be explained by two components (Ding et al., 2014).
The first component is a question of the definition of R&D costs. As just explained in the
previous section on success rates, the pharmaceutical industry is influenced by low success
rates meaning that several drugs are abandoned before a successful drug is discovered and
developed. Some argue that the R&D cost should include cost of both the successful and the
Page 15 of 92
abandoned drugs, while others do not. The second component is the allocation of opportunity
cost due to the long time horizon of the development process. The average time of a drug
development is 12 years and the opportunity cost thus has a significant influence on the total
cost allocation (Ding et al., 2014). Both components will be explained more in the following
examples.
Similar to the literature on success rates the publication by DiMasi & Grabowski (2007) is one
of the most cited by the industry. The authors estimated in 2007 an average out-of-pocket cost
per new drug of US$672 million and after capitalising that at an opportunity cost of 11 per
cent the total pre-approval cost was estimated to $1318 million dollars.
The paper by Light & Warburton (2011) criticises these estimates and argues that since none
of the drugs were titled or specified in therapeutic classes, it is not possible to verify these
results. The authors further argue that the estimates do not include any R&D tax
adjustments, and that they should be based on median numbers rather than a mean, which
decrease the influence of extreme outliers. Most criticised is the allocation of 11 per cent
opportunity cost, which doubles the total cost estimations from $672 million to $1318 million
dollars. Even if one accept the use of opportunity costs, US government guidelines call for
using 3 per cent and not 11 per cent opportunity cost. As a part of their critic, they calculate
their own cost estimations based on the data of DiMasi & Grabowski (2007) and found a
median cost ranging from US$180-231 million dollars.
In 2012 Price Waterhouse Cooper estimated the development cost of an average
pharmaceutical company (PWC, 2012). They found a significant variation in costs depending
on therapeutic classes similar to other studies. Based on average costs and average attrition
rates in each phase of the R&D process, the cost of the R&D process was estimated to be
around US$701 million dollars per pharmaceutical product. More detailed numbers for each
R&D process were given as; preclinical and development $87 million, CP I for $130 million, CP
II for $190 million, CP III for $268 million, and lastly $26 million dollars in approval. The
publication did not include the preceding calculations for the estimations and therefore not
possible to discuss further.
The 2010 publication by Bogdan & Villiger (2010) estimated the cost of each clinical phase in a
small and medium sized pharmaceutical company. Compared to the other literature, the
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estimations by Bogdan & Villiger (2010) provide some of the lowest cost estimates. This is
mainly due to the fact that the estimations are made for smaller companies. More essential
questions of both opportunity cost and failure costs are not clarified, and the reader must
assume that both components have been left out. This could also explain the low cost
measures in each phase of development. The total average cost accounts for: research and
discovery US$ 4-6 million, CP I for $1-5 million, CP II for $3-11 million, CP III for $10-60
million, and last the approval phase for $2-4 million dollars. The maximum total cost denotes
to $86 million dollars, which is incredible low compared to the other findings.
The latest report by CSDD DiMasi et al. (2014) estimates the average capitalised cost to be
US$2.2558 million dollars, allocated as $1.098 million cost in pre-human and US$1.460
million dollars in clinical testing. The cost estimate is calculated using a 11,4 per cent
opportunity cost, which almost doubled the out-of-pocket costs from US$ 1.395 million to 2.558
million dollars. The above results are presented in table 2.2.
Table 2.2: Cost of drug development
Discovery CP I CP II CP III NDA Total Authors / Costs million dollars
DiMasi & Grabowski, 2007* 150 522 672
DiMasi & Grabowski, 2007 439 879 1318
Light & Warburton, 2011 180/231
PWC, 2012 87 130 190 268 26 701
Bogdan & Villiger, 2010 6 5 11 60 4 86
DiMasi et al., 2014 1098 1.460 2.558
Source: Own creation
*Out-of-pocket costs not capitalised (DiMasi & Grabowski, 2007)
As seen in table 2.2 the different cost estimates differ quite substantially. Cost figures range
from US$ 86-2.558 million. When taking the different methods of approach and component
assumptions in each of the studies into consideration, the variation may not be as surprising
as first noticed.
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2.6.3 Time
According to Ding et al. (2014) a successful pharmaceutical company must be able to balance
returns, uncertainty, and not least time. The majority of the income of a pharmaceutical firm
is generated from drugs with patent protection, which will fade out as soon the patent ends
(Ding et al., 2014). The time of research and development has a significant impact on earnings
in the pharmaceutical industry, since a long development process reduces time of patent
protection (as mentioned earlier on page 9).
In 2010 Bogdan & Villiger (2010) found that small and medium sized pharmaceutical
companies had an average development time of; discovery and research 30-42 month, CP I for
18-22 month, CP II for 24-28 month, CP III for 28-32 month, and approval for 16-20 month.
The maximum length of the R&D process is 12 years, which is similar to Ding et al. (2014) and
a minimum time of nine and a half year.
The latest publication by the CSDD DiMasi et al. (2014), which is based on ten unknown
pharmaceutical and 106 new drugs in the US, estimates the following time length of the total
development process; discovery 31,2 month, CP I 19,8 month, CP II 30,3 month, CP III 30,7,
and last approval 16 month. The total time length from discovery to approval was observed to
be 10,6 years (DiMasi et al., 2014).
In 2011 Kaitlin & DiMasi (2011) completed a study on the length of clinical phases and the
approval time on new drugs in the US. The data was gathered using the before mentioned
CSDD database from the period 1980 to 2009. They grouped the data in to brackets of five-
year periods to see the development in clinical phase- and approval time. The earlier results
from their study seem less relevant for this thesis, why only the newest results will be
presented here. They did not distinguish between CP I to III but grouped them in one
category, but they had separate data for the approval time. The total clinical phase time was
in the last period of data, 2005-2009, 76,8 month, while the approval time comprised to 14,4
months. The total time from first in man testing (clinical phase I) to approval was 91,2 months
(7,6 years).
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Table 2.3: Clinical time length
Discovery CP I CP II CP III NDA Total Authors / Time in months
Ding et al., 2014 12 yr.
Bogdan & Villiger, 2010 30-42 18-22 24-28 28-32 16-22 12 yr.
DiMasi et al., 2014 31,2 19,8 30,3 30,7 16 10,6 yr
Kaitlin & DiMasi, 2011 76,8 14,4 7,6y r.
Source: Own creation
As illustrated in table 2.3, the variation in time length is rather low. From the above, we find
the typical time length of a drug development to be 7,6 to 12 years. It is worth noticing that
the publication by Kaitlin & DiMasi (2011) finds a shorter period of drug development. This is
mainly because the discovery phase is not included in the development period. Furthermore,
the study does not divide the clinical phases in to separate phases, which makes it less usable
and expressive.
2.7 Sum-up
This chapter has presented some of the main characteristics of the pharmaceutical industry.
Characteristics that give an indication of an industry that generally face high uncertainty and
risk. From complex patent legislation to the very risky processes and clinical phases
pharmaceutical companies go through in the development new drugs. Combining this with the
just presented actual industry measures on R&D-cost, drug development success-rates, and
time needed to develop a new approved drug, it is clear that valuation of pipeline-projects
within this industry calls for much consideration and attention. Do the well-known
fundamental valuation methods suit the characteristics of the industry or are other valuation
methods more suitable when valuating projects within this industry? During the theoretical
discussion of different valuation methods, continuous references will be made to this chapter’s
description of the characteristics of the pharmaceutical industry.
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3 Valuation methods in the pharmaceutical industry
The previous sections have introduced the pharmaceutical industry and some few significant
characteristics of it. To answer the problem statement, this section will analyse and discuss
some of the theoretical differences between fundamental valuation models and the more
complex real options valuation methods and their underlying assumptions. Each valuation
method will be theoretically discussed and evaluated upon four defined criteria. The potential
of each valuation methods will be evaluated after the practical implementation.
The decisions of financial analysts in corporate finance, in a very simplistic way, can be
divided into two overall decisions. First one is the investment decision, which is choosing the
projects that have positive NPV. Second one is the finance decision, which is deciding on how
to finance the selected projects. All decisions have the overall goal of maximizing the market
value of a given portfolio of investments. These decisions are undoubtedly connected with the
strategy a company pursues. And in an increasingly uncertain global marketplace, strategy
and strategic flexibility are becoming more important for firms in order to capture the
advantage of future opportunities, and limit losses of any unfavourable developments (Smit &
Trigeorgis, 2004). Having the previous chapter in mind, it is clear that the pharmaceutical
industry to a high degree face such challenges. Therefore, the purpose of this chapter is to
present, analyse, and discuss possible project-valuation methods for the pharmaceutical
industry in order to continue with an evaluation of the practical implementation through a
case study and finally make some statements regarding the optimal method to use.
As mentioned in the introduction, the perspective of view is of financial analysts, meaning it is
an outside-in perspective. Also, as mentioned earlier several studies have previously found
that financial analysts favour the present value approach and that the real options approach
is hardly ever used (Block, 2007; C. V. Petersen & Plenborg, 2012). And, this is where the
thesis has its merits – an investigation of which valuation methods are most suitable in theory
and practice when valuing pharmaceutical drug development projects.
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3.1 The Criteria
To carry out the discussion of the different valuation methods, we have constructed four
criteria to evaluate them. Each criterion will be explained in depth in the following section.
The four criteria are chosen to deepen the overall evaluation and improve the understanding
and the analysis of the different assumptions, limitations, and uncertainty factors of the
valuation methods. The criteria, which are seen in figure 3.1, touch upon what previously has
been characterised as fundamental requirements and cosmetic requirements (Plenborg, 2000).
The first, fundamental requirement, relates to whether a valuation model gives an unbiased
and realistic result, while the cosmetic requirements relates to the intuitive and
understandable use of the methods. The fundamental requirements always dominate the
cosmetic requirements, as the opposite may lead to irrational investment behaviour, which of
course is not preferable under any circumstances (Plenborg, 2000). The importance of the
cosmetic requirements should not be underestimated though, as a precise and unbiased
valuation method could be abandoned because of its cosmetic complexity. A later publication
by C. V. Petersen & Plenborg (2012) describes somewhat similar requirements to valuation
models. The authors use terms like value attributes and user attributes corresponding to the
fundamental and cosmetic requirements.
Figure 3.1: Criteria for evaluation of valuation methods
Source: Own creation
Inspired by this, we have created the four criteria above. Concept and Uncertainty are
influenced by the fundamental requirement, while the two last criteria are inspired by the
Criteria to evaluate valuation methods upon
Fundamental
requirements
Cosmetic
requirements
Concept
Strategic flexibilityUncertainty
Usability
ValuationModel
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cosmetic requirement. In the following section, each criterion will be explained and applied to
each valuation method.
3.1.1 Concept
In order to facilitate a discussion and be able to identify differences in valuation methods we
construct the concept criterion. The concept criterion is used for assessing basic conceptual
differences in the valuation methods. This will provide the fundamental insight in the
different models and make it possible to comment on general differences. This description of
the concept criterion corresponds to the fundamental requirement.
3.1.2 Uncertainty
This criterion is used to evaluate the perception and measure of uncertainty in each valuation
method. The criterion furthermore investigates which kind of uncertainty parameters the
valuation models include, and how they are affected. Additionally some of the underlying
assumptions of the models will be touched upon and discussed. This criterion is assumed to be
one of the most essential, as it affects the practical implementation and not least the value
provided by the different models. Again this criterion relates to the previously explained
fundamental requirement.
3.1.3 Strategic flexibility
The strategic flexibility criterion examines whether the valuation methods are able to
incorporate events in the future. Events are defined as what happens when new information
becomes available. Strategic flexibility is defined as the strategic decision for managers to
either maximize or minimize the influence of future events.
The previous chapter has given the picture that the pharmaceutical drug development is a
process involved with great uncertainty and high cost. This fact makes it highly relevant to
evaluate the different valuations methods ability to incorporate strategic flexibility because
this flexibility would intuitively add a substantial value to a project. Strategic flexibility is
related to the cosmetic requirement, since it makes it possible for the user to understand and
perceive flexibility through the way, it is cosmetically modelled.
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3.1.4 Usability
The usability criterion helps to evaluate how theoretically usable and user friendly the
different valuation methods are. The criterion will touch upon several aspects. The
intuitiveness of the results and by that also complexity of the methods is considered. In the
walkthrough of the different valuation methods usability will only briefly be touched, as it is
only theoretical usability, which the criterion focus on.
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3.2 Standard DCF model
In the following, the standard discounted cash flow model (DCF) will be shortly discussed
based on the four mentioned criteria. Assumed that the DCF model is more or less well known
by the reader, it will only be shortly evaluated. The model is one of the most widely accepted
models (Bancel & Mittoo, 2014; Mun, 2002).
3.2.1 Concept
The DCF model is based on the income approach to valuation. The income approach looks at
the free-cash-flow potential of an asset and quantifies, forecast and discount these cash flows
to a present value (Mun, 2002). Other valuation models are based on these principles as well,
but the most common and widely accepted valuation model is the standard DCF model, which
is depicted in figure 3.2. In the following, the model will be referred to simply as the DCF
model. As expressed in the formula, the model calculates the net present value of an
investment based on its potential to generate future cash flows, and then adjusting for the
weighted average cost of capital of debt and equity (WACC).
Figure 3.2: Standard discounted cash flow model
Standard discounted cash flow model
Net Present Value = I0 + ∑FCFt
(1 + 𝑊𝐴𝐶𝐶)𝑡
∞
t=1
I0: Initial investment
FCF: Free cash flow
WACC: Weighted average cost of capital
t: Time
Source: Own creation and (Koller, Goedhart, & Wessels, 2010)
As shown in figure 3.2, the model consists of four inputs where the initial investment and time
are known, and the free cash flow and weighted average cost of capital are estimated inputs.
The free-cash-flow is estimated as earnings after tax, less capital expenditure, less net
working capital, plus depreciations (Koller et al., 2010). The discount rate is estimated as the
weighted average of cost of capital of both debt and equity.
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3.2.2 Uncertainty parameters
The DCF model relies on some strong assumptions and limitations. The DCF model perceives
uncertainty as risk implying that more uncertainty increases the amount of risk in a specific
project and decreases the net present value. In the DCF model, all risk is completely
accounted for in the discount factor, which makes it a central and critical parameter in the
DCF model (Mun, 2002). In the following, only the cost of equity will be discussed in relation
to uncertainty, since the later case study does not consider debt financing, which will be
explained later. There are several methods to estimate the cost of equity. The most common
model is the capital asset pricing model (CAPM). The fundamental assumption of CAPM is the
law of one price, which states that in a competitive market, investments with similar risk
should have the same expected return. Investors can thereby eliminate firm-specific risk by
diversifying their own portfolios and only hold systematic risk (Berk & DeMarzo, 2014).
