St. Petersburg State University
Graduate School of Management
Master in Corporate Finance Program
Real options for investment analysis of
biotechnological startups: The case of North-West
Technology Transfer Center portfolio company.
Master’s Thesis by the 2nd year student
MCF-2016
Pavel Maslennikov
Research advisor:
Associate Professor, Anna E. Loukianova
St. Petersburg
2016
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Масленников Павел Сергеевич, студент 2го курса магистратуры направления
«Менеджмент», заявляю, что в моей магистерской диссертации на тему “Реальные
опционы для инвестиционной оценки стартапов в сфере биотехнологий на примере
компании из портфеля Северо-Западного Центра Трансфера Технологий”, представленной
в службу обеспечения программ магистратуры для последующей передачи в
государственную аттестационную комиссию для публичной защиты, не содержится
элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских
диссертаций имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что
«ВКР выполняется индивидуально каждым студентом под руководством назначенного
ему научного руководителя», и п. 51 Устава федерального государственного бюджетного
образовательного учреждения высшего профессионального образования «СанктПетербургский государственный университет» о том, что «студент подлежит отчислению
из Санкт-Петербургского университета за представление курсовой или выпускной
квалификационной работы, выполненной другим лицом (лицами)».
______________________________________________________(Подпись студента)
______________________________________________________(Дата)
2
STATEMENT ABOUT THE INDEPENDENT CHARACTER OF THE MASTER THESIS
I, Pavel Maslennikov, 2nd year master student, program «Management», state that my
master thesis on the topic “Real options as a tool for investment analysis of biotechnological
startups: The case of North-West Technology Transfer Center portfolio companies.”
which is presented to the Master Office to be submitted to the Official Defense
Committee for the public defense, does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses, which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major
curriculum programs of higher and secondary professional education at St. Petersburg University
«А master thesis must be completed by each of the degree candidates individually under the
supervision of his or her advisor», and according to paragraph 51 of Charter of the Federal State
Institution of Higher Professional Education Saint-Petersburg State University «a student can be
expelled from St. Petersburg University for submitting of the course or graduation qualification
work developed by other person (persons)».
_______________________________________________________(Student's signature)
___________________________________________________________(Date)
3
АННОТАЦИЯ
Автор
Масленников Павел Сергеевич
Название
магистерской Реальные опционы для инвестиционной оценки стартапов в
диссертации
сфере биотехнологий на примере компании из портфеля
Северо-Западного Центра Трансфера Технологий
Факультет
Высшая Школа Менеджмента
Направление подготовки
Корпоративные финансы
Год
2016
Научный руководитель
Доцент, Анна Евгеньевна Лукьянова
Описание цели, задач и Целью данной работы является разработка рекомендаций
основных результатов
для улучшения процесса инвестиционной оценки стартапов
в сфере биотехнологий при помощи реальных опционов.
Задачами исследования являются: Обзор существующих
методов инвестиционной оценки технологических проектов;
Анализ применимости метода реальных опционов для
инвестиционной оценки стартапов в технологической сфере
и более конкретно в сфере биотехнологий; Формирование
методологии
для
инвестиционной
оценки
биотехнологических стартапов с применением реальных
опционов; Использование разработанной методологии для
инвестиционной оценки проекта «PolySeed» из портфеля
Северо-Западного
Центра
Трансфера
Технологий;
Разработка рекомендации для улучшения процесса
инвестиционной оценки стартапов в сфере биотехнологий.
Результатами работы является получение практических
рекомендаций для менеджеров проекта «PolySeed» и
иллюстрация применения метода реальных опционов и его
преимуществ для оценки стартапов в сфере биотехнологий.
Ключевые слова
Реальные опционы, инвестиционная оценка, биотехнологии,
стартапы
4
ABSTRACT
Master Student’s name
Maslennikov Pavel
Master thesis title
Real options for investment analysis of biotechnological startups:
The case of North-West Technology Transfer Center portfolio
company.
Faculty
Graduate School of Management
Main field of study
Corporate Finance
Year
2016
Academic Advisor's Name
Associate Professor, Anna E. Loukianova
Description of goal, tasks The goal of this research is to develop recommendations to
and results
improve the process of investment analysis including valuation
and risk management of biotechnological startups applying the
methodology of real options. The objectives are to provide an
overview of existing methods of investment analysis of
technological and biotechnological projects; To review real
options method applicability to investment analysis of
technological and biotechnological projects; To formulate
methodology for real options analysis of biotechnological
startups; To apply the formulated methodology to conduct the
investment analysis of NWTTC (RUSNANO group) portfolio
project PolySeed; To develop recommendations for improvement
the process of investment analysis of biotechnological startups.
In the result, the practical recommendations for managers of the
PolySeed project were developed and the implementation of real
options analysis and its benefits were illustrated.
Keywords
Real options, valuation, risk management, biotechnology,
investments, startups
5
TABLE OF CONTENTS
INTRODUCTION ........................................................................................................................ 7
CHAPTER 1 LITERATURE REVIEW ................................................................................... 10
1.1 Investment analysis of technological projects .............................................................................. 10
1.1.1 Valuation methods of technological investments ..................................................................... 10
1.1.2 Stages of new project development........................................................................................... 14
1.1.3 Risks of a technological project ................................................................................................ 15
1.2 Real options for investment analysis of technological projects .................................................. 18
1.2.1 Concept of Real Options ........................................................................................................... 18
1.2.2 The Real Options Process ......................................................................................................... 19
1.2.3 Real options in technological sphere......................................................................................... 20
1.2.4 Common types of Real Options in the technological sphere .................................................... 24
1.2.5 Real options in biotechnological sphere ................................................................................... 26
Summary ............................................................................................................................................... 27
CHAPTER 2 METHODOLOGY .............................................................................................. 29
2.1 Strategic analysis ............................................................................................................................ 29
2.2 Risk analysis .................................................................................................................................... 30
2.1.1 Technological uncertainties ...................................................................................................... 30
2.1.2 Commercialization uncertainties ............................................................................................... 32
2.3 Quantitative analysis ...................................................................................................................... 34
2.3.1 DCF valuation ........................................................................................................................... 34
2.3.2 Decision tree analysis ................................................................................................................ 34
2.3.3 Real options analysis ................................................................................................................. 35
Summary ............................................................................................................................................... 39
CHAPTER 3 CASE STUDY ...................................................................................................... 40
3.1 Description of the project .............................................................................................................. 40
3.1.1 Market of brachytherapy ........................................................................................................... 40
3.2 Investment analysis of the project................................................................................................. 42
3.2.1 Strategic analysis of the PolySeed project ................................................................................ 42
3.2.2 Risk analysis of the PolySeed project ....................................................................................... 43
3.2.1 DCF valuation of the PolySeed project ..................................................................................... 46
3.2.2 Decision tree analysis of the PolySeed project ......................................................................... 46
3.2.3 Real options analysis of the PolySeed project .......................................................................... 47
3.3 Discussion ........................................................................................................................................ 49
CONCLUSION ........................................................................................................................... 52
REFERENCES............................................................................................................................ 55
APPENDICES ............................................................................................................................. 60
Appendix 1: DCF valuation ................................................................................................................. 60
Appendix 2: Decision tree of the PolySeed project............................................................................ 62
Appendix 3: Evolution of the underlying asset of the PolySeed project ......................................... 63
Appendix 4: The binomial lattices of the forth and the fifth stages ................................................. 64
Appendix 5: The binomial lattices of the first and the second stages .............................................. 65
Appendix 6: Strategic lattice for the PolySeed project ..................................................................... 66
6
INTRODUCTION
Today a lot of attention is given to investments in the technological sector, especially in
Russia. Such organizations as RUSNANO, FIEP, Skolkovo Foundation, State Corporation Bank
for Development and Foreign Economic Affairs (Vnesheconombank), Russian Venture
Company, Russian Direct Investment Fund, Agency for Strategic Initiatives, Russian Foundation
for Technological Development are contributing to the diversification of the economy. Each of
listed companies is dealing with the new ventures, which are creating new high-tech products to
compete on the market.
Biotechnology is one of the most attractive technological industries for investors in
Russia and all over the world (Russia biotechnology report, 2014). According to the expert
analysis biotechnologies that have a beneficial effect on human body and quality of life have the
potential to be one of the most profitable industries in the 21st century. The key organization in
Russia that invest in biotechnologies are Institute of Human Stem Cells, Bioprocess Capital
Ventures, Bind Therapeutics, Selecta Biosciences and HimRar.
Biotechnology is a complex term, which is usually includes three areas of the research:
biomedicine, industrial biotechnologies, and agrobiotechnologies. Biomedicine is the
development of new pharmaceutical products, vaccines, molecular diagnostics and so on.
Industrial biotechnologies include industrial processes with the use of biotechnological reactors,
microbial
recycling
of
waste,
producing
of
biofuel
and
biodegradable
plastics.
Agrobiotechnologies is the technologies of remediation of soil, increasing the tolerance and
productivity of plants and son on. This paper focuses mainly on the evaluation of biomedical
startups.
The investment analysis of young firms and especially technological ventures is being
one of the most complicated questions in the literature devoted to the investment practices.
Those companies have a limited history, no revenues; they operate in the very uncertain
environment and sometimes don't even have markets for their products. They require special
methods of investment analysis in other words valuation techniques and risk management tools
that are different from already existing approaches.
There is a gap today between theoretical literature and real practice in terms of methods
that are used for valuation and risk management. Most of the practitioners use classical DCF
approach, which has a number of limitations when dealing with technological investments. It
fails to capture the value of flexibility of the project and usually doesn't provide an adequate
7
treatment of risks. There are more complex and sufficient techniques that can be used to value
the technological investment.
Real options analysis (ROA) is the extension of the financial options theory. It applies the
same methodology to assess the real business problems. Basically, the real option is the
opportunity or choice that becomes available within the particular investment. Like a financial
option, it gives a right but not an obligation to undertake an investment. This instrument has an
advantage over other methods of valuation because it enables to capture the flexibility of
managerial decisions and consider multiple sources of risk.
In this paper, the ROA technique is implemented for investment analysis of the particular
biotechnological project in order to give recommendations for managers of the particular
investment fund. The research also might be interesting for managers who work on similar
problems. The goal of this paper is to develop recommendations to improve the process of
investment analysis including valuation and risk management of biotechnological startups
applying the methodology of real options. To reach this goal the following objectives should be
accomplished:
– Provide an overview of existing methods of investment analysis of technological and
biotechnological projects;
– Review real options method applicability to investment analysis of technological and
biotechnological projects;
– Formulate methodology for real options analysis of biotechnological startups;
– Apply the formulated methodology to conduct the investment analysis of NWTTC
(RUSNANO group) portfolio project PolySeed;
– Develop recommendations for improvement the process of investment analysis of
biotechnological startups.
The Research is organized as follows. The first chapter of the paper is devoted to
literature analysis of the topic. There is a description of existing methods of valuation the
technological projects as well as most important stages of each technology development, it’s
intrinsic risks and methods of risk management.
Furthermore, in the first chapter the concept of Real Options is presented as well as
approach of other authors to the ROA process. There is also the discussion about applicability of
real options in technological sphere, observation of common types of real options models and
review of other papers that are devoted to ROA in biotechnological sphere.
8
The second chapter provides the methodology of the research that includes three steps:
strategic analysis, risk analysis, and quantitative analysis. The strategic analysis implies the
framing of the ROA problem. On the second step, the two broad categories of risks that can be
applied to biotechnological startup are analyzed: market risks and technological risks. On the
third stage, the three methods of valuation including DCF valuation, decision tree method, and
Real Options Analysis are described in order to estimate the value of the project. The chosen
method of Real Options Analysis, the process of its implementation, and necessary assumptions
are presented in this chapter.