CAPM relies on the assumption that all investors have perfect information and are able to
hold a well-diversified market portfolio. The CAPM equation expressed in figure 3.3 is a linear
relationship between the risk free rate, beta, and the market risk premium.
Figure 3.3: Cost of equity
Cost of equity
Equity cost of capital = 𝑟𝑓 + 𝛽(𝐸(𝑅𝑚) − 𝑟𝑓)
rf: Risk free rate
𝛽: Beta
E(Rm)-rf: Market risk premium
Source: Own creation and (Koller et al., 2010)
Out of the four input factors, only beta is individually defined for each specific investment.
The risk free rate and the market risk premium are not defined individually for each
investment but are depending on the industry or sector. This causes the beta to be the factor
exposed to most uncertainty and thereby more difficult to estimate. Beta is defined as a
sensitivity factor measuring the expected percentage change in the excess return of a security,
relative to the change in the excess return of the market (Berk & DeMarzo, 2014). In order to
be able to estimate a reliable measure of beta, it is for example necessary to use average or
median long-term equity prices to avoid short-term fluctuations. Further, for non-traded
physical assets like pharmaceutical products it is difficult to estimate beta. The argument of
using a company’s beta is not adequate, as project risk may differ from the company risk.
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3.2.3 Strategic Flexibility
The standard DCF model can only in a very limited way incorporate future events. Through
scenario- and sensitivity analysis one can for example analyse the effect of changes in the
input variables. These considerations typically involve a worst- and best-case scenario and/or
calculations on how different parameters affect the DCF valuation, such as the discount rate,
growth rate etc. Although the standard DCF model can incorporate these events through the
case-analysis just mentioned, it does not capture the actual value of having the opportunity to
react to these events. Scenario-analysis only takes into account one path of uncertainty,
whereas in for example Real Option Analysis all future paths are considered (Triantis, 2005).
It is not possible for the standard DCF model to incorporate this strategic flexibility value
because of the model’s passive management assumption (Mun, 2002: 59). Expressed in other
words, the standard DCF model makes decisions based on today’s expectation of future
information (Koller et al., 2010). However, in the development of pharmaceutical drugs, this is
not the case. Usually a project is actively managed through the projects life cycle, through
either increased commitment or a reduction of exposure, such as budget constraints. The
standard DCF model is therefore not recommended in situations, where active management is
highly necessary in order to maximize project value. Meaning, that using a deterministic
model as the DCF in a stochastic world, may underestimate the value of projects grossly (Mun,
2002: 58).
In the context of the pharmaceutical industry one could argue that it is an industry that does
have a high need for active management. A clear example of that are the mentioned phases in
drug development. The need for active management is somewhat obvious since the value of a
project is heavily reliant on specific project events.
3.2.4 Usability
The standard DCF model is generally perceived as rather user friendly and easy to apply in
relation to theoretical usability. It is one of the most widely used valuation method regardless
of what industry context you operate within (Bancel & Mittoo, 2014). In general, the
estimation of input is perceived as having high usability, but this may be because of the
experience of using the model, rather than actual ease in estimating the input. This will be
elaborated later, when performing the case study. The DCF model is not complicated to use,
and the time effort needed for the actual valuation calculations are not demanding. However,
in some cases estimation of input can be rather cumbersome and time demanding, often
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depending on the level of detail needed. The intuitive appeal is clear though and the outcome
of the model is easy communicated through an organisation. What is to be communicated is if
benefits outweigh cost and NPV is positive (Berk & DeMarzo, 2014).
The below figure 3.4 is a summary of the main findings in each of the four criteria of the DCF
model.
Figure 3.4: Summary of DCF
Source: Own creation
- Valuates the FCF potential of an asset - Perceives uncertainty as risk through beta
- Discounts FCF at WACC or cost of equity - Difficult estimation of beta and the CAPM assumptions
- More uncertainty decreases project value
- Based on today's expectations of the future - Uncomplicated to perform
- Low flexibility through sensitivity analyses of input and result - Well-known and simple to communicate
- Low flexibility due to passive management assumption
UncertaintyConcept
Strategic flexibility Usability
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3.3 Decision Tree Analysis
One alternative valuation method is the Decision Tree Analysis. In the following this method
will be analysed through the four criteria.
3.3.1 Concept
Decision Tree Analysis (DTA) is a relatively simple model that can be used to evaluate options
and is often mentioned to be able to include the value of managerial flexibility. DTA is based
on discounting contingent12 payoffs and investment with appropriate discount rates (Koller et
al., 2010). However, instead of simply discounting a single line of payoffs as in the DCF model,
the DTA model constructs a tree consisting of events and decisions as seen in figure 3.5. The
DTA approach is sometimes referred to as a contingent valuation approach (Koller et al.,
2010).
Figure 3.5: Example of a decision tree
Source: Own creation
As seen in figure 3.5, calculating the value in each decisions node from right to left until
reaching time zero, solves the decision tree as the one above. Based on the specific
probabilities of something occurring in each of the events, the end value of the decision tree
can be calculated as a weighted average of the different outcomes discounted with appropriate
discount rates for respectively cash flows and investments. The probability of something
occurring can in the context of the pharmaceutical industry be translated into technological
12 Contingent payoffs are payoffs that are uncertain and dependent on certain conditions (Koller, Goedhart, & Wessels, 2010).
Decision tree
: Technological risk event Invest: Decision event
Invest
Invest
Succes
DTA
Failure
Page 28 of 92
uncertainty, which again can be translated into the success rates in each of the clinical
phases. One of the difficulties in using DTA lay within finding the appropriate discount rate
and the specific probabilities for each of the decision nodes, which is elaborated below (Smit &
Trigeorgis, 2004).
3.3.2 Uncertainty parameters
The DTA model perceives uncertainty as both risk and opportunities. Risk as the occurrence of
something undesirable (success) and opportunities as something desirable (failure). This can
be seen as the up and down arrows in figure 3.5. This stands in contrast to the standard DCF
model explained above, which only perceived uncertainty as risk – something that decreases
the value of a project. Since it is now assumed that management can make decisions regarding
the project along the way, and make decisions that optimizes the value of the project, the DTA
value will provide a greater value than a standard DCF valuation of the same project.
The contingent cash flows and investment should, as mentioned before, not be discounted with
the same rate. Otherwise, the DTA is likely to overestimate the value of flexibility and
ultimately the value of a project (Koller et al., 2010). Investment outlays are certain (private
risk) and they should then be discounted at a risk-free rate (Koller et al., 2010), while the
payoffs (cash flows) should be discounted at a market-adjusted discount rate, such as WACC,
since market risk is compensated and not the private risk (Mun, 2002). It is often difficult to
find the appropriate discount rate for the tree as a whole, because the discount rate changes
through time as the risk in the project also changes (Lander & Pinches, 1998). Furthermore,
there is no direct way of determining the appropriate discount rate for the tree other than
deriving it from alternative real option methods (Koller et al., 2010).
Another parameter of uncertainty concerns estimating the probabilities used in each of the
events (the technological uncertainty). The probabilities in each of the events must be known
and are specific to each project and event. In many cases it can be somewhat difficult to
reliably and consistently estimate these probabilities, which complicates and make DTA. On
the other hand, in cases where it is reasonable easy to derive these probabilities and use them
confidently, DTA may be the better choice.
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3.3.3 Strategic flexibility
The DTA model, compared to the more static standard DCF model, incorporates future events
and strategic flexibility. Through the decision tree, the DTA model incorporates future events
and decisions, which are the main differences from the standard DCF model (Smit &
Trigeorgis, 2004), and also what drives the uncertainty. The incorporation of future events
implies that the model is not simply based on today’s future expectations, but makes it
possible for managers to make decisions based on new information as time progress. The
decision-based approach of the DTA model makes it possible for managers to maximise a
project’s value based on future events. Thus theoretically creating more strategic flexibility to
managers or in other terms it helps “bridging the gap between strategy and finance” (Skjødt,
2001). The model is therefore also theoretically more suitable in situations where active
management is highly necessary in order to maximise project value. In the context of the
pharmaceutical industry the DTA model has some interesting valuation aspects, as the model
incorporates future strategic flexibility, which is relevant in the context of the different
clinical phases.
3.3.4 Usability
The DTA model has a lower degree of theoretical usability compared to the standard DCF
model, as the uncertainty parameters are more complex to estimate. The usability of the DTA
depends on, whether the input parameters such as discount rates and event probabilities can
be estimated accurate in each event node. Subjective probabilities of technological uncertainty
and discount rates are required in each event note, which complicates the estimations and
may result in wrong outcomes (Mun, 2002). As mentioned above, finding the appropriate
discount rate along each branch at different times is not an easy task (Smit & Trigeorgis,
2004). The complexity of the input parameters is further depending on the number of events
in the model, as the risk of estimation errors increases when managers have various events to
analyse. The DTA is evaluated to have a medium complexity of use taking the difficult input
parameters into consideration. The outcome of the model is easy to communicate and more or
less intuitive for practitioners, who are already familiar to the DCF model. Using DTA in
project valuation is evaluated to require more time and more resources.
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The below figure 3.6 is a summary of the main findings in each of the four criteria.
Figure 3.6: Summary of DTA
Source: Own creation
- Decision tree consisting of future events and decisions - Perceives uncertainty as both risk and opportunities
- Discounts contingent cash flows with appropriate rates - Difficult to determine correct discount rates
- Probabilities as clinical success rates - Requires knowledge of probabilities of future events
- The model captures future events through a decision tree - Requires more theoretical knowledge than the DCF
- Incorporates flexibility in the ability to act upon future events - Medium theoretical complexity
- Flexibility through probabilities i.e. technological uncertainty - Depends on the number of future events modelled
Strategic flexibility Usability
Concept Uncertainty
Page 31 of 92
3.4 Real Options
The following section will provide an introduction to the Real Options approach and evaluate
both the binomial and the quadranomial lattice model upon each of the stated criteria. First of
all, a brief introduction to financial options is presented, followed by real option theory, since
the Real Option approach is based on the basic concept of financial options.
3.4.1 Concept
Before the evaluation of Real Options upon the concept criteria, the basic concepts of financial
options are introduced. Financial options are options on financial assets such as shares of
stock, bonds, or tradable commodities. The basic in a financial option is that the option holder
has the right but not the legal obligation to either sell (put) or buy (call) an underlying asset
(S) at a pre-specified strike -or exercise price (K). If the option holder has the opportunity to
exercise the option before the maturity date (t), the option is known as an American option.
Otherwise, if the option can only be exercised at maturity, it is then known as an European
option. In general, there are four possible positions to take: buyer of a call, the seller/writer of
a call, the buyer of a put, and the seller/writer of a put (Mun, 2002), and for every position one
can take, there is a counter position. Meaning, that for every option owner (long position),
there must be an option seller/writer (short position) as well.
The payoff of an option on a stock is determined by the price of the underlying stock when
exercised. For call options, if the stock price (S) is greater than the strike price (K) it will be
exercised with profit. Since, the option holder can buy the stock from the option writer at the
pre-determined strike price (K) and sell it in the market to the higher price. If the stock price
(S) is less than the strike price (K), the option will then not be exercised. The contrary is the
case for put options. If the stock price is below the strike price, the option will be exercised,
since the holder will receive the strike price, while the stock is only worth the lower stock price
(Berk & DeMarzo, 2014). This can be seen in the figure below. The values are known as the
formula value or intrinsic value of an option.
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Figure 3.7: The intrinsic value of options
The Intrinsic Value of Options
Call Value: Put Value:
C = max(S-K;0) P = max(K-S;0)
Source: Own creation based on (Berk & DeMarzo, 2014)
The payoff structure of the four just mentioned possible positions are shown in the below
figure. It shows the asymmetric payoff in the options. A long position in an option will capture
only the upside and never have a value below zero.
Figure 3.8: Payoff positions
Source: Own creation based on (Berk & DeMarzo, 2014).
The value of an option is in general driven by three major factors. The underlying stock price,
as mentioned above, is the first and most apparent. If the exercise price is kept constant, the
movement in the underlying asset drives a large part of the option value (intrinsic value).
The second is time to maturity. The longer time the option has left, the more time there is for
the option to get in the money. Hence, the longer time to maturity, the higher the option value
Long call and put option
Value Call option Value Put option
Share price Shareprice
Short call and put option
Value Short Call Value Short put
Share price Shareprice
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is (time value). The last factor is the volatility of the underlying. Higher volatility means
higher option value. Higher volatility implies that the options have a greater chance of getting
in the money before it expires (Brealey, Myers, & Allen, 2008).
There are a variety of different methodologies and approaches used to calculate the actual
value/price of a financial option. They range from closed-form equations such as the Black-
Scholes model, simulation models like Monte-Carlo Simulation, lattices (binomial and other
multinomial trees), or using finite differences such as partial-differentiated equations
(Bogdan & Villiger, 2010; Mun, 2002). Each method should in theory provide the same
numerical output, which makes the decision on which method to use more a question of
methodology and appropriateness of the problem at hand, than the result of the valuation.
When valuing financial options, there is often no need for explicit explanation of the valuation-
process, more on the actual result, why closed-form solutions are the most frequently used
(Brandão, Dyer, & Hahn, 2005; Hartmann & Hassan, 2006; Kellogg & Charnes, 2000)
Real options, as the name imply, use option theory to value physical or real assets, opposite to
financial assets (Mun, 2002). Real Options have several similarities to financial options,
because the holder of a Real Option also has the right, but not the obligation, to exercise the
option. Some of the key differences between the input to financial and real options are listed in
table 3.1
Table 3.1: Difference between financial and real options
Variables
Financial options Real Options
Value of underlying asset S Stock price - financial asset Present value of project
Time to maturity t Fixed in contract Variable
Exercise price K Fixed in contract Cost of realizing option
Volatility σ Volatility of the stock -increases Volatility of the project - increases
Option price Price fixed by financial markets Not fixed – often negotiable
Control of the option value No control Possibility to affect option-value
Liquidity of the option Tradable in financial markets Most often not tradable
Rationality in exercising Mostly rational Affected by circumstances
Source: Own creation inspired by (Kodukula, 2006)
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The main differences are described in table 3.1. Some of the most interesting differences are
the liquidity, control of option value, and value of the underlying asset. A key distinction
between these two option approaches is that real options and the underlying assets are rarely
traded in competitive markets (Berk & DeMarzo, 2014). Relating to the pharmaceutical
industry, the exercise price is assumed to be equivalent to the R&D cost, and it is apparent
that pharmaceutical drug development projects are not traded. With these differences in
mind, we continue our investigating of the Real Option theory.
As mentioned in the introduction chapter the focus in the thesis is on a few specific valuation
models. The latter focus is on the binomial lattice model and the extended quadranomial
lattice model. These will in the following be discussed in relation to the concept criteria.