In the third chapter, the methodology is applied to the particular project from the
portfolio of North-West Technology Transfer. There is an overview of the project, its external
environment, and potential risks. The formulated methodology is applied in the chapter. In the
result the certain implications that can give managers more flexibility in the process of
investment analysis of biotechnological startups are proposed.
9
CHAPTER 1. LITERATURE REVIEW
1.1 Investment analysis of technological projects
Everything is pretty much clear with the valuation of firms with well-established
operations and long selling history. There are plenty of models and approaches to analyze their
market price and sources of the value. Things are getting more complex when we start thinking
about young companies and especially about those, which are operating in the technological
environment. There are some analysts who argue that such a firms cannot be valued at all.
When valuing a firm or investment project the analysts draw on information from three
sources (Damodaran, 2006): The current financial state of the firm or project, the past history of
the firm in terms of earnings and market prices, and the firm’s competitors or peer group to
measure how much better or worse a firm is than its competitors. While you would optimally
like to have substantial information from all three sources, it is normal to substitute more of one
type of information for less of the other.
In the case of technological startups, an analyst will run into serious information
problems. First, these firms usually have not been in existence for more than a year or two,
leading to a very limited history. Secondly, these companies usually have very few assets in
place and have all value in the growth potential, which is very difficult to estimate due to the
highly uncertain environment. Thirdly, this firms often develop the break through innovation
products, which don’t have any market data or even competitors or peers.
Given all these information constraints its getting obvious that special methods of
valuation are required for technological investments. In the next paragraph an overview of
existing approaches to financial valuation of technology startups will be given. After that the
important issue of stages in new venture development will be discussed. Investment analysis
includes not only valuation of the startup but also the consideration of risks so the typical
uncertainties of the technological project are also presented in this part of the research.
1.1.1 Valuation methods of technological investments
DCF method
The most common method for investment valuation is DCF approach (Beninnga and
Tolkowsky, 2002). In the case of valuation of young technological companies, the method can
also be called Venture Capitalist Net Present Value (VC-NPV). Generally, this method takes free
cash flows generated in the future by a specific project or company and discounts them to derive
a present value. When DCF calculations produce values that are higher than the initial
10
investment, this usually indicates that the investment may be worthwhile and should be
considered.
In the traditional DCF method, the discounting value that is usually used is the weighted
average cost of capital (WACC). In the VC-NPV approach the discounting factor is usually very
high, 20%-100% (Timmons and Spinelli, 2004), 40%-75% (Westland, 2002, p.136). For
example, in North-West Technology Transfer Center (NWTTC) the usual discounting factor for
financial models is relatively high because of the higher risk of new venture creation is
incorporated in this discount rate.
While the DCF method is widely used by practitioners and there are experts who argue in
favor of its applicability for new ventures (Damodaran, 2006, p. 891), it is being criticized a lot
in the financial literature. Furthermore, this trend is not new, first attempts were made by Mayers
and Tynbull in1977 or Hodder and Riggs in 1985. From more fresh articles on that topic, there
are Boer 2000, Benninga and Tolkowskiy 2002, Steffens and Douglas 2007, Schachter and
Mancarella 2016. There are two main limitations of DCF analysis: It doesn’t explicitly consider
multiple scenarios and it doesn’t provide a flexible treatment of risk.
The first limitation is derived from the DCF assumption of passive management meaning
that investment is made only once at the beginning of the project without the ability to postpone
or dynamically make changes to the investment project. Given assumption doesn’t satisfy the
dynamic nature of technological startups where everything changes very fast. The DCF approach
analyses single (most likely) scenario and ignores hidden opportunities and risks. Moreover, in
the technological sphere a lot of spendings are irreversible and represent high sunk costs for a
firm. So that costs are “locked-in” within a project but will affect future decisions that will be
made.
The second limitation is that the only treatment of higher risk in DCF is simply the
assigning of a high discount rate so it fails to capture the value created by managerial flexibility
in the treatment of risks. The DCF model fails to distinguish between market risk and firm
specific risk so in the result it is good only in treatment of the first one but much of the risks
associated with a new venture is specific (McGrath and MacMillan, 2000). Furthermore, a timebased penalty (i.e. discount rate) is not appropriate for most specific risks, which largely vary in
a lumpy manner at discrete times rather than continuously over time.
Despite all critics the DCF method is still a good benchmark for investment analysis and
the base for all other more complicated techniques. The doubtful benefit of this method is
simplicity, which prevents analyst from the mistakes and enables to get the solutions very fast.
11
The Market Comparables method
The given method uses data from public companies and private market transactions to
estimate value. It gives adequate indications of what market can pay for the company. There are
different ways to use the information from the comparables to estimate the value of the subject
property. One approach is to consider each factor one at a time and compare the averages to the
subject. A better approach, if sufficient data are available, is to build a statistical model to the
data, for example, by using multiple regression. The model can be used to assess the marginal
effect of each factor in conjunction with the effects of the others.
The Market Comparables method can be applicable to the valuation of new ventures and
it is, arguably, delivers value estimates that come close to what investors are willing to pay, but
unfortunately, there are some problems with this approach. It is rather obvious that the method is
not working with disruptive technologies but it also may not be applicable to sustaining
technologies.
Mostly, because data about comparable companies is difficult to obtain. It's not easy to
find companies on the startup market themselves and it is even harder to get access to the deal
terms which are usually kept under wraps. Furthermore, if the company and its data would be
found it is not necessary that it is at the same stage of the development with our company.
Hence, it is highly unlikely that suitable market data is available for establishing a valuation
using the market comparables method.
First Chicago method
Rather than limiting the analysis to a success scenario, the First Chicago method (Smith,
2011) uses probability-weighted scenarios to come up with a more reliable estimate of expected
(in the statistical sense) cash flows, rather than just the optimistic cash flows used in the VC
method. These expected cash flows are then discounted using a more realistic cost of capital,
rather than the high hurdle rates used in the VC method. If the scenario probabilities are
correctly weighted, the appropriate discount rate is identical to the one that would be used in
DCF valuation by the. A benefit of the First Chicago method is that it requires the analyst to
think about the range of possible outcomes for the venture and their probabilities.
Typically, three scenarios are used: the “best guess” (most likely, median case); the “best
case” (optimistic) and the “worst case” (pessimistic). For each of the three scenarios,
management must estimate the subjective probability that the scenario will occur and the cash
flows for each scenario are estimated as for VC-NPV method. The valuation is equal to the
expected (probability-weighted) NPV of the three scenarios.
12
The advantages of this approach are that some of the risks associated with the venture are
identified and different types of risk are separated and explicitly assessed. As such, this approach
acts as a starting point for dealing with the risks in the more flexible manner. An important
consequence of dealing with many sources of risk explicitly is a reduction in the sensitivity of
the valuation to the discount rate – which is now being used to account for less of the overall risk
facing the venture.
However, there are some important limitations of this method. The most crucial is that
the method doesn’t provide any framework of how to value potential flexibility in managerial
decisions.
Decision tree approach
Decision Tree Analysis (DTA) has a long tradition in management science and has been
around since the 1960 (Raiffa, 1968). Here we discuss not the full convention of ‘decision
analysis’ that involve constructing (or defining) the utility function of the decision maker, but
rather the simple DTA which involves calculating the expected NPV using a decision tree.
Long back the decision trees were the only tool to capture the value of flexibility under
uncertainty. However, they use they popularity was limited due to complexity (Diallo, 2000).
DTA handles the sequential risk with discrete outcomes while there are difficulties in the
situations with a wide range of outcomes. DTA is also good when dealing with firm’s specific
risk (Steffens and Douglas, 2007) and is not good in terms of assessing the market risk.
According to Dzuma 2012, the classical methods of valuation may give a biased result in
the situations like:
– Projects that have the option to delay to collect more precise information about current
state of the market;
– Projects the implementation of which gives opportunities to launch another project;
– Projects during the implementation process of which will be needed to make the
decisions to change the volume of the production;
– Projects that can be abandoned;
– Multi-stage projects in which implementation of one phase depends on the successful
completion of the previous one; This situation is typical for investments with substantial
initial costs, for example, to carry out research (pharmaceutical industry, biotechnology,
real estate).
13
1.1.2 Stages of new project development
Every single startup company is going through several stages during its development.
Why it matters in the context of valuation? Because the perception of risk, nature of options will
change from stage to stage. For example, in the early stages, a startup will be riskier for investors
as it has limited operation history without any revenues.
There are usually about five or six stages that are mentioned in the literature (Smith,
2011) (Damodaran, 2006). In this paper, six stages will be discussed: Opportunity stage,
research, and development, start-up, early growth, rapid growth and exit. Each stage is
characterized by its own options and sources of value.
Figure 1. Stages of new venture development.
Opportunity
R&D
Startup
Early-growth
Rapid-growth
Exit
Source: Smith, 2011
Opportunity stage
The main activities on this stage are: to obtain seed financing, assess strategic
opportunities, determine organizational structure and form and to prepare the business plan.
Opportunity staged is not characterized by the significant capital inflows. The source of value
that the company has at this stage is entirely the future growth. The options that the project
usually has are to continue to the next stage, modify the concept or abandon the project.
Research and development stage
On this stage the company is obtaining R&D financing, building research team,
conducting R&D, assessing and upgrading business plan. The source of value on this stage can
be in the form of patent or other intellectual property but not in the form of real revenues. The
options for the venture are to continue to the next stage, extend stage financing, modify R&D
strategy, abandon the project.
Startup stage
On the start-up stage venture should obtain the financing to initiate revenue generation, to
initiate the production, to build sales and marketing team and to acquire facilities and equipment.
Beginning from this stage the company can be compared with other companies on the market.
On this stage there are options to continue to the next stage, to modify production or financing,
to modify marketing and to abandon the project.
14
Early growth stage
On this stage companies usually obtain financing for further growth, work toward
revenue breakeven, expand team and facilities if needed. The main source of revenue still relies
on the further growth but already with a portion of existing assets. The options are to continue to
the next stage, extend stage financing or to abandon the project.
Rapid growth stage
The only difference with previous stage is that the company on the rapid growth stage is
reaching economic breakeven and working towards proven viability to attain investments. On
this stage, the operating history of the company can be already used in the valuation. The options
of the company continue to the next stage or to extend the financing.
Exit stage
On the last stage companies usually do activities to establish a continuing financing such
as IPO, acquisitions, buyouts and so on. The source of valuation on this stage is mainly the
existing assets. The only option that the ventures have on this stage is to choose the form of exit.
1.1.3 Risks of a technological project
Every company or project is influenced by the different factors of risk. This influence can
be both negative and positive. That is why the process of risk management is so crucial
especially for investment projects. Risk management is the process of identification, analysis and
either acceptance or mitigation of uncertainty in investment decision-making. The stages of
identification and assessment of risk are the important part of this research.
In traditional financial literature, there is a distinction between market and firm-specific
risk. Market risk is that part of risk correlated with the market (known as systematic risk),
whereas firm-specific risk (known as private or unsystematic risk) is unique to the firm
(Copeland et al, 2005).
Dixit and Pindyck in their book Investment Under Uncertainty
distinguish between two form of uncertainty for the technological project: “technical”
uncertainty and “input cost” uncertainty.
Technical uncertainty refers to the probabilities of costs and probabilities of
accomplishing technical success; a firm reduces this type of risk only through investment.
Technical uncertainty creates pressure on the firm to invest immediately. Delays, at best, incur a
discounting penalty and, at worst, expose the firm to the risk of competitive preemption. Input
cost uncertainty relates to factors exogenous to the firm. No amount of investment makes a
difference in this form of uncertainty, which creates pressure on the firm to delay investment
until information is revealed with the passage of time.
15
In McGrath, 1997 the third type of risk was suggested and called “external” risk. This
type of risk is presented when sources of uncertainty are caused by external factors, not technical
in nature and can be influenced by the strategic action of a company. These are risks that
technological company can hedge and that are consequently influencing the value of a
technological option.