3.4.1.1 The binomial lattice approach
The binomial lattice approach is often praised for its mathematic simplicity and ease of use.
As most of the real option models provide the same results at the limit13, the binomial lattice
approach is often recommended as the simplest model to evaluate and capture the value of
management decisions (Bogdan & Villiger, 2010; Mun, 2002).
As previously outlined, the pharmaceutical industry is characterised by multiple clinical
phases. Each development phase can be characterised as an option of different opportunities,
which often is recommend, to be valued using the binomial lattice approach. In essence, a
binomial lattice model is simply a discrete simulation process of the value of uncertainty
(Mun, 2002). Meaning that the binomial lattice model estimates the potential values of a risky
underlying asset in a binomial tree (Koller et al., 2010; Mun, 2002). The model assumes that
the underlying asset follows a binomial distribution in each time stage (t), as the value can
either increase (u) or decrease (d). A simple binomial tree of the up and down stages is
illustrated in figure 3.9.
13 If the time steps in the binomial model has around 10.000 steps, the binomial and Black-Scholes approach gives the same
numerical result (Mun, 2002).
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Figure 3.9: Recombining binomial tree
Source: Own creation
Figure 3.9 illustrates the simplicity of the binomial approach, based on the symmetric up and
down stages of a binomial tree. It is often assumed that the trees’ steps are recombining,
which basically means that the same value is reached no matter if the steps are up-down or
down-up. The binomial approach can be solved using two different methods, the market-
replicating portfolio and the risk-neutral probability approach. The results of the methods are
identical, but the underlying assumptions and usage are very different, why each method will
be discussed in the following section.
The replicating portfolio values the option using a constructed replicated portfolio. The
portfolio replicates the value of the option in any given state and must be rebalanced if there
are multiple periods. As the replicated portfolio and the option must have the same payoff
profile, it is possible to calculate the present value of the option using the replicating portfolio.
In a financial world the replicating assumption is easy to accept, as stocks are freely traded
and often highly liquid. In a real option world, where physical assets and firm-specific projects
are being valued, it is difficult to accept the assumption of a replicating portfolio (Mun, 2002).
The essence of the risk neutral probability approach is that instead of replicating a portfolio,
the model simply risk-adjust the probabilities of future cash flows occurring at specific time.
Using risk-adjusted probabilities at future cash flows allow decision makers to use the risk-
free discount rate in the estimation process. The results of both approaches are as mentioned
identical, why the model of choice should be the one that best satisfies the mentioned
assumptions. In the context of the pharmaceutical industry, we believe that the risk-neutral
approach is theoretically a better choice, since it is for example not possible to estimate a
replicating-portfolio. This is mainly because, as we have already established, pharmaceutical
drug development projects are not traded. The formula of the risk-neutral approach is showed
in figure 3.10.
Binomial tree
uV
dV
d^2V
udV
u^2V
Vo
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Figure 3.10: Binomial risk-neutral formula
Source: Own creation and (Mun 2012)
The inputs for estimating the risk-neutral approach are the volatility of the underlying asset,
time to expiration, and the risk-free rate. The up and down movements are equal to the
discrete simulation steps, and the risk-neutral probability is used for backward induction of
the binomial tree. As seen in figure 3.10, the value of the option is depending on nothing more
than the constant volatility (σ) and the variable time factor (t). Higher volatility increases the
range between the nodes, and a volatility of zero collapses the binomial tree into a straight
line (Mun, 2002). The reciprocal relationship between the up and down stages makes the tree
re-combining. When the risk-neutral approach and the up and down movements are
estimated, the value of the option can in the risk-neutral world be calculated using the
formula below in figure 3.11.
Figure 3.11: Option value formula in a binomial tree
Source: Own creation and (Mun, 2002)
Binimial risk-neutral formula
p/q = risk neutral proberbility
u: up movement
d: down movement
rf: risk-free rate
∆t: time step
σ: volatility
p
q 1 −
u 𝑡
d 𝑡 1
Option value in binomial tree
OV0: Present value of option
OVu/d: value of option in up and down movements
p: risk-neutral proberbility
rf: risk-free rate
∆t: time in years
0 + 1 − 𝑡
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The formula in figure 3.11 shows that the value of the option is equal to each expected up and
down stages discounted at the risk free rate. To estimate the value of a binomial lattice model,
it is first necessary to build the asset value tree of the up and down stages of the underlying
asset. Based on the asset value tree, the option formula is used to calculate the present value
by solving it recursively, using backward induction. Figure 3.12 illustrates how the exercise
price (EX) is subtracted in the final node.
Figure 3.12: Option value tree for a two period call option
Source: Own creation inspired by (Mun, 2002)
The following section will present and discuss the concept of the quadranomial lattice
approach. The underlying assumptions will be discussed more in depth in the section of
uncertainty parameters.
3.4.1.2 Quadranomial lattice approach
This section will evaluate the quadranomial lattice approach upon the concept criteria. In
relation to the problem question concerning the potential of option valuation, the
quadranomial lattice approach is interesting to analyse, since the model investigates more
than one source of uncertainty and is less mentioned by practitioners and the general
literature (Rogers, Gupta, & Maranas, 2002). The potential of this model is therefore not as
well studied and why we find it interesting to investigate in relation to the pharmaceutical
industry. The quadranomial lattice approach is an extension of the binomial model, since the
model can theoretically incorporate two sources of uncertainty instead of one. The model is
able to include, what we term as both the market- and technological uncertainty associated
Option Value tree for an two period call option
0 + 1 − 𝑡
0 + 1 − 𝑡
0 + 1 − 𝑡
0 0 − 𝐸
0 0 − 𝐸
0 0 − 𝐸
Backward induction
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with a specific project (Copeland & Antikarov, 2001). Market and technological uncertainties
are very different in nature, but both are assumed to influence the development of a
pharmaceutical development project. This will be explained below. Using two sources of
uncertainty results in four outcomes in each time node, hence the name quadranomial, which
can be seen in figure 3.13
Figure 3.13: Possibilities in quadranomial tree
Source: Own creation and (Rogers et al., 2002)
Figure 3.13 illustrates the influence of both technological and market uncertainty.
Technological uncertainty can either influence a drug development project to either succeed or
fail a clinical phase, illustrated by the horizontal line. If the project fails because of
technological uncertainty, for example low efficacy or unwanted side effects, the value of the
project becomes zero illustrated by O3 and O4. This makes the market movements without
effect on the project value. If the project succeeds technological uncertainty, the project can
either increase or decrease by the effect of market movements. This is illustrated by O1 and O2.
The quadranomial model thus has four different outcomes, illustrated as O1 to O4, instead of
only two, as the binomial model. These outcomes are used in the calculation of the option
value.
Possibilities in quadranomial tree
O1: up
O2: down
Succes
Failure
O3: up
O4: down
V0
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Figure 3.14: Qudranomial formula
Source: Own creation inspired by (Koller et al., 2010; Mun, 2002)
Figure 3.14 shows the backward induction formula of the quadranomial lattice model. The
first equation shows the influence of technological uncertainty. This is expressed as the
probabilities of technological success (S) and technological failure (F). The second equation
shows that the probability of technological failure (F) can be separated from technological
success (S). In the third equation the probability of technological failure is left out of the
formula, as a failure of technological uncertainty causes the drug development project to have
a value of zero, as explained in the above section.
3.4.2 Uncertainty parameters
This section will discuss the underlying assumptions of the binomial and quadranomial lattice
approaches and further discuss the most uncertain parameters. As both models rely on the
same assumptions, the term real options approach will be used as reference for both models in
the following section.
3.4.2.1 Market Completeness and Market Asset Disclaimer
Since it is difficult or almost impossible to find a twin security of a real option, several authors
argue that it is sufficient to estimate the real options value as if the asset were traded, if a
reliable value of the underlying asset can be estimated (Smit & Trigeorgis, 2004). To do so,
most of the literature recommends using the present value of the underlying asset, without
flexibility as the twin security (Copeland & Antikarov, 2001; Smit & Trigeorgis, 2004). To
estimate the value of the underlying asset without flexibility, the previously explained DCF
Quadranomial formula
S: technological succes
F: technological failure
EX: excercise price
e-rf ∆t
discount of one period
OV: option Value
On: outcome in n
p/q: risk-neutral probability
Page 40 of 92
model is often used. The DCF model additionally relies on the assumption of market
completeness, which assumes that all assets can be traded in the market without any
transaction costs. By using the DCF value as the underlying asset, the real option valuation
can help determine the value of the option relative to the estimated underlying project (Smit
& Trigeorgis, 2004). This assumption is also known as the Marketed Asset Disclaimer (MAD),
which is accepted in the following case in the next chapter. By assuming the MAD assumption
and the DCF value of the underlying asset, it can be argued that the above assumptions of the
real option approach are not stronger than the DCF model.
The next couple of sections will discuss the uncertainty variables, when using real options
models. The choice of approach determines the sources and measurements of uncertainty.
First, we introduce the market uncertainty, which is the building block of the lattice
approaches.
3.4.2.2 Market uncertainty (σ) & volatility estimation
The real option approach perceives in general uncertainty as both risk and opportunities.
More specifically market uncertainty is perceived as the possibility of the underlying asset to
either decrease (d) or increase (u). In the binomial model market uncertainty includes both
diversifiable and non-diversifiable risk.
Both the binomial and the quadranomial approach estimate market uncertainty by a constant
volatility (σ), expressing the volatility of the underlying asset (Mun, 2002). Since volatility (σ)
is assumed to be constant, it is the increase in time (t), which increases the stochastic term
𝜎 𝑇 and thereby the total uncertainty. One important assumption for market uncertainty to
increase over time is that the above stochastic term follows a Brownian Motion. The Brownian
Motion is a widely accepted standard assumption necessary for pricing options and it is often
praised for its relative simplicity, since it does not allow the value to be zero (Mun, 2002).
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Figure 3.15: Brownian Motion
Source: Own creation and (Mun, 2002)
The deterministic part accounts for the growth rate of the Brownian Motion process. The
growth rate of the motion has in real option analysis already been intuitively accounted for.
Since the binomial lattice approach is a discrete simulation model, it is not necessary to re-
simulate in each time step, and the remaining stochastic term is simply 𝜎 𝛿𝑡(Mun, 2002).
Another important input is the volatility estimation. Volatility is the most significant value
driver in a real option valuation, and since there are several different ways to estimate
volatility in the literature, the following section will discuss and review the most common
methods. The different estimations methods are first divided into direct and indirect
approaches as showed in figure 3.16.
Figure 3.16: Direct and indirect volatility estimation methods
Source: Own creation and (Kodukula, 2006)
Brownian Motion
deterministic term
stochastic term
stochastic term in discrete simulation
Up movement
Down movement
𝛿𝑡 𝜎 𝛿𝑡
𝛿𝑡 𝜎 𝛿𝑡
𝛿𝑡
𝜎 𝛿𝑡
𝜎 𝛿𝑡
𝜎 𝛿𝑡
𝜎 𝛿𝑡
Volatility estimation methods
Mangement Assumptions
Direct
estimations
methods
Indirect
estimations
methods
Product ProxyLogarithmic present
value
Logarithmic cash flow Market Proxy
σ
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The direct approaches are based on the actual project, and the underlying asset. The indirect
approaches are based on market- and product proxies. Management assumption is a
combination of both the direct and indirect approach. This gives a good indication of two major
perspectives on volatility estimation. Based on the importance of the volatility parameter, we
explain each volatility estimation method more in depth.
Logarithmic Present Value Approach
The logarithmic present value approach is a direct approach. It collapses all future cash flow
estimates of the project at hand into two sets of present values, one for the first time period
and another for the present time (Mun, 2002). The values obtained from the sets of present
values are summed, and a logarithmic ratio is calculated.
Figure 3.17: Logarithmic present value approach
Source: (Mun, 2002)
Assigning distributions to specific variables of the underlying asset and by simulating these,
the standard deviation of the forecasted distribution is the volatility. One of the downsides of
using this method is that simulation is required, because it increases the complexity. Another
point of critique is that the distributions used in the simulation are subjective. Furthermore,
since capital budgeting is very impartial and subjective matter, the volatility could suffer from
very biased estimates.
Logarithmic Cash Flow Returns Approach
The logarithmic cash flow returns method provides a volatility estimate based on the
variability of the underlying asset cash flow (Kodukula, 2006; Mun, 2002), and is also one of
the direct estimations methods. The method calculates the volatility based on the relative
future cash flow estimates and their logarithmic returns. The standard deviation of these
logarithmic returns, are used as volatility (Mun, 2002). The model has some important
Logarithmic present value approach
( 𝐶
=1
𝐶 =0
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shortcomings, when cash flows are negative over certain periods, the relative return will be
negative and not possible to measure in a natural logarithm. The method is often praised for
its simplicity and ease of use, as it is mathematically simple to use compared to other methods
(Kodukula, 2006).
Project Proxy Approach
A project proxy approach is an indirect volatility estimation approach. This approach uses a
proxy for the volatility like historical data from another project that have the same
characteristics. Using historical data from a previous project is the same as using real world
market information, which at first gives this approach an appealing feature. An example could
be a pharmaceutical company that uses cash flow data from a previously developed drug that
have the same cash flow profile, and use this to estimate volatility for the new project
(Kodukula, 2006). Even though this approach has its advantages, it can be difficult to find
another project with identical cash flow structures. Furthermore, using comparable projects
will in many cases not provide a very realistic or precise estimate of the volatility of the new
project.
Market Proxy Approach
The market proxy approach uses publicly available market data to estimate volatility for a
specific product (Mun, 2002). Instead of using cash flow of similar historic projects as the
project proxy approach, the market approach uses stock prices of listed companies with cash
flow profiles similar to the cash flow of the project under consideration (Kodukula, 2006).
Using equity prices, as basis for project volatility, will in most cases not be representative for
the specific project, since these are often based on multiple projects/products, market
psychology, and other factors as well (Kodukula, 2006). Another pitfall is that firms are often
levered and many projects may not be. Meaning, that adjustments are necessary before equity
prices can be considered as a proxy for project volatility (Mun, 2002).
Management Assumption Approach
There is not one specific way to define the management assumption approach. It can be a
combination of the just mentioned approaches, but the important difference from the other
approaches lies within the notion that it is the management’s assumptions that drive the
estimation of the volatility. It could be that management assumes that a project will generate
a certain cash flows and then these are used for simulation (Mun, 2002). Additionally, there
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can be situations where specific project calculations are pointless, and where the experience
and knowledge of management will provide the best estimation of volatility (Copeland &
Antikarov, 2001).
The above has introduced several methods of calculating volatility. We will later, in our case
study, argue for the method that is most suitable for our case and industry.
3.4.2.3 Technological uncertainty
As explained before some valuation methods allow a project to be subject to more than one
source of uncertainty, the market uncertainty explained above and a technological
uncertainty. The following section will clarify, what is understood by technological
uncertainty.