Figure 2. Factors influencing the value of technology option.
Source: McGrath,1997
The risk associated with the level of demand is related to the market size for the
technology (disruptive technologies) and the market share the firm will capture (disruptive and
sustaining technologies). These uncertainties are both related to the market factors and actions of
the particular firm.
The speed of adoption is the very important factor that affects the size of the revenue
stream. People are slow to change their existing, are resistant to making time investments which
are necessary to learn the new way of doing things, and reluctant to throw their old assets. So the
adaptation usually takes longer that was anticipated in the beginning. When adaptation is slow
additional resources are needed to push the product on the market. Moreover, competitors have
more time to respond or even leapfrog the idea.
Blocking occurs when the business is preventing from accessing customers, sales
channels or other critical resources as the result of actions of the third party that can be
competitors, government, etc. Massive regulatory hurdles, for example, affect biotechnology
industry, on the stage of R&D. The problem with blocking is that it can either stop the business
16
or require enormous investments and managerial decisions to continue.
Expropriation is similar to blocking in a way that firm faced a threat from the more
powerful external players but it differs in the outcome. When it is blocked, the firm is denied
access to critical resources and markets. Under expropriation, however, the firm is required to
give away a portion of its cash flows. For instance, such a threat may come from the
government, which can increase the tax burden of the firm in the national interest.
The next portion of uncertainties is dealing with sustainability of revenue streams.
Duration of competitive advantage influenced by the market response. Matching occurs when
after the success in addressing important customer problem, competitors are able to address the
same problem but doing it with technology or other recourses proprietary to themselves.
Imitation is also an important threat because the product, which is easy to imitate, is more likely
be able to preserve price premiums or sustainable market share.
The next big group of uncertainties is related to the cost side. The obvious cost factors
include investment to create and distribute the product or service, the creation of production
facilities, hiring and organization of staff, and marketing expenses. These investments can be
broadly called infrastructure investments and, of course, to the extent that the availability of such
infrastructures is uncertain now or in the future, the expected costs of commercialization and
their variance will be greater.
Two other categories of costs in the model are more specific and occur when the
additional product is needed to support the existing one. This could be in the form of parallel
supportive technology or other cospecialized assets. The greater the potential is to deploy
existing parallel technologies or uniquely owned cospecialized assets in conjunction with the
proposed technology, the greater the value of the technology option will be.
Spillover effect refers to the situation when a company utilizes the developing technology
to enter one modest market with the to target other markets when this technology is sufficiently
well developed. In this case, technological uncertainties are reduced because the company
already have the relevant experience.
Technology life-cycle status matters a lot in terms of risks, as in the periods of
incremental changes firms innovate in the context of dominant design. That means that key
dimensions of the market have been established, the customers have been educated, and
coexisting technologies share a common architecture. In the satiation like this, clearly, the risks,
as well as the costs, are lower, while in the periods of disruptive change firms need to make more
investments.
17
1.2 Real options for investment analysis of technological projects
1.2.1 Concept of Real Options
Literally, the real options approach is the extension of financial option theory to options
on real (nonfinancial) assets. While financial options are detailed in the contract, real options
embedded in strategic investments must be identified and specified. Moving from financial
options to real options requires a way of thinking, one that brings the discipline of the financial
markets to internal strategic investment decisions (Amram and Kulatilaka, 1999).
The real option is right but not the obligation to undertake certain management initiatives.
It can be seen as call or put option on underlying incentive. The connection between vital
parameters of a financial call option and a real option are given in table 1.
Table 1
Parameter comparison of a Financial and a Real option
Financial option on the stock
Real option on a project
Current value of the stock
Present value of expected cash flows
Exercise price
Investment cost
Time to expiry
Life of project opportunity
Stock price volatility
Uncertainty of the project
Risk free interest rate
Risk free interest rate
Source: Hull, 2006.
Real options investments are characterized by sequential, irreversible investments made
under conditions of uncertainty (Dixit and Pyndyk, 1994). The framework suggests that in the
beginning investor purchases the option and then, during the course of the holding period, the
value of option changes in response to external events. Normally, when market volatility
increases the option price is also increases (Oriani and Sobrero 2008).
As it was discussed previously the NPV approach has a number of important limitations.
That was Stewart Mayers who said: “Strategic planning needs finance. Present value calculations
are needed as a check on strategic analysis and vice versa. However, standard discounted cash
flow techniques will tend to underestimate the option value attached to growing profitable lines
of business. Corporate finance theory requires extension to deal with real options» (Mayers,
1984). Moreover, the benefit of real options analysis is that it incorporates volatility, whereas
decision trees (which rely on NPV analysis) only compute expected values.
18
1.2.2 The Real Options Process
In the literature, there are different approaches to illustrate the implementation of the Real
Options Analysis. In Morano (2014) the three basic stages of Real Option Process were
discussed: Risk analysis, strategic analysis and quantitative analysis.
The path is organized in a “cascade” mode, so the results of each phase form the starting
point of the next, the connections between the various steps do not allow a clear separation and
give rise to a linear iterative process, which can sometimes require the return on the factors
already analyzed, in order to deepen the study or broaden the spectrum of investigation.
In Mun (2002) the three main stages of Real Option Analysis implementation are
extended to 8 steps:
Qualitative management screening
On this step management decides which projects, assets or strategic initiatives are viable
for further analysis, in accordance with the firm’s mission, vision, goal or overall business
strategy. The insights are created as management frames to complete a problem. Also on this
stage, various risks of the project are identified.
Time-Series and Regression forecasting
The future is forecasted using time-series analysis or multivariate regression analysis if
historical or comparable data exist. Otherwise, other qualitative forecasting methods may be
used (subjective guess, growth rate assumptions, expert opinions, Delphi method, and so forth).
Base case Net Present Value analysis
For each project that passes the two initial stages, a discounted cash flow model is
created. This model serves as the base case analysis, where a net present value is calculated
using the traditional approach of using the forecast revenues and costs, and discounting the net of
these revenues and costs at an appropriate risk-adjusted rate.
Monte Carlo simulation
Because, the static DCF produces only a single- point estimate result, there is often a
little confidence in its accuracy given that cash flows are highly uncertain. To better estimate the
actual value of a particular project, Monte Carlo simulation should be employed next.
Real Options problem framing
The next critical step is to frame the problem within the context of ROA. Based on the
overall problem identification occurring during the initial qualitative management screening
19
process, certain strategic options would have become apparent for each particular project. The
strategic options may include, the option to abandon, to switch, to contract, to expand and so
forth.
Real Options modeling and analysis
Through the use of Monte Carlo simulation, the resulting stochastic discounted cash flow
model will have a distribution of values. In ROA, it is assumed that the underlying asset is the
future profitability of the project, which is the future cash flow series. An implied volatility of
the future cash flows or other underlying assets can be calculated through the results of Monte
Carlo simulation. Usually, the volatility is measured as the standard deviation of the logarithmic
returns on the free cash flow stream. Using the implied volatility and present value of future cash
flows the ROA is performed to obtain the project’s strategic option values.
Portfolio and resource optimization
This is the optional step of which analysis is done on multiple projects. This is actual
only for companies who have portfolio of projects because there are opportunities for hedging
and diversifying risks through the portfolio.
Reporting and update analysis
Real options analysis assumes that the future is uncertain and that management has the
right to make midcourse corrections when these uncertainties become resolved or risks become
known. Therefore, when it happens, the analysis should be revisited to incorporate the decisions
made or revising any input assumptions.
1.2.3 Real options in technological sphere
There is a discussion on whether ROA (Real Options Analysis) is a good instrument to
value the investments in the technology in general and more particularly in young technological
startup companies. In Adner and Levinthal 2004, it is stated that there are two critical features of
the ROA process: 1) The value of the option and and the underlying asset is exogenous to the
investor's activity – the investor cannot take steps to make the intrinsic characteristics of the
asset more attractive; 2) The market signal of option value is readily observable and is
independent of investors’ behavior.
These two statements imply that in the industries where market and technological agenda
are flexible such as in the most technological industries the implementation of ROA would be
problematic. There would be too many degrees of freedom for ruling out success. However, the
20
firm can have a stricter action mandates, the more formalized milestones, organizational
structures that are more tolerant to failure and so on, to cope with these difficulties.
The next portion of criticism comes from the notion that ROA is good at treatment of
market risk but not the firm-specific risk, which investors usually want to be compensated for.
This comes from the original ROA method that identifies a replicating portfolio of traded
securities that match the volatility of the investment. Nonetheless, it is extremely hard to find
such a security for any technological investment (Steffens and Douglas, 2007).
Another and more common method for volatility determination is the risk neutral
valuation this method is often easier to implement. Both methods build on no arbitrage
opportunity and assume that all investors are neutral towards risk, which is the basis for a risk
neutral valuation. This allows all assets in the market to be valued regardless of the different
individual risk preferences. This assumption is valid in a financial setting under the assumption
that risk can be hedged or completely eliminated at every time step by buying and selling the
right quantity of shares and options, known as a risk-neutral portfolio (Hull, 2006).
However, since RO are usually not traded assets, a portfolio of RO and shares cannot be
traded to hedge an investor’s risk, invalidating the risk-neutral assumption (Hugonnier and
Morellec, 2007) (Sick, Gamba 2010). In Schachter and Mancarella 2016 it is suggested to use
different discount rates depending on the level of risk associated with the cash flow. Possibly this
rates may be different at distinctive points in time to reflect the different level of uncertainty
while the future unfolds.
The uncertainty estimation is very important part of real option analysis. In the BlackScholes model one can use either historic volatility or the implied volatility for calculating the
option value. The historic volatility of the stock can be computed by estimating the standard
deviation of continuous price return of a series of recent stock prices. As an alternative option,
the stock’s volatility can be derived from the market price of a stock by inverting the BlackScholes formula. The volatility parameter that is estimated using the latter method is called the
implied volatility.
When implementing real option models to the technological projects, estimating the
volatility parameter for the underlying asset is much more difficult because the relevant market
data is rarely available. In case of technological projects it is more appropriate to estimate the
volatility of project value rather than volatility of comparable stock that usually do not exist.
Since the underlying asset for a real option is the gross project value it is important that one use
the volatility of underlying asset value. There are a lot methods present in the financial literature
to estimate the volatility of underlying cash flows.
21
ROA methods can be classified into three types: Analytical or continuous methods,
discrete time or lattice models and simulation models. The basic features of each method and it’s
applicability to valuation of technological investments will be discussed next in this paragraph.
Analytical methods
All analytical methods are mostly the modifications of the Black-Scholes model which is
described below. They allow deducing an analytical price of the option by creating a
mathematical expression to value its dynamics over time. Analytical methods are exact, quick,
and easy to implement with the assistance of some basic programming knowledge.
! = #$ % &' − )* (,-./) %(&1 )
(1)
2 = )* (, 345)% −&1 − #$ % −&' (2)
&' =
ln (
#$
:1
) + 34 +
5
)
2
(3)
< 5
&1 = &' − : 5 (4)
Where c is a value of the call, p is a value of the put, #$ is the price of underlying asset, K
is the exercise price, rf – risk free rate, : is volatility of the asset, T is the time of expiry of the
option and finally N is the cumulative probability distribution for a normal distribution.
However, these kinds of methods are criticized a lot in the classical ROA literature and
literature devoted to implementation of ROA in the technological sphere. Analytical methods are
also called closed-form because they are difficult to explain, as they tend to apply highly
technical stochastic calculus mathematics.
They are also very specific in nature, with limited modeling flexibility. Closed-form
methods are very precise for European options but can be only approximations for American
options. Moreover, many more complex compound and Bermudan options cannot be solved with
analytical methods.
The “Black-Scholes” model is by far the most numerically tractable model we have for
valuing options, but are its assumptions appropriate for ROA? Almost not, because it assumes
continuous trading, constant interest rate, and no exercise before final option maturity. The
model is considering only one uncertainty, or at most two correlated once, which limits its
implementation on difficult technological projects.