Technological uncertainty is assumed to be independent of market conditions and of time
dependencies, and are assumed to be diffuse in the beginning of a projects timeline, but then
reduced through time (Copeland & Antikarov, 2001). Relating to the industry it means the
technological uncertainty is independent of market conditions. It only encompasses the
specific technological uncertainty of each clinical phase. A technological uncertainty could be
the uncertainty of toxic side effects or the uncertainty of non-consistent efficacy and safety of a
drug development. Technological uncertainty is opposite to market uncertainty decreasing
trough time, as the project are exposed to less technological uncertainty as more clinical
phases are approved. The approval of a clinical phase reduces the accumulated technological
uncertainty of the project. Later in the practical implementation and evaluation, technological
uncertainty is discussed more in depth.
Figure 3.18 below summarises the theoretical principles of market- and technological
uncertainty. The market uncertainty is expected to increase as time progress while,
technological uncertainty on the other hand is resolved over time. Furthermore, it is worth
noticing that because we assume technological uncertainty is linked with the clinical phases in
the drug development process, the uncertainty is resolved step-wise. The steps symbolise the
phase-approvals that occur over time. Once a phase is approved the cumulative technological
uncertainty decreases.
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Figure 3.18: Market and technological uncertainty
Source: Own creation
3.4.2.4 Time
Another parameter of uncertainty is time, or more precisely the time-steps. The time-steps
determine the granularity of the lattice models (Mun, 2002). Many time-steps will result in a
large “web” of possible outcomes and increases the scope of outcomes as well – increasing the
extreme values in the end nodes. Typically the time-steps are yearly or quarterly
corresponding to using 1 or 0,25 as time steps.
3.4.3 Strategic Flexibility
Real Option models can theoretically incorporate, quantify, and add the value of decisions
related to future events. Real Option valuation always provides a greater value than for
example a DCF valuation of the same project or asset, since it adds the value provided by
flexibility (Mun, 2002). This incorporation of flexibility is based on the fact that Real Option
valuation models do not assume passive management through a project’s lifetime, as the
standard DCF model does. This ability to incorporate strategic flexibility and quantify it is the
key essence in Real Option thinking.
Uncertainty is the main driver in the real options models, and strategic flexibility is thereby
depending on, how each model estimate and perceives uncertainty and future events. In the
binomial model, uncertainty and the up and down movements is incorporated in the volatility
estimate. The question is therefore whether the volatility estimate is able to incorporate all
future uncertainty of a pharmaceutical drug development project. Another important question
is related to the assumption that uncertainty is resolved continuously over time, and the
Market and technological uncertainty
Tu
Market uncertainty
Mu
Time
Technical uncertainty
Page 46 of 92
question to ask is thereby if this assumption corresponds to the changing uncertainties in each
of the development phases. To get the optimal execution of a real option, it is necessary to
build an event tree that reflects the actual resolution of uncertainty over the development
process. Separating major uncertainties and to model their different interactions, is the best
way to do this (Copeland & Antikarov, 2001). Opposite to the binomial model, the extended
quadranomial model separates market and technological uncertainties. The model is
theoretical able to incorporate the changing uncertainties in each clinical phase, and thereby
in theory separates the technological uncertainty and events from the general market
uncertainty. This separation should theoretically increase the strategic flexibility of the
quadranomial model compared to the binomial mode.
Besides the way of incorporating uncertainty, strategic flexibility depends further on if
management is rational, strategies are executable, the level of uncertainty that drives the
project value and the availability of options (Mun, 2002). This will be evaluated more in depth
in following chapters. Whether management have strategic flexibility depends on the
availability of options, and the following will present a variety of different types of options. It
is necessary to consider the different option types and their characteristics what kind of
flexibility they offer and how they fit with the industry under investigation. In table 3.2 below
a short list of different types of real options is listed.
Table 3.2: Overview of selected types of real options
Source: (Berk & DeMarzo, 2014; Brealey et al., 2008; Copeland & Antikarov, 2001; Mun, 2002)
Different options
Option to abandon An option to abandon a giving project for a salvage value or to avoid future cost.
Option to expand Option to expand current capacity in case of favourable development.
Option to contract An option to sell of capacity and shrink the scale of operations.
Option to delay Delay investment until market conditions has developed further.
Compound options
Simultaneous compound optionsOptions that is contingent on the value another option, and are alive at the same
time.
Sequential compound optionOptions that are contingent on the value of another option, and are staged and not
alive at the same time.
Sophisticated options
Rainbow options An option with more than one sources of uncertainty
Learning optionsAn option with two uncertainties where market uncertainty increases over time and
technological uncertainty decreases/resolves over time
Simple Options
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It is difficult to precisely point out one type of option that would fit best in the development of
pharmaceutical drugs. One could argue for almost all the listed simple options. For example
the option to abandon a pipeline drug if the human clinical trials turn unfavourable. Or, the
option to delay the decision on beginning new phase-trails or delaying an NDA to a drug
agency. When furthermore taking the development phases into consideration, compound
options seem to be a relatively obvious match. The decision or options regarding the latter
development phases are dependent on previous phases, which again match the characteristics
and definition of compound options – options that are contingent on the value of another
option. Furthermore, the drug development process can be seen as a sequence of phases that
occur in continuation of each other, indicating that a sequential compound option would be an
obvious choice, when having a Real Option approach to valuing pharmaceutical drug
development.
3.4.4 Usability
The real option approach is evaluated to be more theoretically complex, time demanding, and
more difficult to apply compared to the two previous valuation methods. The ability to
incorporate the value of flexibility is intuitively and relevant for this industry. But as written
before, it is not a very commonly used method of valuation. We believe that the limited use of
real options can be explained by inexperience in use and a theoretical complex first impression
than actual difficult calculations. Though the calculations may not be very demanding, the
estimation of the input parameters can be difficult to estimate. The Real Option approach can,
as just explained, incorporate one and two sources of uncertainty depending on the model of
choice. Two sources of uncertainty will complicate the process, and make it a more challenging
process. Additionally, the real option approach incorporates more input parameters than the
previously mentioned valuation methods, which increase the use of resources and knowledge
requirements in order to the valuation. The below figure 3.19 is a summary of the main
findings in each of the four criteria.
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Figure 3.19: Summary of Real Option
Source: Own creation
The first research question regarding the theoretical differences and the underlying
assumptions of the three different valuation approaches are answered through this chapter.
The findings based upon the defined criteria are summarised in each of the summary figures.
To see the main theoretical differences each summary figure should be compared.
- Value estimated based the movements of an underlying asset - Perceives market uncertainty through volatility (σ)
- Option value calculated through backward induction - Perceives technological uncertainty as clinical success rates
- Follows a Brownian Motion - Difficult to estimate uncertainty parameters
- Flexibility through a broad variety of options - Theoretical complex and difficult to estimate
- Models flexibility through an asset tree - Depends on the granularity of the model i.e. time steps
- More flexibility in separating market- and technological - Separating the uncertainties increases the complexity
uncertainty
Concept Uncertainty
Strategic flexibility Usability
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4 Case study - practical implementation
This chapter will seek out to answer the second research question stated in the introduction.
In order to answer the question the focus will be on, how the different valuation methods can
be implemented when valuating pharmaceutical drug development projects. This will be done
through a case study of a pipeline product from the Danish pharmaceutical company Novo
Nordisk. Using a case study to analyse the practical implementation will provide a more clear
view of the general potential, focusing on the challenges and advantages, and how the
methods are applied most effectively. Additionally, it will later make it possible to make some
concluding comments on which valuation method is the most suitable for the industry under
investigation.
As written previously the focus is not on a numerical valuation of the case project, or to test if
the value of Novo Nordisk is priced correctly in the market, but on the assumptions and
practical implementation of the methods we have chosen to investigate. Each of the valuation
method described earlier will be investigated separately through this chapter. The comparison
of them will first be touched upon in the next evaluation chapter. Therefore, this chapter
should only be seen as a step towards fulfilling the objective of this chapter and thesis.
Throughout this chapter references will be made to the industry. These references will make
basis for, mostly, the DTA and ROV of the pipeline product. It is primarily the data on the
length of R&D periods, cost of R&D, and the success rates involved in developing new drugs
that will be used. Using these general industry data to analyse a specific product may
intuitively lead to inaccurate valuations, but since the focus is an outside-in perspective, this
is accepted as being appropriate.
Appendix 6 serves as support to the calculations shown.
The case product – NN9927
The case product, NN9927, is a new long-acting oral GLP-1 analogue14 that is intended to act
as a once-daily tablet treatment for Type-2 diabetes (Novo Nordisk, 2014). The product is
14 Glucagon Like Peptide 1 (GLP-1) enhances the glucose-stimulated insulin secretion and inhibits the release of glucagon i.e. a
treatment for diabetes (Novo Nordisk, 2014).
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currently in development phase 1, which makes it ideal for the purpose of our study. We
believe it provides a realistic case for investigating different valuation methods. Moreover, the
new drug introduces a new treatment type, which makes it interesting to investigate and
analyse, how to estimate the most appropriate project value.
Assumptions for practical implementation
In the following valuation of the oral diabetes drug NN9927 some simplifying assumptions
will be made. Key elements in a valuation-purposed thesis will be ignored or only shortly
mentioned. This includes a complete strategic analysis of the project. Because of this, some
simplifying assumptions are made in order to conduct the valuation for the evaluation of
practical use.
In the valuation of the project under investigation several considerations on how to setup the
actual valuation are necessary in order to implement the methods in the most appropriate
way. A major issue to consider is the development phase; mainly its lengthy time period and
the fact that in this period only cash out-flows exits. The DCF method as explained in the
previous has no problem in comprehending this fact and can easily incorporate this, however,
for the other methods to be able use the DCF as underlying asset, a separation of the
development phase and the commercialisation phase will be beneficial. More specifically, the
separation is carried out in a way such as the free cash flow and present value of the
commercialisation phase can be used as the underlying assets in the real option approaches,
where development costs are first deducted, when the option/decision is exercised/taken. This
will be elaborated later.
Figure 4.1: Illustration of the development –and commercialisation phase
Source: Own creation
Development Commercialisation
11 years 14 years
Page 51 of 92
The value obtained from the commercialisation phase cannot itself be used for comparison or
as a decisions-making value. It is only the value of the commercialisation of the drug under
the assumption that the development process is successful and already completed.
Based on the industry findings in Chapter 2, the development-period is assumed to be eleven
years, while the commercialisation period is assumed to span fourteen years, totalling a time-
line of 25 years. The length of the commercialisation phase is based on the assumption that
after patent expiration (assumed a patent of 20 years), the drug will face rapid declining sales
and will eventually be phased out. This is primarily caused by intensified competition from
generic products. The development period is based on the previously found industry averages
(see page 17). Notice that the total timeline exceeds the patent period of about 20 years. This
is because we assume that some research is done before patenting and because of the
possibility of gaining patent extension as previously explained.
Page 52 of 92
4.1 Practical implementation of standard DCF model
The steps we have chosen to go through in the practical implementation of the DCF model is
shown in the below figure. The implementation of the DCF model is important especially in
relation to the previously explained and accepted MAD assumption i.e. the underlying asset.
This is the reason, why we go through the following steps in detail even though it is a well-
known model.
Figure 4.2: Practical steps in DCF valuation
Source: Own creation inspired by (Copeland & Antikarov, 2001)
Each step in the practical implementation of the DCF model is illustrated in figure 4.2. The
first step is to forecast future earnings of the development and commercialisation phase.
Calculating the free cash flow is the second step. The third step is an estimation of the
discount rate. The last step is a sensitivity analysis of the different variables in the model and
the outcome.
In the following each step will be elaborated. Again we refer to appendix 6 for a detailed view
of the succeeding calculations.
4.1.1 Step One: Forecast
4.1.1.1 Sales
One of the difficult steps in the implementation of the DCF model is the first step. The
practical forecasting of a pharmaceutical drug is a difficult and a complex process, since it
requires great knowledge of for example epidemiology and medical legislation. There are
Step 1
Forecast future earnings
Forecast of
future sales and
cost of the
development and commer-
cialisation phase
Step 2
Estimate of free cash flow
Step 3
Estimate discount rate
Step 4
Estimate DCF value
Step 5
Sensitivity analysis
Calculation of
FCF by
deducting NWC
from NOPLAT
Estimation of the
risk free rate and
cost of equity as
discount rate
Discounting the
FCF by the
discount rates to
present value
Analysing the
result and input
variables by 5 to
20 per cent changes
Page 53 of 92
several different forecasting methods suggested in the literature, and the following section will
shortly discuss two different forecasting methods and their practical difficulties. Lastly, a
forecast of NN9927 future sales will be estimated.
A publication by Cook (2006) discusses the main differences between the patient- and the
prescription-based algorithm, which are two common forecasting methods used in the
pharmaceutical industry. The patient-based algorithm is focused on estimating the theoretical
number of potential patients, who are receptive to a specific drug treatment. The number of
potential patients is compared to the number of people, who are already receiving similar drug
treatment, and the future growth potential can then be estimated. To calculate the number of
potential patients, the algorithm is based on several different criteria of epidemiologic,
symptomatic and diagnosing, which are applied as filters to a specific market population. The
prescriptive-based algorithm uses data of prescriptive patients, who are already receiving
similar drug treatment. The prescriptive algorithm estimates the minimum number of people,
who are already treated and thereby useful to forecast future sales.
The main difference between the two methods is the different focus on the theoretical
maximum number of patients and the minimum number of already patients in the market.
There are several arguments for using each one of the forecasting methods, and the author of
the publication does not argue that one model is superior, as both models rely more or less on
the same input. Both models are complex, unsure, and require great knowledge of disease
states, epidemiology, and treatments in order to forecast a precise number of patients.
Because of the high demand for technical- and epidemiology insight and since we do not have
the required expertise to make a useful forecast, the forecast of NN9927 will be based on data
from third parties. The approval of NN9927 has for example an unknown cannibalisation
effect on the current diabetes product of Novo Nordisk, as the product has a new GLP
analogue treatment effect. External parties have forecasted a sales potential of NN9927 to
peak around USD 6 billion (Frovst & Thomsen, 2015; F. M. Petersen & Hansen, 2015). The
peak sale is assumed to occur after approximately eight years. This is in accordance with a
previous study, which showed that peak sales often occur around 8-10 years after initial
product launch (Grabowski et al., 2002). Lastly, the forecasted sale is adjusted with the
Page 54 of 92
cumulative probability of development success15, since only the expected cash flows of the
commercialisation phase should be included (Koller et al., 2010).
4.1.1.2 Cost
The forecasted costs are divided into two categories. The cost associated with the R&D
activities and the cost linked to producing and selling the developed drug. The following will
describe the considerations necessary in order to reliably estimate these cost figures. Research
and development costs are assumed only to appear in the development period and the cost of
producing and selling the drug in the commercialisation period only.