The “Black-Scholes equation” relies on the uncertainty following a stochastic process
with constant mean and volatility over time. On technological markets have changing mean and
22
volatility over long timescales (Brigo, 2007). Again assumption is better suited for operational
decisions of technological or engineering companies, as in Kitapbayev, 2013.
Simulation methods
These types of models are based on Monte-Carlo simulations. Implementation of the
simulation gives an opportunity to incorporate into the ROA many scenarios and several sources
of volatility. Simulation models can price options for any stochastic process with different
probability distributions, thus extending the valuation to assets with non-normal or nonlognormal returns too.
The example of simulation approach is the Datar-Mathews method (Datar and Mathews,
2007). The method provides an easy framework to conduct ROA of a project simply by using the
average of positive outcomes for the project. The approach can be seen as an extension of the net
present value (NPV) multi-scenario Monte Carlo model with an adjustment for risk aversion and
economic decision-making.
As it was discussed above the terms N(d1) and N(d2) are used in the calculation of the
Black–Scholes formula, and are variables related to operations on lognormal distributions. The
Datar–Mathews method does not use N(d1) or N(d2), but instead typically solves the option
problem by means of Monte Carlo simulation applicable to many different types of distributions
inherent in real option contexts. When the Datar–Mathews method is applied to assets with
lognormal distributions, it becomes possible to visualize graphically the operation of N(d1) and
N(d2).
However, despite of it’s methodological excellence simulations methods are still
complicated for practical usage and what is more important they don’t provide clear strategic
framework for managers.
Lattice methods
To solve the American option problem, Cox, Ross and Rubenstein developed the
binomial tree which provides discrete time approximation to the Geometric Brownian Motion
process (GBM) and computing the value of exercising an option earlier than at expiry. At every
time period, the value from immediately exercising the option (known as the exercise value) is
compared with the value from holding this option one extra time period in the future (known as
the continuation value which is based on the expected value of the option).
The basic assumption of the binomial model for pricing options is that the options market
is the (supposed) efficient, i.e., speculators are unable to obtain excessive profits from the
combination with the basic tools and options with the simultaneous buying and selling of both.
23
Provided that, if known to the base price, the possibility that price changes in one direction or
another, risk-free interest rate, you can calculate the price of the option with the set deadlines.
There are generally two types of methods to calculate an option in the lattice framework:
replicating portfolio and risk-neutral valuation. The replicating portfolio method is based on
finding a security in the market that has the same or very similar cash flow as the project the
company is considering undertaking. Based on the law of one price, if the so called twin security
and the investment have those characteristics they will have the same value.
Risk neutral valuation is a more common method than the replicating portfolio approach
to value real options and often easier to implement. Both methods build on no arbitrage
opportunity and assume that all investors are neutral towards risk, which is the basis for a risk
neutral valuation. However, in the risk neutral valuation the search for a twin security is
unnecessary since the method approaches the valuation from the theory of option pricing.
Lattice methods are convenient, very popular and are analogous to dynamic programming
as they rely on backward-recursion through Bellman's optimality principle. However, they have
some important limitation such as: difficulty with incorporating multiple volatilities, the
underlying asset should follow lognormal distribution and practical flows of replicating portfolio
and risk-neutral probability methods.
1.2.4 Common types of real options in the technological sphere
There are plenty of real options types or as they are also called Real Options models. The
most common are the Option to defer, Option to expand, Option to contract, Option to abandon
and Option to switch. The more complex real options models still exist, including compound
options, which are basically the combinations of the five most common options. Compound
options are widely used in technological sphere because usually projects in this field have
multiple stages depending on the success of previous stage. In this paragraph the main types of
real options will be observed.
Option to defer
An option to defer enables a company to defer its investment decision for some period of
time or until more information about the project is available. It can be seen as the American call
option on the project value. For example, we can imagine a company that has a new product
under development and protected by the patent, then there is an option to wait the favorable
conditions on the market. This option is widespread in technological project because they
operate in the uncertain markets. The value of an option to defer can be denoted as max(V – I, 0),
where I is initial investments and V is the present value of the projects cash flows. When the
24
value of the project will exceed the value of the investments the company will exercise its option
and launch or go ahead with the project.
Option to expand
This option is enables the company to expand its current production. The option to
expand can be interpret as an American call on the value of the additional output. In
technological sphere the Option to expand is usually involved into compound option. For
example, company that has the production facilities is waiting for a patent and wants to expand
the production if it will get it. Intrinsic value of an expansion option is denoted as max(pV-I-V,
0), where V is the present value of the projects cash flow, p is the percent of expansion of the
project and I is the initial investment cost.
Option to contract
Is the opposite of the expansion option and enables the company to reduce the output if
market conditions turn out to be unfavorable. Option to contract is analogous to American put
option on the capacity installed. For example, the company that is operating in an uncertain
environment, where demand for its product is decreasing. Then the management has the option
to shut down one of their production lines and therefore to decrease their maintenance and
production cost. The value of the right to decrease an output is max(I – pV, 0) where I is the cost
saved by contracting, p is the percent of reduction of the project and V the present value of the
projects cash flow.
Option to Abandon
If a project turns out to be unsuccessful the company has the right to close it and may be
sell it for a salvage value instead of keeping it going and facing losses. Option to Abandon, can
be seen as the American put on the project value and denoted as max(V-S, 0) where S is the
salvage value. In technological industry it is the common case when larger company for salvage
value merges one company with the bright R&D but unsuccessful business model.
Option to Switch
This type of option enables the company to switch between either input materials or
output products. It is particularly valuable in uncertain environment where prices can fluctuate
very frequently. With a switching option it can be possible to choose from one project to another
as if the previous project is unsuccessful but has some knowledge that the second project can
benefit from. A switching option may give companies a considerable competitive advantage.
25
Compound option
Compound option analysis refers to the situation when existing of one option generates
another. For example, at the R&D stage of a new drug company has several stages of
technological development, clinical tests and receiving the final approval. Every step depends on
the previous stage because it can be either terminated or continued depending on technological
success of the drug.
A compound option derives from the value of another option but not from the value of
underlying asset. The first investment creates right but not the obligation to create next
investments which in turn gives the possibility to make further investment and so on. A
compound option can be sequential or parallel (Kodukula and Papudesuk, 2006). If a company
must exercise an option to create another one, it is considered as sequential option and in
opposite case the compound option is parallel.
1.2.5 Real options in biotechnological sphere
There are a number of representative studies in the financial literature that were aimed to
value the biotechnological firm using the Real Options Analysis (Cassimon 2004, Fujiwara
2015, Kellog 2000, Loch 2002, Wang 2015, Zabolotskij 2008). The given studies apply different
approaches to the problem. The approaches mainly differ by the ROA models that are used and
the types of options that are estimated.
In Kellog (2000) and Zabolotskij (2008) the binomial lattice method with risk-neutral
valuation advocated by Cox, Ross and Rubenstein (1979) was used. The key insight of this
approach is that because the option value is independent of investor’s risk preferences, the same
value will be obtained even when everyone is assumed to be risk neutral. This important
assumption simplifies the calculations by eliminating the need to estimate the risk premium in
the discount rate. Also, it is important that the method enables to estimate the market value of the
project rather than a subjective or private value.
Another approach is to evaluate options with dynamic programming (Loch 2002, Wang
2015). The given method doesn’t require asset replication which is beneficial in case of
technological projects. The drawback of the dynamic programming approach is that it does not
address the question of the correct risk-adjusted discount rate. Dynamic programming requires
an exogenously specified discount rate that reflects the decision maker's risk attitude. Moreover,
this approach doesn't provide a decision framework for the mangers but rather calculates the
final value of an option.
26
Concerning the nature of Real Options that are applicable for biotechnological projects
the most common one is the option to abandon the project. Almost in every case the
management have a possibility to close the project and receive a salvage value from selling the
patent rights or equipment or other assets. The value of such flexibility is contributing to the
present value of the project.
Another model is growth option which is substitutable to the abandon option so they can
be presented in the project at the same time. The idea for the growth option is that engaging in
the development of the project is similar to purchasing a call option on the value of the
subsequent project. By engaging in the development of the biotech product, the company earns
the right but not the obligation, to conduct the subsequent development.
The third most common real option in biotechnological sphere is the option to defer the
project. For instance, if one of the stages of the development process was not successful
management can may not just close the project but try again or wait until the more favorable
conditions on the market.
The most accurate way of analyzing multi stage projects within the real options
framework is the sequential compound option model (Cassimon 2004). In the given model every
step of the development process is described as a European call option. Only after exercising one
option the company have a right but not the obligation to continue the next stage. The series of
options lattices are considering one after another and it gives more accurate results than creation
of only one lattice.
The specify of a biotechnological sphere or even every technological development is the
possibility to estimate the Rainbow Option. There is such possibility because technological and
market uncertainties are not correlated with each other. The Rainbow Option means that the
option has multiple uncertainties in the case of biotechnologies it is the probability of proceeding
to the next round of development and possibility to commercialize the technology.
Summary
The given chapter is devoted to the review of existing approaches to investment analysis
of technological startups and description of Real Options concept as well as specifics of its
implementation in technological area. Such approaches to valuation of technological projects
like DCF analysis, First Chicago, market comparables, and decision tree methods do not allow to
capture the full flexibility of technological investments. Moreover, these approaches do not value
properly the multi-stage projects in which implementation of one phase depends on the
successful completion of the previous one, which is typical for biotechnological sphere.
27
Every technological startup has the number of stages or milestones including opportunity
stage, R&D stage, startup stage and other that are different in terms of uncertainties. The risks
can be classified as market uncertainties systematic or firm specific and technical uncertainties.
The real options approach is the extension of financial option theory to options on real
(nonfinancial) assets. The real option is right but not the obligation to undertake certain
management initiatives. It can be seen as a call or put option on underlying incentive. The ROA
process includes risk assessment, net present value calculation, real options problem framing and
quantitative analysis.
The ROA technique can be applied to technological projects, among three main methods
of real options the binomial (lattice) method is most common and convenient for managers as it
allows to build a consistent framework of the project. There are several different types of real
option types or models such as the option to defer, option to expand, the option to contract, the
option to abandon, option to switch and the compound option.
There are a number of papers that were aimed to value the biotechnological firm using
the Real Options Analysis. According to these studies, the most accurate method to value the
biotechnological development is to use sequential compound rainbow option. The sequential
compound option exists when the project has multiple stages and each step depends on the
previous. The rainbow option refers to the situation when the project has different uncertainties,
for instance, technological and market uncertainties.
28
CHAPTER 2. METHODOLOGY
The new ventures that are focused on creation of new biotechnological products have the
number of common traits. These features can be called the specifics of biomedical startups. They
refer to the process of product research and development, specificity of risks and the way of
“cashing out” the investments. These specific traits will affect the design of real options and are
important in further analysis.
This chapter is devoted to the methodology of real options analysis process of new
biotechnological startups. As it was discussed above the ROA process has several required steps.
These are Strategic analysis, Risk analysis, and Quantitative analysis. In this chapter the
methodology will be described according to given sequence.
2.1 Strategic analysis
In the stage of strategic analysis of biotechnological project management should identify
the areas of managerial flexibility, or the strategic opportunities built-in in the project and which
the investor could provide. The flexibility is needed to hedge against technical and market
uncertainties. Out of the three main steps of ROA process, strategic analysis is the most
subjective step. The analysis defines the characters of the options provided, necessary to quantify
their value in the later stage of quantitative analysis. In fact, the type and the algorithms that
synthesize the condition of exercise are necessary to identify for each option.
First of all, on the strategic analysis stage is important to identify the potential market
drivers, for example, unmet customer’s needs that influence the process commercialization of the
company’s product. It is also needed to forecast the information about the future cash flows by
making realistic assumptions about the size of the market, the market share, and the price of the
product. In some cases, it is possible to obtain the information about similar deals in the industry.
The way of exit is also matters, the company may establish manufacturing on its own facilities or
it can sell the technology license to the another company which is the most popular case.
When entering into the license agreement the type of the contract is also matters. In some
situations, the startup could receive royalty payments during the whole period of the technology
being on the market. The other case is when buyer of the license is guaranteeing to finance the
R&D stage.
For biotechnological projects, the main source of flexibility is hidden in the R&D stage.
The given step of the project can be divided into several phases and the milestone approach is
usually used to control its progress. An initial investment is similar to the purchasing of an
option on a future investment. The decision makers also have the option to stop or defer the
29
project at the end of each phase. Therefore, each phase is an option that is contingent on the
earlier exercise of other options. If the project is a technical success, then it creates the option to
make a significantly larger investment in the continuing project with relatively higher expected
net benefit. If the project fails to achieve the technical success, then there is no need to commit
any further resources, and therefore, the downside risk is limited to the initial investment cost of
the R&D phase. It is also important to mention that if one phase of the R&D process was not
successful, managers have the option to defer the project to improve the initial product so that it
can pass through that stage.
In the beginning of the strategic analysis, it is useful to build the decision tree of the
project that will include the most important milestones. The weighted average value of the
project that will be obtained after decision tree analysis is a good proxy for investment analysis.
However, one should not forget that decision tree doesn’t evaluate the full range of the options
of the project.
2.2 Risk analysis
Every technological startup is the subject for a big number of risks that have different
nature. Of course, biotechnological startups and more specifically biomedical startups have its
own specifics that should be considered. The specifics mainly refer to the process of
development and commercialization.
According to Schwartz and Moon (2000), there are three main types of uncertainties in
pharmaceutical R&D process: technical uncertainty associated with the success of the R&D
process itself, an exogenous chance for obsolescence, during and after the development process
and there is uncertainty about the value of the project on completion of the R&D. The former
uncertainty refers to firm-specific risk and two latter refer to the risk of commercialization or
market risk.
As a result, two broad groups of risks for biotechnological startup company can be
identified: technological risks that affect the capability of the project to pass all the development
milestones and commercialization risks that are connected with the market success of the
product. These two groups of risks are not correlated with each other.
2.1.1 Technological uncertainties
Biotechnology is characterized by the long process of product development. It can take as
around a decade to get a new drug on the market in developed countries. In emerging economies
such as Russia, this process is not so time consuming but still requires a considerable time.
Anyway, there are typical milestones that can be implied to every biotechnological project.
30
The typical process of new biomedical development includes concept development,
analysis of optimal characteristics of products which include computer modeling and toxicity
check, pre-clinical studies where new drugs are tested on animals, synthesis of pilot pieces for
the clinical studies and the clinical studies that have four stages.
The first stage of clinical studies is the first trials on people, which are aimed to find the
optimal dose for the components of the drug. The given trials are aimed to indicate tolerance,
safety, and presence of the therapeutical effect on the wholesome body. On the second step,
clinical effect is tested on people with the certain disease again to prove the sufficiency of
product which is tested.
The third stage is the mass trial of people with the certain disease. It is usually the most
time consuming and the most expensive period of clinical study. Before the third stage, the entity
which product is being tested should produce the required amount of samples, usually around
one thousand. On the results of this stage, the government body that is in charge might confirm
or decline the registration of new drug.
The last stage is conducted after obtaining the license when the drug is already on the
market. These trials are aimed to determine the difference of the product comparing to other
drugs on the market in a particular niche and hidden risk factor that were not described during
the previous studies.
To sum up, there are four important technological milestones for the biotechnological
project (Table 2). During each step, there is a likelihood to fail and to continue. The probability
of failure can be obtained using the Delphi method the anonymous survey of the experts who are
working on the project and on the projects with similar characteristics.
The length of the steps of the development process is also matters. The data can be
obtained from the web-site of Association of Clinical Trials Organizations (ACTO) that annually
publishes the numbers for the duration of the clinical process. The average number of days to
obtain the license from 2005 to 2015 is 116 days (Clinical Trials in Russia, 2016).
Figure 3. The process of biotechnological R&D.
Concept
development
Analysis of
optimal
characteristics
Preclinical
studies
Cliniclas
studies (4
steps)
Source: Association of Clinical Trials Organizations
31
2.1.2 Commercialization uncertainties
The biotechnological industry consists of ventures that are using living organisms or
molecular or cellular techniques to provide medicines, food and services to meet human needs.
There are thousands of small firms in biotech industry, whose R&D activity shapes the overall
industry. Mergers and acquisitions happen very frequently in this market and are used as an exit
strategy for those smaller biotech firms who often have financial difficulties, such as few or no
marketable products and low cash-to-sales ratios.
Early-stage companies in the biotech industry face market uncertainties that should be
considered in valuation. Those uncertainties depend not only on the stage of development and
the experience of the company, but also the types of drugs being developed. There are several
type of main specific risks in biotechnological industry, they were summarized in Bratic et al,
2014 and will be discussed in following paragraph.
Table 2
Commercialization risks in biotechnological industry
The risks
Government regulation
Risk of litigation
Risk of estimating the patient
population
Risk of biosimilars
Risk of fake drugs
Risk of different legal
environment
Impact
After the whole technological process there is a risk for
biotechnological company in not obtaining the license.
The commercianalization of a drug can be blocked by the law.
Can lead to misevaluation of demand.
There is a risk that another research unit will find a better
solution to a problem that addressed by the venture’s drug.
The large amount of fake drugs has a downward effect on
commercianalization potential of a new drug.
If the company selling its products to another country more
likely that it will require additional examinations and
confirmations.
Source: Bratic et al 2014
32
Government regulation
A newly created chemical or biological entity in every developed or developing country
should overcome numerous regulatory hurdles. In Russian Federation as it was discussed above
the procedure of drug approval takes around 120 days and consists of 5 stages including the
expertise of all documents and clinical research and ethical expertise.
Moreover, a venture needs to receive a special license, which is given for 5 years only if
venture has approved production facilities and certified well-trained personnel. According to
open data of Federal Service for Surveillance in Healthcare of Russia only 3% of total amount of
licenses that were requested in 2015 were denied but at the same time, 18% of total requests to
extend the license were canceled. So there is always the risk not to obtain regulatory approval.
Risk of litigation
Litigation risk is another area for consideration when valuing early-stage biotech
companies. In spite of extensive risk management efforts of pharmaceutical companies, there has
been a rise in the number of settlements for violations of a variety of laws in the last two decades
abroad and in Russian Federation.
Risk of estimating the patient population
The actual number of patients that need to be treated, as compared to an extrapolated
estimated prevalence, is often uncertain. The mistakes in estimations can be related to the fact
that prevalence studies are usually done in regions of higher prevalence and usually based on
hospital data. The misevaluation of the demand, of course, has a significant effect on forecasted
revenues.
Risk of biosimilars
The risk of “Biosimilar” drugs is an important factor and should be considered. There is
always the uncertainty that another research unit will find a better solution to a problem that
addressed by the venture’s drug. Moreover, another company may just produce a cheaper
generic alternative. This sphere is well regulated in recent time (Brtatic et al. 2014) but there is
always back doors for generic producers.
Risk of fake drugs
Ernst & Young observed that as of 2008, counterfeit drugs accounted for approximately
10 percent of the world’s pharmaceutical product supply. This problem is very important for
emerging markets where there is a lack of normative regulation. Counterfeit biologics are
extremely challenging to detect, and they are extremely vulnerable to environmental
degradation, more so than other drugs.
33
Risk of different legal environment
The company from Russia that aims to sell its products for example in
German market
will be needed to obtain the commercial license and pass through the whole stages of clinical
trials that are needed in the another country. Hanse, there is an additional risk of not obtaining
the license and that the procedure of passing through clinical trials will temporize.
2.3 Quantitative analysis
2.3.1 DCF valuation
The first stage of the ROA process is the estimation of the Net Present Value of the
project. The estimating of a discount rate is the essential step of DCF analysis. There are some
widespread methods of obtaining the discount rate such as WACC or CAPM but they are not
suitable for technological startups. One of the few reliable approaches in case of technological
projects is the cumulative method (Managarov, 2011) where discount rate is calculated using the
following formula:
3 = 34 + ? + 32 (5)
Where, r is the discount rate, rf is the minimal discount factor or risk-free rate, I is the
inflation rate, and rp is the coefficient that considers the investment risk of the project. The riskfree rate is needed not only for DCF valuation but also for the real option valuation using the
binomial approach so it should be calculated. Russian government bond yield at the time of the
research is 9,34% (Moex.com, 2016) but due to the unstable economic situation, the country
default spread should be considered. According to Damodaran (2016), the Russia’s rating based
default spread currently is 2,77%, so the risk-free rate is equal to 6,57%.
The annual inflation rate in 2016 according to the forecasts of Ministry of Economic
Development of Russian Federation is estimated to be 12%. The risk premium for innovation
development is very high. The risk is increasing when the R&D process is conducted by several
organizations, there is an uncertainty about demand, prices and absorption of the technology.
2.3.2 Decision tree analysis
The decision tree method enables to estimate the value of the project with managerial
flexibility. The method is basically the graphical representation of the sequential decision
making process. Real options approach uses decision trees method as the starting point of the
analysis. However, decision tree valuation is discrete in time, and doesn’t capture full flexibility
of the project.
34
To estimate the Expected Net Present Value (ENPV) it is needed to determine the present
value of all possible end points and then multiply them by the respective cumulative
probabilities. Each stage of the R&D process has its probability to pass as well as there are
several outcomes of commercialization that have their own distribution. Normally, a constant
discount rate is applied to find value of the project using the decision tree method (Steffens,
2007). The estimation of Expected Net Present Value is conducted using the formula 6:
M
A%BC =
GN'
DEFG,I
∗ 2G +
(1 + 3)I
M
LN'
EEFL,I
∗ OL (6)
(1 + 3)I
Where, i is the index of stages, 2G is the conditional probability to pass the stage i, r is the
discount rate for development cash flows, t is the time of the project development, DEFG,I is the
expected development cash flow at time t given that i stage is the end stage, j is the index of
market state at the time of commercialization, EEFL,I is the expected commercialization cash
flow at time time t and the market conditions j, and OL is the probability that market will be in the
state j. The typical decision tree for biotechnological development is presented on the figure
Figure 4. Decision tree for biotechnological development.
Source: Kellog, Charnes, 2000
2.3.3 Real options analysis
As it was discussed in the previous chapter the binomial real options approach doesn’t
require sophisticated continuous-time stochastic calculations but allow scenario planning
techniques to be integrated to determine possible development paths for the value of the
underlying R&D projects. As scenario planning is one of the most common long-term planning
35
tools in corporate practice, the binomial approach has the potential to be implemented in the
practice of investors.
The model by Cox, Ross, and Rubenstein is used to form a binomial lattice. The first step
of the binomial model is to form evolution lattice of the underlying asset. It is assumed that in a
small time interval Δt the price may change in only two directions: u (u > 1) times if the price
will rise and d (d < 1) times if the price will go down. At the end of the first stage, the result is
the value of the underlying asset at any period of time. Then, the option is calculated using the
process of backward induction. Figure 4 shows the example of binominal tree with the
underlying asset S, which is transformed into the tree with option value C:
Figure 5. Binomial trees with evolution of underlying asset and corresponding option values.