Research & Development
The cost figures for the R&D phase are based on the industry. As shown previously (see page
14) there is not a common understanding and opinion on the cost of developing a new
pharmaceutical drug. The estimates vary substantially depending on source and author. This
therefore raises the question on what numbers to use when forecasting the development cost.
From an external point of view it is necessary to evaluate the industry figures provided by the
litterateur and experts. If the viewpoint is internal, one could look at other drugs with similar
characteristics, and use data from these projects. Since we take an external view, we base the
R&D cost on general industry data.
The development cost assumed for the case project is inspired by PWC (2012). After having
investigated the area, it is our opinion that these estimates will provide the best and most
unbiased foundation for forecasting the development cost for the case project. Furthermore,
this choice will be in accordance with the sceptical view of the estimations from CSDD (Light
& Warburton, 2011). The specific development cost assumed for this project is the figures
provided by the report from PCW adjusted slightly. In table 4.1 the development cost is
shown.
15 The cumulative success-rate is acquired from table 2.1. Since our litterateur review of the success rates provided different
estimates, we have chosen to use the middle ranged estimate.
Page 55 of 92
Table 4.1: Development cost assumed for NN9927
Source: Own creation inspired by (PWC, 2012)
Cost in commercialisation phase
The cost estimate assumptions are based on different sources; Novo Nordisk’s annual reports
from the three last years and a publication by Kellogg & Charnes (2000). The two sources
provide somewhat similar estimates and the cost assumptions used in this study are shown in
table 4.2 below. See appendix 1 for more specific information the data used.
Table 4.2: Cost assumptions in the commercialisation phase
Source: Own creation based on (Kellogg & Charnes, 2000; Novo Nordisk, 2012; Novo Nordisk, 2013; Novo Nordisk, 2014)
4.1.2 Step Two: Estimating the free cash flow
Since this is a project valuation some simplifying assumptions are made in relation to the
estimation of these free cash flows. The working capital is assumed to be relevant from the
period the new drug is introduced to the market, meaning the first period of the
commercialisation period. The reasoning behind this assumption is that it is first within this
period the elements, that constitutes the working capital, actually becomes, pertinent. Before
Development cost for NN9927
Discovery CP I CP II CP III NDA
Cost in USD millions 90 130 190 268 26
Development cost for NN9927
Item
COGS/Cost of revenue
Marketing expense
Year 1 after launch
Year 2
Year 3-4
Year 5-14
General and administration expense
Tax
20
5
25
Pct. of revenue
20
100
50
25
Page 56 of 92
the drug is produced, no inventory is build up nor any significant change in payables or
receivables. The working capital is calculated as percentage of sales a given year.
Table 4.3: Free Cash Flow of project of NN9927
Source: Own creation
Strict assumptions are made for both the depreciations and capital expenditures. We assume
that no further investment in production assets is required, why both capex and depreciation
are assumed to be zero.
4.1.3 Step Three: Estimation of discount rate
The previous two steps have forecasted and estimated the free cash flow, where this step will
now focus on the practical estimation of the discount rate and the underlying technicalities.
In the practical estimation of the discount rate, it is first central to focus on the previous
separation of the forecasted cash flow. The cash flow separation in the development and
commercial phase implies adjustments to the discount rates used in each phase. The cash
DCF of development and operational phase
Phase NDA
Year, US $ million dollars 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 … 25
Total sales - - - - - - - - - - - 43,9 100,3 151,5 250,2 38,4
Cost of gods sold - - - - - - - - - - - 11,2 25,6 38,6 63,8 9,8
Gross Profit - - - - - - - - - - - 32,7 74,7 112,8 186,4 28,6
General and admin cost - - - - - - - - - - - 2,2 5,0 7,6 12,5 1,9
Marketing cost - - - - - - - - - - - 43,9 50,2 37,9 62,6 7,7
Research and development cost 53,5 53,5 69,0 69,0 72,7 72,7 72,7 96,7 96,7 96,7 26,0
EBITDA -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 19,0
Depreciation and amortisation
EBIT -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 19,0
Carry forward previous period 0,0 -53,5 -107,0 -176,0 -245,0 -317,7 -390,3 -463,0 -559,7 -656,3 -753,0 -779,0 -792,4 -772,8 -705,4 0,0
Carry forward current period -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 19,0
Remaining carry foward -53,5 -107,0 -176,0 -245,0 -317,7 -390,3 -463,0 -559,7 -656,3 -753,0 -779,0 -792,4 -772,8 -705,4 -594,1 -
Taxable income - - - - - - - - - - - - - - - 19,0
Tax - - - - - - - - - - - - - - - 4,7
Profit after tax -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 14,2
FCF calculation
EBIT -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 19,0
Tax - - - - - - - - - - - - - - - 4,7
NOPLAT -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 14,2
Add depreciation - - - - - - - - - - - - - - - -
Less Capex - - - - - - - - - - - - - - - -
NWC - - - - - - - - - - - 7,5 17,1 25,7 42,5 6,5
Less ΔNWC - - - - - - - - - - - 7,5 9,6 8,7 16,8 -8,8
FCF -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -20,8 10,0 58,7 94,6 23,0
Discovery CP I CP II CP III
Discovery and clinical trials Commercialisation phase
Page 57 of 92
flows of the development phase should be discounted at the risk free rate, since the project is
only affected by costs and private risk. Cash flows of the commercialisation phase should be
discounted by the market-adjusted discount rate, since it is affected by market risk in
earnings (Mun, 2002). The market-adjusted discount rate is in most cases WACC, but will in
this case be the cost of equity, since no debt financing is considered. The cost of equity is based
on three inputs; the risk free rate, the market premium, and beta. Beta is the only project
specific parameter and the most uncertain and difficult parameter to estimate. The practical
estimation of each parameter will be discussed in the following sections.
4.1.3.1 Market risk premium
The market risk premium is the difference between the risk free rate of return and the return
of holding the market portfolio, and defined as the additional return that investors expect to
earn to compensate for risk (Berk & DeMarzo, 2014). There are in general two different
methods to estimate the market risk premium; the historical based ex-post approach and the
forward-looking ex-ante approach. The ex-post approach is often based on larger number data,
and the ex-ante forward-looking risk premium is often based on expectations from
practitioners. The well-know Aswath Damadoran estimates an average risk premium from
1960 to 2015 of 4,07 per cent, and the average risk premium for year 2015 is estimated to 5,7
per cent (Damodaran, 2015). A publication by Fernandez, Linares, & Acín (2015) finds a
similar average risk premium of 5,5 per cent. We use a risk premium of 5 per cent based on
the above and our case.
4.1.3.2 Beta value (β)
Beta is assessed to be the most uncertain parameter of the DCF model in the previous
discussion of uncertainty, and the practical estimation of this is thus important for the
valuation. For the valuation of project NN9927 beta should theoretically reflect the specific
risk of the project and not the company risk, which complicates the estimation. In practice the
literature in general suggest that a common method for estimating a project’s beta is to
identify comparable firms in the same line of business, and compare these to the beta of the
firm undertaking the project. Based on the comparable firms it is possible to estimate a proxy
for the project (Berk & DeMarzo, 2014; Brealey et al., 2008). The assumption makes the
practical estimation easier, but the literature has no answer to the number of peer companies,
which makes the estimation fully dependent on practitioners’ own considerations. In our case
Page 58 of 92
comparable peer companies and the industry will be used in the practical estimation of beta. A
typical method to estimate beta is to calculate the return of a company stock and regress these
on a large market portfolio index (Berk & DeMarzo, 2014). Which time frame and market
portfolio that should be used are depending on the specific project, as the literature has no
straightforward answer. In this specific case a time frame of six years and both monthly and
weekly returns are used (Damodaran, 2015). The chosen market portfolio index is the MSCI
World Index, which is a free float-adjusted market capitalisation weighted index of 23
developed countries. This index is chosen to avoid the influence of a local market index, which
may be weighted towards some few industries. To choose a local market index is a common
estimation error in the literature, as a small local market index may be weighted towards one
or few industries and thereby not representative for a weighted market portfolio.
The estimated betas will be based on closing adjusted prices to avoid the influence of
dividends, and will furthermore be unlevered and adjusted for debt, because the development
of NN9927 is considered to have no debt financing. An average of a three-year debt/equity
ratio of each company will be used to estimate the unlevered betas. The calculations are
clarified in appendix 2. The estimated betas of Novo Nordisk, peer group, and the industry is
shown in table 4.4.
Table 4.4: Overview of Beta estimation
Source: Own creation from DataStream, Damodaran (2015)
Weekly Monthly Weekly Monthly
Beta 0,74 0,85 Beta 0,94 1,03
R-squared 0,27 0,30 R-squared 0,45 0,49
Standard Error 0,07 0,15 Standard Error 0,06 0,12
BetaUnlevered-avg 0,73 0,84 BetaUnlevered-avg 0,86 0,94
Observations 313 72 Observations 313 72
Avg D/E 0,017 0,017 Avg D/E 0,12 0,12
Weekly Monthly
Beta 0,56 0,36 Beta 1,03
R-squared 0,26 0,08 R-squared -
Standard Error 0,05 0,13 Standard Error -
BetaUnlevered-avg 0,43 0,28 BetaUnlevered-avg 0,91
Observations 312 72 Observations 151
Avg D/E 0,40 0,397 Avg D/E 0,13
Novo Nordisk Sanofi
Eli Lilly Industry
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Table 4.4 shows the levered and unlevered beta estimations and the statistical output. The
output is carefully interpreted. The beta estimations differ between the weekly and monthly
data, and it is seen that the standard error is larger in the monthly data. Sanofi is estimated
to have a higher beta value and Eli Lilly a lower beta value than the unlevered monthly beta
of Novo Nordisk. The industry consists of data from 151 companies, which have a higher beta
value but the statistical output is unknown for this. Based on the above estimations a beta
value of 0,9 is chosen for this project. The value is primarily based on the beta value of Novo
Nordisk and the industry of 151 different companies. We assume project NN9927 have
similarities to the general industry and thereby outweighs the influence of the competitors’
beta estimates.
4.1.3.3 Risk-free rate of return
The risk free rate of return is theoretically the return of an investment with no risk or loss,
and the minimum return any investor would expect. Practically government bonds with no
default-risk are used to represent the risk-free rate (Berk & DeMarzo, 2014). The literature
has no mutual answer on which government bond to use. Some practitioners argue that the
maturity of the government bond should match the time period of the cash flow of the project,
while others argue that a government bond of 10 year should be used (Koller et al., 2010). We
use an American treasury bill with a 10 year maturity, which corresponds to an effective
interest rate of 2,38 per cent.
After having estimated the market risk premium, beta, and last the risk free rate of return,
the cost of equity can now be estimated in the following section.
4.1.3.4 The cost of equity
The unlevered cost of equity can now be calculated based on the above. The calculation is
shown below in figure 4.3. We estimate a cost equity of 6,88 per cent. This is the rate used to
discount the commercialisation phase.
Page 60 of 92
Figure 4.3: Cost of equity for case project
Source: Own creation
4.1.4 Step Four: Estimate of DCF value
After having estimated the cost of equity in the previous step, the free cash flow of each year
can now be discounted. Both the free cash flow calculation and the discount factor are
presented in table 4.5 below. The discount factor is calculated as the reciprocal value to show
the influence in each year.
Table 4.5: Calculation of DCF value
Source: Own creation
The discounted cash flow value of project NN9927 is equal to US $2,71 million dollars. It is
important to mention, that the discounted cash flow includes both the development and
commercialisation phase, and that the R&D period is discounted at the risk free rate and
commercialisation period with the cost of equity. The DCF value used for the real option
approach, the underlying asset, is only the discounted cash flow value of the
Cost of equity
rf: risk free rate
6,88% = 2,38% + 0,9 x 5% β: Beta
E(Rm)-rf: Market risk premium
+ ( − )
Calculation of DCF value
Phase NDA
Year, US $ million dollars 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 … 25
EBIT -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 19,0
Tax - - - - - - - - - - - - - - - 4,7
NOPLAT -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -13,4 19,6 67,4 111,4 14,2
Add depreciation - - - - - - - - - - - - - - - -
Less Capex - - - - - - - - - - - - - - - -
NWC - - - - - - - - - - - 7,5 17,1 25,7 42,5 6,5
Less ΔNWC - - - - - - - - - - - 7,5 9,6 8,7 16,8 -8,8
FCF -53,5 -53,5 -69,0 -69,0 -72,7 -72,7 -72,7 -96,7 -96,7 -96,7 -26,0 -20,8 10,0 58,7 94,6 23,0
Discount factor 0,98 0,95 0,93 0,91 0,89 0,87 0,85 0,83 0,81 0,79 0,77 0,45 0,42 0,39 0,37 0,19
Discounted FCF -52,3 -51,0 -64,3 -62,8 -64,6 -63,1 -61,6 -80,1 -78,2 -76,4 -20,1 -9,4 4,2 23,1 34,9 4,4
DCF value 2,71
Commercialisation phase
Discovery CP I CP II CP III
Discovery and clinical trials
Page 61 of 92
Input Variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
Success rate -151,9 -121,0 -90,1 -59,1 -28,2 2,7 33,6 64,3 95,1 125,8 156,5
COGS 88,4 71,3 54,2 37,1 19,9 2,7 -14,5 -31,7 -49,0 -66,2 -83,4
Tax 38,7 31,5 24,3 17,1 9,9 2,7 -4,5 -11,7 -18,9 -26,1 -33,3
GA cost 19,6 16,2 12,8 9,5 6,1 2,7 -0,7 -4,0 -7,4 -10,8 -14,2
Working capital 7,9 6,8 5,8 4,8 3,7 2,7 1,7 0,7 -0,4 -1,4 -2,4
Input Variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
Beta 152,3 119,8 88,6 58,8 30,1 2,7 -23,6 -48,8 -73,0 -96,1 -118,3
Market risk premium 152,3 119,8 88,6 58,8 30,1 2,7 -23,6 -48,8 -73,0 -96,1 -118,3
Risk free rate 53,7 42,9 32,4 22,2 12,3 2,7 -6,6 -15,7 -24,5 -33,1 -41,4
commercialisation phase, without development cost. The DCF value will be discussed more in
depth in the evaluation section.
4.1.5 Step Five: Sensitivity Analysis of input variables
In order to test what we have previously determined as being the most theoretical uncertain
parameters, we conduct a sensitivity analysis in the following. The sensitivity analysis is
evaluated to be the only way to incorporate strategic flexibility in the DCF model, which will
be tested now and commented on in the later evaluation chapter. The analysis provides an in
depth understanding of each input and how the DCF result is influenced by changes in
different input variables. The analysis is performed on the cost of equity and the free cash
flow. The analysis is presented in the following figures and shows the DCF value, when one
input variable is affected by a percentage change, ceteris paribus.
Figure 4.4: Sensitivity analysis of input in cost of equity
Source: Own creation through crystal ball
The first figure 4.4 shows the sensitivity analysis of the input variables to the cost of equity.
As seen, the practical estimation of both beta and the market risk premium is important, since
a relative percentage change in beta and the market risk premium have a great impact. This
is in accordance with our previously findings. In relation to our previous assumptions, the cost
of equity is only influencing the commercialisation phase and the risk free rate the
development phase. The development phase is thereby affected with less uncertainty.
Figure 4.5: Sensitivity analysis of input of various inputs
Source: Own creation through crystal ball
Page 62 of 92
The cumulative technological success rate has the greatest impact on the project’s DCF value.
An increase of the cumulative success rate has a positive influence on the project value, which
is opposite for the remaining input variables. COGS is the only input variable, which also has
a significant effect.
From the above sensitivity analyses, the input in the cost of equity and the cumulative success
rates are found to have the greatest impact on the DCF value. It is thereby confirmed that the
practical estimation of these input variables should be carried out with great caution and
precision.
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4.2 Practical implementation of Decision Tree Analysis
This section will go through the practical implementation of the DTA and demonstrate, how
the discussed theory can be practically applied to the project valuation of project NN9927.
Step 1 and Step 3 will be shortly considered, since they are somewhat similar to what
previously has been discussed in the implementation of the DCF model.
Figure 4.6: Overview of steps in DTA
Source: Own creation
Firstly, a forecast of the sales and free cash flow is estimated in step 1, and next the
probabilities used in each of the decisions nodes are estimated. Step 3 is the estimation of an
appropriate discount rate for the model. Finally, the two last steps are the actual estimation of
the DTA value and a sensitivity analysis.
4.2.1 Step One: Forecast
The first step of the DTA model is the practical forecast of future earnings and the free cash
flow. As mentioned above, this step is similar to the first steps of the previous DCF model, and
both models are based on the same forecasting methods. In spite of this, there are some few,
but rather important differences between the forecast of the DTA and DCF model. The free
cash flow of the DTA model is not adjusted by the cumulative success rate, since each step of
the backward induction is adjusted by the success rate of each clinical phase. The forecast step
is similar to the previous DCF model, but the estimations are used differently in the further
valuation.
Step 1
Forecast future
earnings
Forecast of future sales and cost of
the development
and commer-cialisation phase
Step 2
Estimate probabilities of
each phase
Step 3
Estimate discount rate
Step 4
Estimate DTA value
Step 5
Sensitivity analysis
Investigation of clinical success
rates from
previous drug developments
Estimation of cost of capital used for
FCF and
development phase
Using backward induction and
clinical success
rates
Analysing the result and input
variables by 5 to
20 percent changes
Page 64 of 92
4.2.2 Step Two: Probability
The second step of the practical implementation is the estimation of the probabilities, which
we determine as the probabilities of either success or failure in the development phases.
Determining the probabilities of success and failure are rather difficult, as the probability
must be estimated based on either previous results or rough estimates. The probabilities are
highly project-specific, and it is difficult to compare different pharmaceutical development
projects. Since the model is set up to consider the probability of success or failure, the
discussed technological success rates are used as proxies for these estimations. The probability
of success in each of the phases must be considered, as these constitutes each of the decision
nodes. The probabilities chosen for the case product are shown in table 4.6 below and are
based on historical estimates (DiMasi & Grabowski, 2007; DiMasi et al., 2010; DiMasi et al.,
2014).
Table 4.6: Probabilities used for in each of the decision nodes
Source: Own creation
4.2.3 Step Three: Discount rate
The third step of the practical implementation of the DTA is the estimate of discount rate. The
practical estimation is similar to the DCF model, and according to the arguments earlier, the
development and the commercialisation phase should be discounted by different discount rates
(Mun, 2002). The development phase should be discounted by the risk free rate, since the
clinical phases are uncorrelated to market uncertainty and are subject to private risk. The
commercialisation phase should be discounted by the cost of equity, as these cash flows are
subject to market risk. As no debt financing is considered in the development of project
NN9927, the cost of equity is used as the market discount rate. The risk free rate has been
estimated to 2,38 per cent and the cost of equity was estimated to 6,88 per cent previously in
the last section.
Probability and technological uncertainty
Phase CP I CP II CP III NDA Cumulative
Probability 68% 42% 66% 90% 17%
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The practical estimation of the discount rate, particularly the cost of equity, is subject to great
uncertainty, since the input parameters are estimated based on strong assumptions, and the
results of the practical estimations should be evaluated in the context of the sensitivity
analysis.
4.2.4 Step Four: Estimate DTA value
Now we calculate the DTA value in the fourth step. The calculations are illustrated in figure
4.7. Based on the value of the commercialisation phase, the technological success rates, and
the costs of research and development in each clinical phase the DTA value is calculated.
Figure 4.7: DTA calculations
Source: Own creation
From figure 4.7 it is seen, that the value of the commercialisation phase (without adjusting
with the cumulative success rate) is equal to USD 3666,7 million dollars, discounted by the
cost of equity. The cost of R&D of each clinical phase is discounted by the risk free rate, and
the costs are assumed to occur at the end of each clinical phase. The technological success
rate, the chance entering the next phase, is showed for each of the five phases and the
cumulative rate is equal to 17 per cent, as in the our DCF case. Using the explained backward
induction method, the discounted cash flow value of the commercialisation phase is multiplied
by each of the technological success rates, and the corresponding research and development
costs are deducted. The DTA is equal to US$ 182,4 million dollars, when the different
probabilities of future events are taking into consideration. Compared to the previous DCF
model, the DTA generates a much higher value.
Decision Tree Analysis of development and commercialisation
Phase
Year
Technological success rate
Research & Development
PV of R&D
DTA
DTA Value 182,4
218/(1+ 2,38%)^5 MAX(3667,7*90% - 20;0)
Commercialisation phase
NDA
3280,8
11
90%
26
20
Development phasse
107
107
182,4
3
138
128,6
289,4
Discovery CP I CP II
42%68%
1
614,7
194
218
5 8
66%
290
1925,1
240
CP III
3667,7
11 - 24
Page 66 of 92
Input variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
Beta 321 291 262 234 208 182 158 135 112 91 70
Market risk premium 321 291 262 234 208 182 158 135 112 91 70
Risk free rate 243 230 218 206 194 182 171 160 150 139 129
Input variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
CP I success rate 78 99 120 141 162 182 203 224 245 266 287
CP II success rate 45 72 100 127 155 182 210 237 265 292 320
CP III success rate 28 59 90 121 151 182 213 244 275 306 337
NDA success rate 27 58 89 120 151 182 214 245 276 307 338
4.2.5 Step Five: Sensitivity
In the last step of the DTA model, the result is analysed through a sensitivity analysis. Since
the DTA model incorporates more uncertainty and more variables, the sensitivity analysis
shows that the DTA value is influenced differently. Given the uncertainty in the cost and
success rate estimates found in the introduction chapter, we find it important to conduct a
sensitivity analysis on these parameters. The analysed variables are the input of the discount
rate, the technological success rate, and the research and development cost of each clinical
phase. The results of the sensitivity analyses are showed in the below tables.
Figure 4.8. Sensitivity analysis of the components in the discount rate
Source: Own creation
The discount rate has a lower influence on the DTA value, which is an important difference
from the DCF model, and therefor focus should be on the below findings.
Figure 4.9: Sensitivity analysis of the technological success rates of each clinical phase
Source: Own creation
The sensitivity table of the technological success rates shows that the NDA, CP III, and CP II
have the largest influence on the DTA value. This supports the importance of being able to
practical estimate a reliable technological success rate of each clinical phase. The technological
success rates are influenced by high uncertainty and have the largest influence on the
estimated DTA value.
Figure 4.10: Sensitivity analysis of the research and development cost of each clinical phase
Source: Own creation
Input variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
R&D Discovery 209 204 198 193 188 182 177 172 166 161 156
R&D CP I 215 208 202 195 189 182 176 170 163 157 150
R&D CP II 215 209 202 196 189 182 176 169 163 156 149
R&D CP III 200 196 193 189 186 182 179 176 172 169 165
R&D NDA 183 183 183 183 183 182 182 182 182 182 181
Page 67 of 92
Figure 4.10 shows that the research and development costs of each clinical phase have the
lowest influence on the estimated DTA value. The table shows a clear relationship of an
increase in development costs implies a decrease in the DTA value. The costs of each phase
depend on the technological success rate, and even if the costs are affected by a large
percentage change, the DTA value is rather stable in comparison to the other input variables.
Page 68 of 92
4.3 Practical implementation of the Binomial Lattice approach
This section will examine the practical implementation of the Binomial Lattice approach in
relation to our case project. The below figure illustrates the five practical steps that will be
implemented in the following.
Figure 4.11: Practical implementation of the binomial lattice approach
Source: Own creation inspired by (Copeland & Antikarov, 2001)
The first step is to estimate the underlying asset of the commercialisation phase, and further
identify and estimate the volatility of the underlying asset in step two. The third step is the
creation of the asset tree, which the backward induction is conducted upon in the fourth step.
Lastly, a sensitivity analysis is analysing the different input variables and the result.
4.3.1 Step One: Estimate underlying asset
The first step in the practical implementation of the model is to determine and estimate the
underlying asset. In our case the underlying asset is equal to the discounted cash flow value of
the commercialisation phase in accordance with the MAD assumption. The underlying sales
and earnings forecasts of the commercialisation phase are similar to the two previous
valuation methods. For the binomial model the future sales are assumed to peak around US
$6 billion dollars, discounted at a cost of equity of 6,88 per cent.
4.3.2 Step Two: Identify and estimate volatility
Second step is estimating the volatility, which is used to create the asset tree in step three. As
previously mentioned, the volatility estimate is the most significant value driver in Real
Step 1
Determine and estimate
underlying asset
Estimate DCF value as the
underlying asset
of the ROA
Step 2
Identify and estimate
volatility
Step 3
Create and estimate asset
tree
Step 4
Conduct binomial Real Option
analysis
Step 5
Sensitivity analysis
Estimate the volatility by
Monte Carlo
Simulation
Estimate asset tree by the up
and down stages
of the distribution
Using backward induction
adjusted by the
up and down probabilities
Analysing the result and input
variables by 5 to
20 per cent changes
Page 69 of 92
Option valuation, why it also is essential to do thorough reflections on this parameter before
continuing. Referring back to uncertainty parameters of Real Options there are different
methods of calculating the volatility. There is no simple and easy choice and it is largely a
matter of preference. The market proxy approach can in our case relatively quickly be
rejected. It would be nearly impossible to find a stock that will match the risk profile of the
project. If any, it should be the volatility of Novo Nordisks share price. Finding a portfolio with
a similar risk profile can be extended to the other proxy approach as well, the product proxy
approach. The development of pharmaceutical drugs are such a specialised and unique chain
of events that finding a similar product that can be used as proxy would be difficult. Unless it
is possible to find an identical project with similar risk and use that as proxy, using a product
proxy would not provide a very precise estimate of the volatility of project NN9927. The
specific project under investigation introduces a brand new oral type of diabetes treatment,
which makes our project even more matchless and makes the indirect methods irrelevant.
This leaves the two direct approaches described earlier. As explained, the two methods are
somewhat similar, why the choice between them is a question of preference. We have chosen
to focus on the logarithmic present value approach, since we find it more appropriate to use
simulation in the volatility estimate. Our specific setup, calculations, and estimation of the
volatility can be found in the in appendix 6 and a deeper elaboration of the chosen assumption
can be found in appendix 3. The input assumptions for the volatility estimation are the
variables with the highest influence on the underlying asset. These were found in the tornado
diagram showed in appendix 4. The six most influential variables were chosen. In this
appendix it can be seen that these six variables are; Cost of goods sold, Marketing expense
year 5-14, Cumulative success rate, Beta, Market risk premium, and the Risk free rate. Using
15.000 simulations the volatility was estimated to 19 per cent. See appendix 3 for further
details.
4.3.3 Step Three: Create asset tree
The underlying asset value of the binomial tree is implemented in the following. Figure 4.12
shows the practical calculations of the underlying asset tree. The first value shown in time
zero is the present value of the commercialisation phase of US$ 677,2 million (adjusted with
the cumulative success rate). The asset tree is subsequently calculated by letting the
underlying asset value follow the up and down movements in each of the 11 years.
Page 70 of 92
Figure 4.12: Asset tree in the binomial model
Source: Own creation
Moving from left to right the asset tree is expanding as more nodes are occurring in time. In
last node, NDA, it can be seen that the underlying asset theoretically can result in two
extreme values. The maximum value is obtained if the underlying asset is only affected by up-
movements, while the lowest value is the opposite.
4.3.4 Step Four: Conduct Real Option analysis
In step four the option value tree is constructed seen in figure 4.13 below. The option tree is
based on a European compound option and the option to abandon the development of project
NN9927. The option tree is practically solved from right to left by using backward induction,
starting with the last end-node of the asset value tree. In each node, the option value is
weighted by the risk-neutral probabilities of p and q and further adjusted by the risk free rate
and an annual time factor. The risk neutral approach is used in this case rather than the
replicating portfolio, since it has not been found reliable to practical estimate a replicating
portfolio, which has a similar payoff structure.
In each end-node of the clinical phases, year 2,7,10, and 11 the exercise price EX, which is the
research and development costs, is subtracted from the option value. The option to abandon
will only be exercised, if the value is less than zero, otherwise the option will not be exercised
and the development project of NN9927 will be continued.
Asset tree
Phase NDA
Year, US $ million 0 1 2 3 4 5 6 7 8 9 10 11
677,2 819,0 990,3 1.197,5 1.448,1 1.751,2 2.117,6 2.560,7 3.096,5 3.744,4 4.528,0 5.475,4
560,1 677,2 819,0 990,3 1.197,5 1.448,1 1.751,2 2.117,6 2.560,7 3.096,5 3.744,4
463,1 560,1 677,2 819,0 990,3 1.197,5 1.448,1 1.751,2 2.117,6 2.560,7
383,0 463,1 560,1 677,2 819,0 990,3 1.197,5 1.448,1 1.751,2
316,7 383,0 463,1 560,1 677,2 819,0 990,3 1.197,5
6,772 * 1,2092 = 819,0 261,9 316,7 383,0 463,1 560,1 677,2 819,0
V0 216,6 261,9 316,7 383,0 463,1 560,1
6,772 * 0,8270 = 560,1 179,1 216,6 261,9 316,7 383,0
148,1 179,1 216,6 261,9
122,5 148,1 179,1
101,3 122,5
83,8
Discovery CP I CP II CP III
Discovery and clinical trials
Page 71 of 92
Figure 4.13: Binomial option value tree
Source: Own creation
The value of development project NN9927 and the real option are equal to US$ 132,878
million. By deducting the previously calculated DCF value, the real option value is calculated
equal to US $130,2 million dollars, which is the value reflecting the option. A discussion of the
different findings of each valuation method is carried out in the later evaluation section.