S*u^3
C3,uuu
S*u^2
S*u
S
C2,uu
S*u^2*d
C1,u
S*u*d
S*d
C0,0
S*u*d^2
C1,d
S*d^2
t=0
t=1
t=2
C3,uud
C2,du
C3,udd
C2,dd
S*d^3
t=3
t=0
t=1
t=2
C3,ddd
t=3
Source: Cox, Ross and Rubinstein (1979)
The initial binomial lattice is building only with up (u) and down (d) factors. The
formulas are:
Q = *R
SI
; & = * ,R
SI
1
= (7)
Q
Where <t = length of binominal period and σ = volatility. As it was discussed in the
first chapter, the volatility of the technological project is typically based on its market
performance that is affected by the external factors. Following Kellog et al 2000 in this research
volatility is estimated using the formula 8.
:=
1
ℎ
ln (8)
]
_
Where l is the time period, h is the maximum value for the project cash flows, A is the
value of the underlying asset. The idea is that we want the value of the project to grow from A to
the maximum value of h after l time intervals. The natural log is used for volatility estimation to
make it follow the exponential Brownian Motion stochastic process because the binomial model
requires it as a fundamental assumption. The binomial period is the time interval in which the
36
price of an underlying asset is changing. For example, after the first-month value of the project
may either increase or shrink by 10% and so on.
After the lattice for underlying asset is formed we can calculate the option value using the
process of backward induction. Values are rolled back using the risk-neutral probability. That are
probabilities of future outcomes adjusted for risk, as the assumption of the binomial model is that
investors are neutral towards risk. The assumption is needed to price the asset based only on its
expected payoffs. The formula for risk-neutral probability is presented below:
2=
*
-a ,b ×SI
−&
Q−&
(9)
Where rf is the risk-free rate, D is the dividend yield, t = length of the binominal period, u
is the up state and d is the down state. The binomial model was initially developed for pricing of
financial options so the dividend yield was used to properly assess the price of the stock.
However, in real options analysis, the dividend yield is not used. Finally, the general formula to
find the value of each node of the lattice is:
Ee,G =
2×ELf',G + 1 − 2 ×ELf',G
* -a ×gI
(10)
Sequential Compound Option
According to the results of the first chapter, the most accurate way to estimate the
flexibility value of the biotechnological R&D development is to use the sequential compound
rainbow option model. The compound option exists when the project has multiple stages and the
latter phases depend on the success of previous ones. To find the value of the project it is needed
to find a value of several sequential European options. The first step of building the sequential
compound option is not different from any other ROA model. It includes calculation of up and
down factors and the underlying asset lattice. On the table below there is the lattice of underlying
asset S.
Figure 6. Lattice for the underlying asset.
S*u^3
S*u^2
S*u
S
S*u^2*d
S*u*d
S*d
S*u*d^2
S*d^2
t=0
t=1
t=2
S*d^3
t=3
37
The underlying asset is typically the present value of project cash flows. After building
the first lattice the analysis requires the calculation of the longer-term option first and then the
shorter-term options because the value of the option is based on the value of previous one. So
there are as many lattices as stages in the project. If for example the project has two stages, the
first stage duration is the two time intervals and the second stage start after the realization of the
first and lasts the one time interval the following lattices should be estimated:
Figure 7. Example of the lattice for the first stage.
C3,uuu
C2,uu
C1,u
C3,uud
C0,0
C2,du
C1,d
C3,udd
C2,dd
t=0
t=1
C3,ddd
t=3
t=2
Figure 8. Example of the lattice for the second stage.
C2,uu
C1,u
C0,0
C2,du
C1,d
C2,dd
t=0
t=1
t=2
The value on each node of the lattice for the first stage depends on the underlying asset
but values of the second lattice depend on the values from the previous stage. At the end of each
stage, the manager has a choice to continue the project or to defer the investments. So the value
of EG,L is the maximum between investing and keeping the option open.
Cj,k = max EG, Lf' − ?noI ∗ pI ; 2×E Gf',L
q
+ 1 − 2 ×E Gf',L
g
∗ * ,-.
SI
(11)
Where EG, Lf' is the value of the underlying asset taken from the previous lattice, ?noI
are investments that are required at time t and pI is the probability to continue to the next stage, p
is the risk-neutral probability, E Gf',L
q
is the value of underlying asset in the upper state in the
same lattice, rf is the risk free rate, and <t is length of binominal period.
38
Summary
The goal of this chapter is to present the methodology for real options analysis of
biotechnological startups. The ROA process normally includes three steps: Strategic analysis
Risk analysis, and Quantitative analysis.
The strategic analysis is needed to identify the areas of managerial flexibility, or the
strategic opportunities built-in in the project and which the investor could provide. In the case of
biotechnological development, this flexibility is that investor may choose to infuse capital in
stages. If the one stage was not successful investor may choose to defer or abandon the project.
The risk analysis is required to properly estimate the uncertainties of the particular
project. These uncertainties are then incorporated into the real options model. Each
biotechnological development has two main categories of uncertainties: Technological
uncertainties and Commercialization uncertainties. There are typically four milestones of
biotechnological R&D process: concept development, analysis of optimal characteristics of the
product, pre-clinical studies where new drugs are tested on animals, synthesis of pilot pieces for
the clinical studies and the clinical studies that have four stages. Every step has the probability to
pass that is included in the Decision tree analysis and in the ROA as the parameter.
The commercialization uncertainties are individual for every technological project.
Nevertheless, there are common market risks for biotechnological projects such as Government
regulation, Risk of litigation, Risk of estimating the patient population, Risk of biosimilars, Risk
of fake drugs, Risk of the different legal environment. The given risks, as well as those that
apply for each case specifically, influence the cash flow volatility of the project.
The DCF valuation is the first step of real options analysis. To estimate the cost of capital
for technological development the cumulative method is used. The decision tree approach is the
alternative method to find the value of the technological project. It enables to consider the option
to abandon but doesn’t treats properly the market risk and doesn’t take into consideration the
possibility to defer the investment. However, the method is used to build the strategic tree of the
project.
To find the real option value the sequential compound rainbow option model is used. The
word compound means that the value of one option depends on the previous option. For
example, in the case of biotechnological development, the value of the first stage depends on the
success of further stage. This model allows finding the value of an option with two sources of
uncertainty so it is called a rainbow.
39
CHAPTER 3. CASE STUDY
The purpose of this chapter is to implement the methodology of real options analysis to
the project PolySeed from the portfolio of North West Technology Transfer Center (RUSNANO
group) in order to solve the particular managerial problems such as valuation of the project and
reducing of risks. The results of given case study can be applicable to other biotechnological
projects.
3.1 Description of the project
PolySeed is the fundamentally new technology for manufacturing polymer microsources, based on the iodine adsorption effect into polymers. These polymer micro sources can
be used in the cancer treatment method called “brachytherapy” and they are better than micro
sources that are currently used. The benefits of “PolySeed” micro sources are:
– The simplicity of the production cycle - the developed technology eliminates the complex
manufacturing operations while maintaining the physical properties of the mineral;
– Lower cost - there is no need to use expensive materials (titanium housing, gold or silver
marker) for the production of micro sources;
– The increased specific activity of micro sources and the ability to make different shapes;
– Expanding the range of measurable LDR-brachytherapy diseases - using PolySeed will
lead to the expansion of the number of Diseases treatable by low-dose brachytherapy.
Besides prostate cancer, surgery may be the treatment of breast cancer, eye cancer,
esophagus, lung, trachea, and bronchus. In the long term option for targeted delivery of
drugs can be developed;
– Reduces injury rate of operations - because micro sources used will be manufactured
entirely from biodegradable materials, their presence in the patient's body will be limited
to a certain period, after which the Micro Source dissolved, while not causing any harm
to the body.
3.1.1 Market of brachytherapy
Brachytherapy - is a local form of radiotherapy in which the Micro Source of radiation is
introduced into the affected organ for some time (HDR brachytherapy) or placed on a permanent
basis in the patient's body (low-dose brachytherapy).
In the treatment of prostate cancer brachytherapy technique has become the most popular
and replaced the surgical method and EBRT method because of the similar efficacy, but lower
costs and injury rate. The cost of surgical intervention for brachytherapy remains high.
According to the Institute for Clinical and Economic Review (ICER), cost of brachytherapy in
40
the treatment of prostate cancer in the US in 2010 was USD 35.1 thousand, and the cost of
external beam radiation therapy - USD 59,5 thousand.
The world medical statistics testifies to the annual increase in the number of new cases of
cancer worldwide - around 13 million. Most of the malignant tumors are radiosensitive. Over the
past decade, the number of men annually detected with prostate cancer increased by an average
of 7.6%. The absolute number of newly diagnosed prostate cancer has increased since 2001,
more than 2 times: from 12.8 thousand to 28.6 thousand the case of the time. This type of disease
accounts for 12.4% of the total of detected cancers in men.
HDR brachytherapy method has become a primary therapy to be applied for the treatment
of localized malignant tumors in remote organs such as the prostate gland, the esophagus,
stomach, pancreas, kidney, bladder, uterus, trachea, bronchi, and lungs. In the total volume of
newly diagnosed cancers, there are more than 40% that can be cured by low-dose brachytherapy.
Today, the capacity of the Russian market of HDR brachytherapy is estimated at about 3
thousand micro sources sets per year, in Europe 12 thousand in the US -50 thousand. When
selling price of micro sources in USD 130, the entire market is estimated at USD 507 million. a
year, Russia USD 23 million. per year. The number of micro sources implantation into the
prostate gland is growing 2-3% annually.
Figure 9. Market volume for low dose brachytherapy in 2015.
Europe
USD 94 mln.
18%
Russia
USD 23 mln.
5%
USA
USD 390 mln.
77%
Source: Cancer Treatment Centers of America
In Russia the state allocates quotas for operations: in 2013, the number of quotas for lowdose brachytherapy was 737, an increase of 100 quotas than in 2012. Thus, the state provided
about 25% of the potential demand. According to the Association of Russian Brahitherapists,
there are 17 medical centers in our country that have brachytherapy services, with leading clinics
41
in the segment of low-dose brachytherapy is FGUZ KB №8 FMBA of Russia (Obninsk) and the
Clinical Hospital №122 them. LG Sokolova FMBA of Russia (St. Petersburg).
Figure 10. Allocation of low dose brachytherapy surgeries in Russia (2015).
Quotas
25%
737
Paid
surgeries
75 %
2 263
Source: Association of Russian Brahitherapists
3.2 Investment analysis of the project
3.2.1 Strategic analysis of the PolySeed project
In the case of the PolySeed project as well as in the case of almost each biotechnological
development the investors have the option to infuse capital in stages. To consider the given
option, the project development process was divided into stages according to a methodology that
was presented in the second chapter. The typical structure of the biotechnological development
includes four main stages: analysis of optimal characteristics of products which include
computer modeling and toxicity check, pre-clinical studies where new drugs are tested on
animals, synthesis of pilot pieces for the clinical studies and the clinical studies that have four
stages. In this particular case, the following structure is applied. The project aims to develop the
micro sources to carry the nuclear materials for cancer treatment, so the second stage includes
the research with nuclear materials.
The crucial part of the strategic analysis of the biotechnological project is to split the
costs between stages of the development as well as estimate the duration of every step. In this
case, the duration of clinical tests was taken from the ACTO database. According to the
database, the average duration of clinical tests in Russia is 130 days. The durations of other
stages was obtained from the interviews with the investment managers of NWTTC that have
experience of conducting the similar projects. The probabilities to succeed on every step was
derived from interviews with experts group who are working on this or similar projects The
results summarized in the table above.
Table 3
42
Stages of the PolySeed project
Stage
Duration (month)
Costs (In thousand
roubles)
Probabilities
Research of
technology’s optimal
8
-2850
0,7
6
-4000
0,7
2
-300
0,9
4
-1850
0,6
parameters
Research with
materials
Production of
necessary amount of
materials for clinical
trials
Clinical trials
Source: Interview with experts from NWTTC
The full range of real options that are typical for biotechnological development is
presented in the PolySeed project. For the NWTTC, the project clearly embraces the growth
option. There is a possibility to abandon the project at every stage. The equipment is supposed to
be rented through the life of the project so the project doesn’t have any salvage value. Managers
also have an option to defer the project in case of any technological stage will not be successful.
In this scenario rather than shutting down the project, it is more sensible to make adjustments in
technology and continue the development.
3.2.2 Risk analysis of the PolySeed project
The probability of passing every step was assessed by the survey of technical experts who
are in charge of the process realization. The another source of risk is the volatility of project’s
cash flow. Commercialization of the technology is planning to be achieved through the sale of
Russian and international patent rights on the production technology to create polymer
radioactive sources and for utility installation model to interested strategic investors. The leading
manufacturers of micro sources in Russia and worldwide are:
– Russian manufacturer of micro sources JSC "NanoBrahiTek" Dubna (shareholders: OJSC
«RUSNANO», Eckert & Ziegler BEBIG s.a, NADEX MAX LTD, OOO "Santis");
43
– The world's largest manufacturers: Eckert & Ziegler BEBIG, Varian Medical Systems,
Nucletron (Elekta), GE Healthcare.
One of the main risks of PolySeed project is the threat from similar technologies. In table
4, the competitive position of the project is summarized. There are two rival technologies on the
market are IsoSeed® I-125 and Oncoseed. Their main advantage is they already well established
on the market. However, PolySeed technology has an advantage in terms of price as it uses
cheap polymer materials instead of titanium.
Table 4
Competetive position of the PolySeed project
Name of
technology
Manufacturer
Stage
Material
Shape and Radiactivity
size
(mCi)
Price
Cylinder
Length:
IsoSeed®
I-125
Eckert &
Ziegler
BEBIG
On the
Ti, Ag,
4.5 mm ±
Au
0.2 mm;
market
0.203-1.687
On request
Diameter:
0.8 mm ±
0.04 mm
Cylinder
Oncura (GE
Oncoseed
Healthcare
unit)
On the
market
Ti, Ag,
Length:
Au
4.5 mm;
0.191-1.016
On request
Diameter:
0.8 mm
Sphere,
cylinder
PolySeed
Project’s
Start of the
Medical
group
development
polymer
and so
forth.
Typical
0.10-2.00
Lower than
price of
competitors
size: 0.50.8 mm
Source: North West Technology Transfer Center
44
The commercialization of the technology is planning to be achieved through the disposal
of patent rights in Russia and abroad. The capacity of the Russian market of low-dose
brachytherapy is estimated at about 3 thousand sets (1 set = 15 micro sources) per year in Europe
- 12 thousand in the US - 50 thousand. The commercialization of the technology to foreign
countries is impeded by the fact that technology should overpass the whole medical control.
Moreover, the international companies will be ready to use the technology if it will recommend
itself on the market. Nevertheless, the main goal of the project is the disposal of patent rights to
international companies.
The assumptions about the price of the technology are based according to realistic market
data that are:
– Developed polymer micro sources have no analogs on the market;
– Developed micro sources will have a number of competitive advantages, from the lower
cost of production, ending with the increased specific activity of micro sources;
– In 2009 JSC "NanoBrahiTek" acquired the Eckert & Ziegler BEBIG license to
manufacture successfully passed clinical trials titanium brachytherapy sources for the
USD 2 000 000;
– Low-dose brachytherapy market is growing at a pace of 3.2% in real terms (According to
estimations of Russia’s brachytherapy center).
The assumption about the price of the technology was made based on the interview with
managers of NWTTC and the analysis of the market. The most likely scenario is that PolySeed
will obtain 15 millions of rubles for the technologic patent, also there is a possibility to get 60
millions of rubles in the case of the favorable market conditions and 0 in the case of the failure in
the market.
Table 5
The revenue assumption
Revenue assumption
In thousands rubles
Probability Revenue from selling the
patent rights
0,6
15000
0,25
60000
0,15
0
Source: Interview with managers of NWTTC, market analysis
45
3.2.1 DCF valuation of the PolySeed project
As it was discussed in the second chapter the discount rate was found following the
cumulative method. The annual inflation rate was assumed as 13%, the risk-free rate is 6,5% and
the project risk premium is 30% that can be explained by the high risk of the project and internal
norms of the NWTTC. The discount factor that was used in the analysis is equal to 50%. The tax
rate is assumed to be 20% for the entire life of the project. The revenue of the project was taken
from the most likely scenario. The process of Net Present Value calculation is presented in the
table below. The detailed table with computations is in Appendix 1.
Table 6
DCF valuation of PolySeed project
Starting date of the project
Expected duration
In thousands rubles
Contractor's fee
Administrative cost
Patent registration
Salary budget and the social tax
Operating expenditures
Rent and following acquisition of
equipment
Capital expenditures
Income from selling the patent
rights
Income tax (1,7% month)
FCF
DCF
NPV
IRR
01.10.16
20 month
2016
-1 775,00 ₽
-60,00 ₽
2017
-5 475,00 ₽
-240,00 ₽
-68,00 ₽
-5 783,00 ₽
2018
-1 800,00 ₽
-60,00 ₽
-200,00 ₽
-23,00 ₽
-2 083,00 ₽
Total
-9 050,00 ₽
-360,00 ₽
-200,00 ₽
-108,00 ₽
-9 718,00 ₽
-17,00 ₽
-1 852,00 ₽
-23,00 ₽
-23,00 ₽
-90,00 ₽
-90,00 ₽
-1 388,00 ₽
-1 388,00 ₽
-1 501,00 ₽
-1 501,00 ₽
15 000,00 ₽
923,55 ₽
11 529,61 ₽
4 702,27 ₽
15 000,00 ₽
-93,75 ₽
-1 174,60 ₽
-1 843,13 ₽
-5 684,69 ₽
-1 228,75 ₽
-3 158,16 ₽
316,27 ₽
51%
Source: Made based on the previous analysis and data provided by NWTTC
The discounted cash flow analysis shows that present value of the project is slightly
above zero. The internal rate of return is just on the same level with what investors want to gain
from such types of projects. After such results, the project is at risk of not being approved by the
investment committee so the flexibility of the project should be taken into the account.
3.2.2 Decision tree analysis of the PolySeed project
The decision tree method was used to estimate the Expected Net Present Value of the
project. The main advantage of the method comparing to DCF is that it captures managerial
flexibility. It estimates the value of the option to abandon the project in case of technological
46
failure. The method implies that the revenues occur only in case of passing all the technological
stages
To calculate the ENPV of the PolySeed project the data obtained during the strategic
analysis was used. The project was divided into four stages. Probabilities to pass each stage were
derived from a survey of experts who had the experience of launching the similar projects. The
overall costs of the project were split between the stages. On the table below there are inputs and
results of the decision tree analysis. The decision tree itself is presented in Appendix 2.
Table 7
Decision tree analysis
Cumulative
probability of
negative result
Stage
Probabilities
of CCF
NPV
P*NPV
In thoussands roubles
Research of
technology's optimal
parametrs
Research with
nuclear materials.
Production of
necessary amount of
materials for clinical
trial.
Clinical Tests
Approval
0,3
-2 807,83 ₽
-842,35 ₽
0,21
-4 597,45 ₽
-965,47 ₽
0,0245
0,1862
Optimistic
Most likely
Pessimistic
-4 808,00 ₽
-117,80 ₽
-6 237,06 ₽ -1 161,34 ₽
0,25 20 429,60 ₽ 5 107,40 ₽
316,27 ₽
189,76 ₽
0,6
-935,56 ₽
0,15 -6 237,06 ₽
1 274,65 ₽
ENPV
Source: Made based on the previous analysis and data provided by NWTTC
The decision tree analysis gives the higher value of the project than DCF method because it
incorporates the option to abandon and make possible to estimate the distribution of project’s
cash flows based on different scenarios. However, it doesn’t capture the full flexibility that
investors have.
3.2.3 Real options analysis of the PolySeed project
For the purpose of the analysis, the present value of revenue was taken as the underlying
asset for the binomial tree. It was estimated as 6,553 millions of rubles. The volatility of the
project’s cash flow was estimated using the formula presented in the second chapter where h is
60 millions of rubles – the maximum amount of money that the project can obtain, l is the
47
duration of the project – 20 months, and A is 6,553 the present value of the project. The
volatility of the project was estimated as 27%. The binomial period was taken as 2 so we
assumed that the price of the technology will change every two months.
The sequential compound rainbow option captures the flexibility of managers to invest in
the next stage or keep the option open. So at each node, there is a choice between investing and
waiting. The decision rule is the following:
?4 EG, Lf' − ?noI ∗ pI > 2×E Gf',L
q
+ 1 − 2 ×E Gf',L
g
∗ * ,-
SI
→
5ℎ* tno*uvw3u uℎwQ]& 23w!**& xtvℎ vℎ* 23wy*!v
(12)
Where EG, Lf' is the value of the underlying asset taken from the previous lattice, ?noI
are investments that are required for the stage t and pI is the probability to continue to the next
stage, p is the risk neutral probability, E Gf',L
q
is the value of underlying asset in the upper state
in the same lattice, rf is the risk free rate, and <t is length of binominal period. The first part of
equation symbolizes the value of investments and the second part the value of keeping the option
open.
After building the lattice with the evolution of the underlying asset, the four sequential
phases are evaluated using the described above decision rule, starting with the latter stage. The
lattices are presented in the Appendices 3, 4, and 5. In Appendix 6, there is a strategic lattice
built based on the previous binomial trees. The prices of options for each stage are summarized
in the following table. The option price for the first option reflects the flexibility value of the
whole project. The extended value of the project according to ROA approach is the value of the
compound sequential option, i.e. the option price of the first stage plus the NPV.
Table 8
Option values for the sequential stages of the research (In thousands of rubles)
Stage
First
stage: Second
Research
of Research
stage: Third
with Production
technology's
nuclear
necessary
optimal
materials.
amount
parameters
stage: Fourth
materials
stage:
of Clinical Tests
of
for
clinical trial.
Option Value
132,5
1399,4
3514,2
2943,6
48
In the given model such options as growth option, the option to abandon the project and
option to defer were incorporated. The real options approach allows receiving the value of the
venture at every stage of R&D development. It is the practically useful information because
commonly before the initiation of the certain development stage managers need to bear the
certain costs for example for patenting or prototyping. The value of the option shows the
maximum sum that investors can spend in the beginning of the stage.
Moreover, managers may use the ROA approach in the decision-making process because
it uses the volatility of project cash flows that are dependent on the state of the market. So before
the initiating of the project, managers may use the binomial lattice to see in what of the market
state they should make investments. The strategic lattice is presented in Appendix 6.
3.3 Discussion
In the given research the methodology for investment analysis of the biotechnological
project was formulated and applied to the specific project from the North West Technology
Transfer Center. The real options model was adopted for the purpose of the analysis. The
proposed model suits the process of the biotechnological development because it enables to
calculate the value of every stage using information about the future sequential options. The
model is also consistent with the risk-free arbitrage method of valuing options. The proposed
model may approach not only projects in biotechnological sphere but other technological
projects that require several steps of development.
For the investment managers of the particular project, the value of the startup was
obtained using three methods, the results are summarized in table 9.
Table 9
The value of the project obtained using different approaches
Method used
DCF
Decision tree
ROA
316,27
1274
448,77
Value of the project
(in thousands of rubles)
Apart from the strictly practical result, this paper provides the comparison of valuation
methods. The methodology that applied in the paper clearly provides more sophisticated
approach to value technological project than simple DCF approach. The usage of the decision
tree method and real options approach enables to estimate the flexibility of the project.
Moreover, those methods take into account two factors of volatility: technological and market
that appear to be more precise in terms of risk management.
49
In this research, the real options model was implemented for valuation of the project in
biotechnological industry. There is a limited amount of works on the given topic in financial
literature, especially in Russia. The binomial method was used for valuation of biotechnological
development in Kellog 2000 and Zabolotskij 2008. However, the models of real options that
were used in this works are slightly different.