4.3.5 Step Five: Sensitivity Analysis.
The last step in the practical implementation of the binomial model is a sensitivity analyses,
analysing. Each input variable is analysed by holding all others constant, which is showed in
figure 4.14 below.
Figure 4.14: Sensitivity analysis of volatility and risk free rate
Source: Own creation
From figure 4.14 it is seen, that an increase in the risk free rate has a large negative influence
on the value. The influence of the risk free rate is greater than the volatility variable, which
on the other hand has a positive influence on the option value. The volatility variable is used
Binomial Option Value
Phase NDA
Year, US $ million 0 1 2 3 4 5 6 7 8 9 10 11
132,9 226,4 375,1 675,8 913,8 1.335,6 1.692,1 2.124,9 2.848,7 3.490,7 4.268,1 5.455,4
39,9 79,3 299,8 456,0 782,0 1.022,6 1.315,4 1.869,8 2.306,9 2.836,6 3.724,4
- 74,8 148,5 403,4 564,8 761,8 1.200,4 1.497,4 1.857,7 2.540,6
- - 156,3 251,7 383,2 742,5 943,8 1.188,3 1.731,1
- 31,5 62,6 124,3 429,5 565,2 730,5 1.177,5
- - - 215,4 306,3 417,4 798,9
- - 78,6 129,3 203,3 540,0
- 14,4 28,6 56,9 362,9
- - - 241,8
- - 159,0
- 102,4
63,7
Excercie Price EX 107,0 128,6 193,8 240,3 20,1
Total value 132,9
Option Value 130,2
MAX(0,52*2.848,7+0,48*1.869,8)*EXP(-2,38%*1)-193,8;0)
MAX(5.475,4 - 20,1;0)
(0,52*913,8 + 0,48*456,0)* EXP(-2,38%*1)
Discovery and clinical trials
Discovery CP I CP II CP III
Input variables, pct change -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25%
Volatility 112,5 116,3 120,4 124,5 128,6 132,9 137,2 141,5 145,8 150,2 154,6
Risk free rate 163,3 157,0 150,8 144,7 138,8 132,9 127,1 121,4 115,9 110,4 105,0
Page 72 of 92
in the estimation of the underlying asset tree, and the risk free rate is used in the adjustments
for the risk neutral probabilities, which explain the different influence on the result. This is in
accordance with previous findings.
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4.4 Practical implementation of the Quadranomial Lattice approach
The following will show the implementation of the quadranomial option approach. It also
consists of five steps.
Figure 4.15: Steps in the quadranomial approach
Source: Own creation
4.4.1 Step One, Two, and Three
The quadranomial approach is simply an adjustment to the binomial approach. The concept of
adding another source of uncertainty is complicating the practical implementation slightly.
Now the model contains both market uncertainty (the volatility) and technological uncertainty
(the clinical phase success rates). The overall approach is the same as the above steps and is
only marginally adjusted compared to binomial approach.
The principle of estimation of the underlying asset is the same. But since we now have to
construct a model that explicitly takes into account the technological success, the underlying
asset must be different from the binomial approach, which implicitly had taken the
cumulative success rates into account. The underlying asset used for conducting the
quadranomial option analysis is therefore the un-adjusted present value of the
commercialisation phase used in the DTA-analysis. It does not include any modifications for
technological uncertainty, which is necessary because otherwise the success rates would be
accounted for twice. The volatility estimate is the same as before, and the principle in creating
the asset tree is the same as before as well, and to avoid redundancy these steps are not
explained separately here.
Step 1
Determine and estimate
underlying asset
Estimate DCF value as the
underlying asset
of the ROA
Step 2
Identify and
estimate
volatility
Step 3
Create and
estimate asset
tree
Step 4
Conduct
quadranomial
Real Option
analysis
Step 5
Sensitivity
analysis
Estimate the volatility by
Monte Carlo
Simulation
Estimate asset tree by the up
and down stages
based on the distribution
Using back-wardinduction
adjusted by the
up and down probabilities and
technological
uncertainty
Analysing theresult and input
variables by 5 to
20 per cent changes
Page 74 of 92
4.4.2 Step Four: Conduct quadranomial analysis
In step four, the quadranomial option tree is estimated based on the underlying asset tree,
which is showed in figure 4.16. The quadranomial option approach is constructed as a
European compound option, holding the option to abandon the development project NN9927
after each clinical phase, depending on the outcome. The practical estimation of the
quadranomial option tree is almost similar to the binomial model, and is solved from right to
left starting with the last node of the underlying asset tree. By using backward induction,
each node is weighted by the risk neutral probabilities of p and q and is further adjusted at
the risk free rate and a time factor. The option to abandon in each clinical phase is exercised if
the value is less than or equal to zero, otherwise the development project is continued.
The quadranomial option approach is in simple terms adjusting the option value by the
technological uncertainty of each clinical phase, which is resolved in year 4,7,10, and 11. By
doing so we take each of these technological uncertainties into consideration and explicitly use
them for calculating the option value.
Figure 4.16: Quadranomial option value
Source: Own creation
The value of development project NN9927 is equal to US$ 355,5 million. This value reflects
both the project value and the value of the quadranomial option. As we use the un-adjusted
DTA value of the commercialisation phase as underlying asset, we subtract the previously
found DTA value to obtain the quadranomial option value of US$ 173,1 million.
Quadranomial Option Value
Phase NDA
Year, US $ million 0 1 2 3 4 5 6 7 8 9 10 11
356 479,3 630,1 923,4 1.149,3 2.224,9 2.716,6 3.311,7 9.798,8 11.879,2 14.395,7 26.669,5
241,4 342,5 575,5 728,7 1.476,9 1.812,1 2.218,0 6.649,7 8.071,2 9.790,9 18.232,6
145,8 337,7 441,1 965,4 1.193,5 1.470,0 4.496,2 5.467,0 6.641,8 12.462,8
175,0 244,4 615,6 770,5 958,5 3.023,4 3.686,1 4.488,2 8.517,1
109,9 376,4 481,3 608,7 2.016,3 2.468,3 3.015,5 5.818,8
212,8 283,4 369,5 1.327,6 1.635,4 2.008,4 3.973,6
148,2 205,9 856,6 1.065,8 1.319,6 2.711,7
94,0 534,5 676,3 848,6 1.848,7
314,2 410,0 526,5 1.258,5
227,8 306,3 855,0
155,6 579,0
390,2
107,0 128,6 193,8 240,3 20,1
68% 42% 66% 90%
Total value 355,5
Option value 173,1
MAX(29.652,9-20,1;0) * 90%
(0,52*1.149,3 + 0,48*728,7)*EXP(-2,38%*1)
MAX(0,52*9.798,8 + 0,48*6.649,7)*EXP(-2,38%*1) -193,8;0)* 42%
Discovery CP I CP II CP III
Discovery and clinical trials
Page 75 of 92
4.4.3 Step Five: Sensitivity Analysis
The last step, the sensitivity analysis of the input variables of the quadranomial option
approach differs from the previous. The sensitivity analysis showed that in order to change
the option value the input volatility variable requires more than a 25 per cent change. This is
why, we exclude the sensitivity figure.
4.4.4 Sum-up
To answer the second research question regarding the difference in practical implementation,
ease of use and strategic opportunities, we have made the practical implementation of each
step figure, implemented our different assumptions in relation to the industry, and estimated
a project value. These findings will be further evaluated in the next chapter.
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5 Evaluation & recommendation
This chapter has the purpose of answering our last research question; what is the potential of
using real option valuation with the pharmaceutical industry in mind.
Firstly, in order to evaluate the potential of one method we find it necessary to consider other
possible methods as well. Therefore in the following, the different methods will be discussed in
relation to each other and how they differ, both in a structural context and in their final
results. This will help show the possible reasons, why practitioners favour certain methods of
valuation than others. The evaluation will take into consideration the fact that the
development of new drugs is costly, time lengthy, attached with great uncertainty, and
generally a complicated business, which is kept in mind in the following evaluation.
As we already have determined that real options analysis will always provide a greater
valuation value than a standard present value valuation16, the objective of this chapter is
consequently not to emphasise this statement, but to take the discussion and evaluation
further. The objective is to investigate, which of the different valuation methods that matches
the industry in the best way – which one of the methods provide the optimal value based on
the theory and the practical implementation. Furthermore, we look in to the depth of the
usability, suitability, and potential of the models.
Finally, it is worth mentioning that we are aware that especially the evaluation of practical
implementation will be severely biased by the way, we have formed the implementation, and
the assumptions we have made. We will try to see beyond that and stay as objective as
possible in the evaluation.
5.1 Evaluation of the DCF model
The DCF model estimates the discounted free cash flow of the development and the
commercialization phase equal to US$ 2,71 million. The DCF model valuates product NN9927
to the lowest present value compared to the other valuation models, which was expected
taking the discussed theory in mind. The relative low DCF value reflects the critical limitation
16 A real option analysis will always provide a greater value than a traditional DCF valuation. The only case where they would
provide the same value is when the volatility estimate is zero. The asset tree collapses in to a straight line i.e. becoming a
standard DCF (Mun, 2002). It will never provide a lower value.
Page 77 of 92
of the DCF model, and reflects the difficulties for decision makers to decide whether to invest
in an uncertain development process and project.
The practical estimation of the DCF model has previously been evaluated as less complicated
to implement compared to the other valuation models, which we agree after our
implementation. The main critic of the model is based on the general characteristics of the
input variables rather than the practical calculations in the valuation process. The discount
rates used; the risk free rate for the development phase and cost of equity for the
commercialisation phase, are both static input variables and are not affected by the
uncertainty in the clinical phases, which we find to be important in order to reach an optimal
value. Compared to the other valuation models, the DCF model only perceives uncertainty
through the estimation of beta in the cost of equity and does not integrate any specific
technological uncertainty of any kind. Further, the estimated beta has a critical influence on
the valuation, since it affects a US$ 50 million dollar change by a 10 per cent change in the
beta value. The ability to estimate a correct and reliable beta estimate is thereby highly
important in our valuation of product NN9927. Another critical aspect of the DCF model is the
adjustment of the free cash flow of the commercialisation phase, which is adjusted by the
cumulative success.
Compared to the other valuations methods, the main critic of the DCF model is the lack of
strategic flexibility and the lack of explicitly perceiving the technological uncertainty in each
of the development phases. As we showed in the practical implementation, the only way to
consider events in the future is through sensitivity analysis. We find that the sensitivity is not
an adequate tool for analysing the uncertainty that lies within the development process.
Furthermore, the result from the sensitivity analysis is found from hypothetical changes in
specific values, which makes the tool less useful for unbiased evaluation of unknown future
events.
The uncertain phases of a pharmaceutical development project require a flexible valuation
model, which challenges the simplicity of the DCF model. Compared to DTA, Binomial, and
Quadranomial valuation model, we believe that the DCF model has a low potential for
valuating NN9927, or any pharmaceutical projects in general. Moreover, we find that the DCF
should only be used as an underlying asset, since it offers a simple valuation value without
any flexibility. The potential of the DCF model to valuate NN9927 is overall evaluated as low.
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5.2 Evaluation of Decision Tree Analysis
Applying the theory on DTA to our case project produce a value of US$ 182 million, which is
higher than the DCF value. The estimated DTA value is furthermore higher than the value
found through the binomial option method and lower than the extended quadranomial
method. We believe that the higher DTA value reflects an improved ability to valuate some
strategic flexibility and to recognise the technological uncertainty in each of the clinical
phases in development process.
Taking a deeper look at the practical implementation reveals some interesting facts about the
DTA model. The main issues to address are how the model estimates and perceives
technological uncertainty, and how the discount rate is estimated and applied. Giving the way
we have implemented the model, the technological uncertainties, or probabilities, are added to
the model through backward induction. The probabilities in each of the clinical phases are
very project specific, and we must assume our estimates to be biased. This somewhat
complicates the practical estimation, at least compared to the DCF model. Furthermore, each
clinical phase is attached with great uncertainty, and the amount of risk that needs to be
compensated for is changing constantly depending on each phase, and how they progress. This
fact makes it difficult to reliably estimate a discount rate for the projects commercialisation
phase and thereby ultimately the total project value. As seen in the case study, an easy and
straightforward assumption is to discount the commercialisation phase with the cost of
capital, or cost of equity in our case.
The overall idea and purpose of the model is intuitively good, and the model is able to value
strategic flexibility and incorporate probabilities in each clinical phase. Compared to the DCF
model, the DTA model offers a more detailed valuation process, but at the same time it is more
complex to implement, and requires a rather detailed insight in the technological challenges
regarding the development of the product. However, the model is evaluated to have a better
match to what we believe is important, when valuing NN9927 and in general pharmaceutical
development projects. Since the input variables in the DTA model have several similarities to
the DCF model, we believe it is necessary to be able to estimate the specific technological
uncertainties within each clinical phase for the DTA model to have superior potential. The
DTA model is lastly assessed to have good potential for the pharmaceutical industry compared
to the other valuation methods.
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5.3 Evaluation of the Binomial and Quadranomial approach
Using the Binomial model to valuate project NN9927 results in a positive value of US$ 132,9
million dollars, which reflects the value of the project and the option, affected by only market
uncertainty. The Quadranomial model estimates the value to US$ 355,5 million dollars, which
reflects the project and the option, influenced by both market and technological uncertainty.
The actual option values were, US$ 130,2 million for the Binomial model, and US$ 173,1
million for the Quadranomial model.
The main critic of both approaches is mostly concerning the estimation of input variables
rather than the intuition of the models, which at first seem appropriate in our industry
context. Because ROA and the resulting option value are only as accurate as the estimated
underlying asset, a critical aspect of both the binomial and the quadranomial approach is the
use of DCF as the underlying asset. The option analysis will only be as accurate as the
estimation of the DCF, meaning that if the underlying asset is miscalculated, then the option
value is also miscalculated. This emphasises the need for strong consideration regarding the
choice of underlying asset and relating volatility. Since volatility is what drives most of the
value in both ROA models it is important to consider the estimation of this. Proving the
importance of the estimation of the volatility is the influence it has on the result. In the
binomial case, changing the volatility by 10 per cent resulted in a US $8,6 million dollars
change in project value. Both models therefore depend on the ability to reliably estimate the
volatility. The complex and difficult estimation process decreases the otherwise appreciated
quality of the models’ ability to valuate strategic flexibility.
The extended quadranomial model further depends on the ability to estimate technological
uncertainties, and the model is more demanding to fully exploit the advantage of, since the
requirements to the input are stronger. As just explained, DTA is also dependent on
technological uncertainty, and the ability to reliably estimate it. But opposite the
quadranomial model, it does not rely on the challenging volatility estimation.
At first, the general impression is that both the binomial and the quadranomial model seem
fitting for valuing product NN9927 and pharmaceutical projects in general. The ability to
valuate and incorporate uncertainties and flexibility is well aligned with, what we find as
characterising the pharmaceutical industry. Though, given the complex practical
implementation, both models are restricted by the complexity of the practical implementation,
we found in the previous chapter.
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Furthermore, through our own research of the industry we believe that the potential of using
ROA is highly dependent on the ability to incorporate the uncertainties regarding the
technological development i.e. clinical phase success rates. The clinical phases are the most
characterising and unique element in the drug development, why a valuation model should be
able to comprehend and incorporate these. When considering this, the explained theory and
practical implementation of the binomial model have shown that it does not have the best fit
to the industry. The binomial model only incorporates market uncertainty. It simply lacks the
ability to comprehend, what is important for determining the value of a pharmaceutical
project, resulting in that the large amount of private risk17 that affects the decisions in all of
the development stages is neglected (Amram & Kulatikala, 2000). The extended quadranomial
approach is evaluated to have a better potential than the binomial model, since it incorporates
this important technological uncertainty. The quadranomial option approach, therefore,
challenges the DTA by using option theory to model the uncertainty in the market, while still
incorporating the success rates of the clinical phases.
The application of ROA in general has some issues that are relevant to address. Since the
development of pharmaceutical drugs are heavily dependent on the approval from
governmental bodies such as the FDA, one thing to reflect upon in the use of real options
theory is who gets to exercise the options. The approval or decision right that such bodies has,
constitutes a large exogenous risk that challenges the use of real option models, especially the
binomial model. The question is, does management actually have options? Early phase of
pharmaceutical drug development may have some of the features of a strategic option—in the
sense that today’s investment creates a set of future decisions – there are no significant
options in later stages, just sudden death of a project (Amram & Kulatikala, 2000 p. 17).
Another thing to consider is managerial behaviour and especially irrational behaviour. Real
Option Analysis is subject to many considerations in the estimation of input. In order to make
useful decisions, managers need to be as objective as possible. But it is widely recognized that
the cognitive biases of managers and analysts tend to affect the analysis, creating more or less
predictable distortions (Triantis, 2005). These biases or irrationalities can have a significant
impact on investment decisions. Likewise, company culture and organisational structure can
affect decisions as well. Compensations schemes that have short-term focus may also have an
affect in early-stage projects (R&D), because it will only produce cash outflows. Investment
17 Private risk is assumed to the same as technological uncertainty and is the uncertainty involved in development of
pharmaceutical drugs.
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managers have less incentive to undertake these investments. Applying ROA requires one to
reflect on this and perhaps incorporate such matters into the model, which we suggest for
possible further research.
5.4 DTA vs. ROA
Based on the above evaluation of each model, summary table 5.1 is showing a comparison of
each model. Each model is evaluated by the previously stated criteria and ranked with low,
medium or high, corresponding to how the model matches our description of the
pharmaceutical industry.
Table 5.1: Evaluation of the investigated models
Source: Own creation
From the above evaluation and the summary table, it is clear that the DCF model has a low
potential to valuate pharmaceutical projects. We thereby evaluate both the DTA and ROA to
have a potential in valuation of pharmaceutical development projects. The important question
is then, whether the real options approaches have a better potential compared to the simpler
DTA model. Both the Binomial and the Quadranomial model have a lower usability, but a
higher flexibility, which makes the final evaluation of the real option approach difficult.
Since we have determined that the both Decision Tree Analysis and Real Option Analysis are
suitable methods for valuing pharmaceutical drug development projects, the next to consider
is the discussion and choice between them. The choice between the two different approaches
can relatively simply be evaluated through two parameters – characteristics of the underlying
asset and the underlying risk. This is the focus of the following paragraph.
Evaluation of each model
DCF DTA ROA
Concept Low High High
Uncertainty Low Medium Medium
Flexibility Low Medium High
Usability High Medium Low
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First of all, it is important to look more into depth of the underlying risk of the investment. Is
it diversifiable? Generally investment projects are exposed to a variety of different sources of
risk. This ranges from product price, demand risk, state of the world market, interest rates,
and exchange rates to mention but a few. The question is then which particular risk, or group
of risks, that could affect a project’s cash flow to such an extent that it would change
management’s future decisions (Koller et al., 2010). If commodity prices and their fluctuations
are a key part of the future decisions that are to be made then the key underlying risk is not
diversifiable. Investment projects are often affected by both diversifiable and non-diversifiable
risk but are in most cases more dominated by one of them. If the risk of the underlying has a
low correlation with the overall economic activity, then the underlying risk is diversifiable.
For non-diversifiable risk ROA provides the best and most correct value of the investment. If
we apply the DTA approach, when the underlying risk is non-diversifiable, difficulties in
determining the correct discount rate arise, as mentioned earlier. But when the underlying
risk is diversifiable, the DTA approach is a more appropriate method to use. The projects
payoffs can in each scenario be discounted with the cost of capital of the underlying asset. And
as we have already determined, it is an easier model to implement and use, at least compared
to ROA.
Figure 5.1: DTA versus ROA
Source: Koller et al., (2010) and own creation
The other parameter to consider is the characteristic of the underlying asset. Is it a traded
asset? As demonstrated in our practical implementation of, the models the result from both
ROA and DTA are heavily dependent on the variance of the underlying asset, since we accept
DTA vs. ROA
Underlying
Asset
Non-
traded
asset
Traded
asset
Underlying risk
Diversifiable risk Nondiversifiable risk
Decision Tree
Analysis
Decision Tree
Analysis,
Real Option Analysis
Decision Tree
Analysis
Real Option
Analysis
Page 83 of 92
the MAD assumption. The source of the variance, or volatility, can help determine, which of
the two methods that are the most applicable. If the underlying’s volatility can be derived
from a traded asset, then ROA should be more accurate than DTA, as the key input in the
underlying can be estimated through for example traded commodities. If this is not the case,
the DTA will be the better choice. Since the variance of the underlying asset cannot be derived
from a traded asset, the estimation of it will be largely judgmental and risk of misestimating
the variance is fairly high (Koller et al., 2010).
Returning focus to the choice between ROA and DTA, the objective is now to relate the
development of pharmaceutical drugs to the above and answer the research question. We start
with examining the first parameter – whether the underlying risk is diversifiable or not. The
technological uncertainty enfolded in developing a new drug and the outcome of each of the
clinical phases in the development is not correlated with the overall economy, or at least a
very low correlation. The success rate in a given clinical phase is not correlated with for
example exchange rates, interest, or any commodity in general. And since the main driver in
investing in a new pharmaceutical drug is the uncertainty of the success in the clinical phases
(technological risk), and not really if the economy is in a favourable trend (market risk) when
the drug if successful is launched, the risk must be assumed to be diversifiable. All this
indicates that the underlying risk in developing a new drug is diversifiable. In relation to
figure 5.1 we position our self in left side of the figure.
The other parameter is relatively easy to determine. Since pharmaceutical drugs are not
traded, as mentioned previously, the variance in the underlying asset can be difficult to
determine. This indicates that, from the above framework, DTA is the most suitable method to
use. This is aligned with our own findings, which have indicated that DTA encompasses many
features that match our short review and walkthrough of the pharmaceutical industry, such
as the stringent division of the clinical phases, the length and cost of each phase, and the
success rates. When everything is taking into consideration, we believe that out of the
methods described and reflected upon in this thesis, using DTA in the valuation of a
pharmaceutical development project will provide the best result. From this we answer our
final research question. We find that real options theory, more specifically the quadranomial
approach, to have adequate potential, but we also find that DTA in overall provide a better
practical potential.
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5.5 Future research
Based on the research questions and the above findings of this thesis, some recommendations
for future research are stated in the following.
This thesis has taken an external point of view analysing different valuation models based on
the industry and publicly available data. Another interesting problem statement would be a
future research taking an internal point of view and analyse the DTA and Quadranomial
approach. In this perspective, it would be possible to include management and project specific
data in the case study. By taking this point of view, several aspects of the volatility and
technological uncertainty could be tested in relation to the difficulties of the practical
implementation. The potential of each method is dependent on the availability of data, since it
affects the different methods and the underlying assumptions. An internal point of view would
be an interesting case study in relation to the delimitations of this thesis, but is difficult to
achieve, since the pharmaceutical industry has a high level of confidential regarding R&D
data.
In relation to the internal point of view, it would be further interesting to analyse the
influence of debt financing. The study should thereby analyse the influence of the interest tax
shield in each of the valuation methods and how this would affect the theoretical potential of
each model. Considering debt financing would furthermore include a more in depth discussion
of WACC and a discussion of, whether the leverage-ratio would increase or decrease the total
value of the pharmaceutical development project.
To support our findings of this thesis, a future case study could expand the study and analyse
several different pharmaceutical development projects in different phases. By analysing
different pharmaceutical projects in different phases, it is possible to discuss the influence of
the time parameter. We have previously discussed whether strategic options exist in the later
part of the development process.
In continuation of the findings of this thesis, another interesting focus would be on a
qualitative research study, formulating hypotheses about, why practitioners do not implement
or consider the Real Option approach. The hypotheses would have to be verified or rejected
based on preferably a large field study. More specifically it could be based on interviews, or
questionnaires send out to practitioners. The study could furthermore investigate, how the
Real Option approach is practically implemented by the practitioners, who have experience
and chosen to use these theories.
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6 Conclusion
In order to answer our research question and investigate the optimal valuation method for the
pharmaceutical industry and drug development projects, we first studied the pharmaceutical
industry. The pharmaceutical industry consists of companies that primarily use chemical-
based synthetic processes to develop new drugs. Many large pharmaceutical companies
typically also use biotechnology in the development process, why we did not separate the two
terms and used the term pharmaceutical industry to cover both pharmaceutical companies
and biotechnology firms. R&D, innovation, and patents are terms greatly associated with the
industry and form, what makes it unique and complex. The development process, which
consists of several clinical phases and approvals, likewise characterises a complex and
uncertain business environment. Based on in depth research of the industry we found the
following result. Industry measures on the success rate for developing a successful drug is
around 17 per cent and cost measures range anywhere from US$ 86 to 2.558 million,
depending on the size of operations and therapeutic class. We found that developing new
pharmaceutical drugs are time-lengthy processes with the average time spend on developing
new drugs being 11 years.
In order to assess the theoretical differences we constructed four criteria: ‘Concept’ that
assesses conceptual differences, ‘Uncertainty’ evaluates the perception and measure of
uncertainty, ‘Strategic flexibility’ examines the methods ability to incorporate decisions to
future events, and finally ‘Usability’ helps evaluate, how usable and user friendly the methods
are. We conducted a case study of an early stage pharmaceutical drug project to evaluate the
practical implementation of the methods as well. The overall goal of this case study was not a
numerical valuation but an evaluation of the different methods and assumptions of each
valuation method. Each method was implemented through a five-step model. The steps
ensured a systematic walkthrough of the methods. The implementation required certain
assumptions, such as a division of the development phase and the commercialisation phase, in
which the developed drug is sold. Doing so makes it easier to implement all the methods,
especially the more complex methods. We found that the quadranomial model provides the
highest value, while the DCF models provides the lowest value.
The Discounted Cash Flow model’s key concept is to assess potential future cash flows of an
asset and discounting these cash flows to present value using an appropriate discount rate.
Page 86 of 92
The DCF model perceives uncertainty as risk, thereby also meaning that a more uncertain
environment increases the discount rate and thus decreases the valuation value. The model
base its conclusion on today’s expectations of the future and assumes that management acts
passively through the investment period and because of this not having the possibility of
incorporating future events i.e. lacking the possibility of valuing strategic flexibility. The
relative simplicity of the model makes it, from a theoretical standpoint, uncomplicated to
implement, and the result is easily understood and communicated through an organisation.
Compared to the other valuation models, the DCF model only perceives uncertainty through
the estimation of beta in the cost of equity and does not integrate any technological
uncertainty of any kind. The ability to estimate a correct and reliable beta value is thereby
highly important in the valuation of pharmaceutical projects. We evaluated the DCF model to
have a low potential in valuating pharmaceutical drug development projects.
Decision Tree Analysis is a slightly more complicated model. The basic idea is to create a
decision tree consisting of events and decisions, and the concept revolves around discounting
the contingent cash flows with appropriate discount rates. When using the DTA framework,
uncertainty is perceived as both risk and opportunities. In the context of the pharmaceutical
industry, we modelled uncertainty as technological uncertainty. More specifically the success
rates in the clinical phases. In DTA future events and decisions are incorporated in the model,
enabling management actions during the life of the investment. The main issues to address
are the estimation of the technological uncertainty, and how the discount rate is estimated
and applied. The probabilities in each of the clinical phases are very project specific, which
make them difficult to estimate accurately for a specific project. The overall idea and intention
of the model is intuitively good. Compared to the DCF model, the DTA model offers a more
detailed valuation process, but at the same time it is more complex to implement and requires
a rather detailed insight in the technological challenges regarding the development of the
product.
The most complex model investigated is Real Option Analysis. The concept is to use option-
pricing theory to determine the value of a project. The value is based on the progression of an
underlying asset, which we assume to be the commercialisation DCF valuation in the
acceptance of the MAD assumption. By using backward induction we determined the value of
the project. We investigated two types of real option models; the binomial- and quadranomial
model. In the latter, two sources of uncertainty is separated and incorporated – market- and
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technological uncertainty, whereas in the binomial model only market uncertainty is
modelled. The degree of strategic flexibility is relatively high and can be seen in the broad
variety of different options, such as the option to grow, abandon, or wait. The idea and thought
of incorporating strategy in to the valuation through option theory seems at first appealing
but is let down by its complex and occasionally difficult estimations. The main critic is also
mostly concerning the estimation of input variables rather than the intuition of the models.
We found that the potential of using ROA is highly dependent on the ability to incorporate a
reliable volatility of the underlying asset and the technological uncertainties in the clinical
phases.
Through our investigation of the industry and evaluation of the models, we subsequently
found that both DTA and ROA have features that match the pharmaceutical industry. As we
have determined, the industry is faced with great uncertainty, stringent legislation and
regulations, and an ever-changing environment, which makes the simplicity of the DCF
without real potential. Moreover from our research, the most important and significant
characteristic of the industry is the clinical phases. Therefore an optimal valuation method
must incorporate these, why we advocate the use of DTA or the quadranomial real option
approach, which explicit include the success rates in the clinical phases. In order to single out
one method in particular, we looked more in depth of assumptions behind the models. Based
on a framework examining tradability and underlying risk, we reached the conclusion that
DTA is the best valuation method for valuating pharmaceutical drug development projects.
Page 88 of 92
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