The model of the sequential compound rainbow option was used in Cassimon 2004,
Herath 2002, and Fujiwara 2015. Nevertheless, there is no the parallel comparison with other
methods of valuation and the specifics of the real option implementation is rather different. In
this way, the given paper provides a new glance on the problem of real options implementation
to the biotechnological industry.
The research illustrated that DCF analysis tends to give undervalued results because it
doesn’t deal with density distribution of project’s cash flows. Hence, application of real option
methods may be extremely useful to value the projects with negative NPV. The decision tree
approach allows to build a consistent framework of the project and should be used as the first
stage of real options analysis. However, it doesn’t capture the full spectrum of managerial
flexibility implied in the project. In a case of PolySeed decision tree doesn’t account for the
option to keep the project open.
Both the decision tree and real option methods imply several scenarios for cash flows
distribution that are more realistic assumption that DCF model has. However, the decision tree
method is rather simplified because it assumes the discrete distribution of cash flows. Like in the
case of PolySeed, there are only three scenarios: 60, 15 and 0 millions of rubles. In real options
analysis, the continuous distribution of cash flows is used so the result is the stochastic variable.
The latter approach appears to be more reasonable but tends to give lower results in terms of
valuation. The value of the project obtained with decision tree technique is more than twice
higher than those obtained using ROA.
The additional benefit of the sequential compound real options model with multiple
volatilities is that it estimates the value of every step of the separate project that gives the most
precise understanding of the development scheme for managers.
On the basis of the conducted research, it can be concluded that real option is the tool that
most accurately suits the valuation process of biotechnological startups as it allows to estimate
the full flexibility of the R&D process. The importance of the tool is obvious for the firms that
operate in a very uncertain environment like technological ventures. In this research, the
methodology for conducting ROA in the biotechnological industry is presented. The proposed
model can be used for projects that have several stages.
50
At that point, it is important to say about the limitations of the sequential compound
model for valuing real options. Despite that the model very well suits the process of
biotechnological and other multi-stage developments it is might be problematic to implement in
other fields because the nature of real options there is different. Secondly, the process of real
options analysis requires rather sophisticated calculations that managers who are limited in time
wouldn’t approach. Moreover, there are limitations that are aligned with implementation of the
binomial model. It was initially developed to work with historic volatilities rather than future
estimation of cash flows. Despite the implementation of volatility associated with future
projections of cash flows is scientifically sound it may give the small mistake in the results.
51
CONCLUSION
The goal of given research was to make recommendations concerning the improvement
of the investment analysis process of biotechnological projects applying the methodology of real
options. In order to accomplish this goal, the certain objectives were solved. First of all, the
overview of existing methods of valuation of technological startups and typical risks of such
projects were provided. Also, the real option concept and its applicability to the investment
analysis of technological and biotechnological ventures was reviewed. Then the methodology for
investment analysis of biotechnological ventures with consideration of real options was
formulated. Then in order to illustrate the advantages of the method it was applied to the
particular project. Finally, we propose some recommendations for improvement the process of
investment analysis of biotechnological startups.
In the first chapter, the benefits and limitations of the most common methods of valuation
of technological startups were reviewed. Additionally, the typical structure of the technological
development and standard risks that are associated with technological companies were discussed.
The second part of the first chapter is devoted to the literature overview of the real options
concept and its applicability in the technological and biotechnological sphere. The aim of this
part was to review the real options models that can be relevant to investment analysis of
biotechnological projects.
On the basis of the first chapter, it was determined that the most widespread approaches
do not value properly the multi-stage projects in which implementation of one phase depends on
the successful completion of the previous one, which is typical for the biotechnological sphere.
The risks can be classified as market uncertainties systematic or firm specific and technical
uncertainties. ROA process includes three main stages: Strategic analysis, risk analysis, and
quantitative analysis. The binomial (lattice) method and the sequential compound rainbow option
model are the most accurate to value the biotechnological development.
The second chapter was devoted to the formulation of the methodology for the
investment analysis of the biotechnological startups. The strategic analysis is needed to identify
the areas of managerial flexibility, or the strategic opportunities built-in in the project and which
the investor could provide. In the case of biotechnological development, the typical flexibility is
that investor may choose to infuse capital in stages. If the one stage was not successful investor
may choose to defer or abandon the project.
The specifics of biotechnological projects were considered in this chapter. There are
typically four milestones of biotechnological R&D process: concept development, analysis of
52
optimal characteristics of the product, pre-clinical studies where new drugs are tested on
animals, synthesis of pilot pieces for the clinical studies and the clinical studies that have four
stages. The commercialization uncertainties are specific for every technological project.
However, there are common market risks such as government regulation, risk of litigation, risk
of estimating the patient population, risk of the different legal environment and other. The given
risks, as well as those that apply for each case specifically, influence the cash flow volatility of
the project.
In the part devoted to the quantitative analysis, three methods of valuation were described
such as DCF method that is basically the first part of the ROA, Decision tree method, and real
options analysis. The combination of this methods enables to conduct the investment analysis of
the biotechnological project in an adequate manner.
Finally, in the third chapter the developed methodology was applied to the real project
from the portfolio of North West Technology Transfer Centre (RUSNANO group). The
PolySeed project develops the new technology for manufacturing polymer micro-sources that are
used in the cancer treatment method called brachytherapy. Strategic and risk analyses included
the identification of stages of the project, amount of capital that is needed for each stage and
analysis of the external environment of the project in order to identify the cash flow volatility.
The quantitative analysis contained the assessment of the project using three abovementioned
techniques.
In the result the following recommendations for managers and investors who involved
into the investment analysis of biotechnological startups are proposed:
1. The DCF analysis tends to give undervalued results because it doesn't deal either with the
flexibility that the project has nor with density distribution of project’s cash flows.
However, it rather simple to implement and is good to obtain the conservative value of
the project. Moreover, the DCF analysis is the first stage of implementation of the ROA.
2. The decision tree method allows to build a consistent framework of the project and to
capture the value of the option to abandon. However, it doesn’t capture the full spectrum
of managerial flexibility implied in the project and uses the simplified assumption of
discrete distribution of cash flows.
3. The real option allows estimating the full flexibility of the R&D process. The importance
of the tool is obvious for the firms that operate in a very uncertain environment like
technological ventures because it incorporates the multiple sources of volatility and
provides the appropriate treatment of risks. Application of real options method may be
53
extremely useful to value the projects with negative NPV because it estimates the value
of managerial flexibility.
4. To conduct the real options analysis of the biotechnological venture, the sequential
compound rainbow option model was implemented. The given model allows to properly
estimate the flexibility that biotechnological project has. Moreover, the model allows
finding the value of every stage of the development that represent the marginal
expenditures for managers. It also provides the strategic lattice of the project’s value that
is the useful tool in the decision-making.
There are two main contributions of this papers. First of all, the investment analysis of
the PolySeed project was conducted. The paper has a practical information for managers from
NWTTC (RUSNANO group). Secondly, the developed methodology might be implemented for
investment analysis of projects in the biotechnological sphere and for other multi-staged
projects. The implementation of the real options tool and its benefits was illustrated on the
example. The assumptions of the method appear to be the most realistic for the companies
operating in the uncertain environment among all methods that were described so it definitely
should be applied for investment analysis of the biotechnological projects.
54
REFERENCES
Adner, R., & Levinthal, D. A. (2004). What Is Not a Real Option: Considering
Boundaries for the Application of Real Options to Business Strategy. The Academy of
Management Review, 29(1), 74.
Amram, M., & Kulatilaka, N. (1999). Real options: Managing strategic investment in
an uncertain world. Boston, MA: Harvard Business School Press.
Benninga, S., & Tolkowsky, E. (2002). Real Options—An Introduction and an
Application to R&D Valuation. The Engineering Economist, 47(2), 151-168.
Boer, F.P. (2000) ‘Valuation of technology using “real options’’, Research
Technology Management, Vol. 43, No. 4, p.26.
Bratic, W., Blok, J. R., & Gostola, M. M. (2014). Considerations for Start-Up Biotech
Company Valuation. Journal of Commercial Biotechnology, 20(2).
Brigo, D., Dalessandro, A., Neugebauer, M., & Triki, F. (2007). A Stochastic
Processes Toolkit for Risk Management. SSRN Electronic Journal.
Cassimon, D., Backer, M. D., Engelen, P., Wouwe, M. V., & Yordanov, V. (2011).
Incorporating technical risk in compound real option models to value a pharmaceutical R&D
licensing opportunity. Research Policy, 40(9), 1200-1216.
Cassimon, D., Engelen, P., & Yordanov, V. (2011). Compound real option valuation
with phase-specific volatility: A multi-phase mobile payments case study. Technovation,
31(5-6), 240-255.
Cassimon, D., Engelen, P., Thomassen, L., & Wouwe, M. V. (2004). The valuation of
a NDA using a 6-fold compound option. Research Policy, 33(1), 41-51.
Clinical Trials in Russia. (2015.). Retrieved March 28, 2016, from http://www.actorussia.org/files/sroki_2015.pdf
Copeland, T.E., Weston, J.F. and Shastri, K. (2005) Financial Theory and Corporate
Policy, 4th edition, Boston: Pearson.
Country Default Spreads and Risk Premiums. (2016). Retrieved April 25, 2016, from
http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html
Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: A simplified
approach. Journal of Financial Economics, 7(3), 229-263.
55
Damodaran, A. (2006). Damodaran on valuation: Security analysis for investment
and corporate finance. New York: Wiley.
Danzon, P., Epstein, A., & Nicholson, S. (2004). Mergers and Acquisitions in the
Pharmaceutical and Biotech Industries. National Bereau of Economic Research.
Dimasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation:
New estimates of drug development costs. Journal of Health Economics, 22(2), 151-185.
Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton, NJ:
Princeton University Press.
Dzyuma U. (2012). Real options compared to traditional company valuation methods:
possibilities and constraints of their use. Financial Internet Quarterly «e-Finance», Vol. 8,
No. 2, pp. 51–68.
Ernst & Young. Strategic Business Risk: Pharmaceuticals (2008). Retrieved April 3,
2016, from Retrieved April 25, 2016, from
http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/ctryprem.html.
Federal Service for Surveillance in Healthcare, Retrieved April 3, 2016, from
Retrieved April 25, 2016, from http://www.roszdravnadzor.ru/opendata/7710537160stat103_licfarm
Fujiwara, T. (2014). Real options analysis on strategic partnerships of
biotechnological start-ups. Technology Analysis & Strategic Management, 26(6), 617-638.
Gupta, A., Pawar, K. S., & Smart, P. (2007). New product development in the
pharmaceutical and telecommunication industries: A comparative study. International
Journal of Production Economics, 106(1), 41-60.
Hartmann, M., & Hassan, A. (2006). Application of real options analysis for
pharmaceutical R&D project valuation—Empirical results from a survey. Research Policy,
35(3), 343-354.
Hauschild, B., & Reimsbach, D. (2014). Modeling sequential R&D investments: A
binomial compound option approach. Business Research, 8(1), 39-59.
Herath, H. S., & Park, C. S. (2002). Multi-stage capital investment opportunities as
compound real options. The Engineering Economist, 47(1), 1-27.
Hodder, J.E. and Riggs, H.E. (1985) ‘Pitfalls in evaluating risky projects’, Harvard
Business Review, January–February, pp.128–136.
56
Huchzermeier, A., & Loch, C. H. (1999). Project management under risk: Using the
real options approach to evaluate flexibility in R&D. Fontainebleau: INSEAD.
Hugonnier JN, Morellec E. (2007) Risk aversion and real options. Swiss Finance
Institute Research Paper.
Hull JC. (2006) Options, futures and other derivatives. 6th ed. Essex, UK: Prentice
Hall;
Kellogg, D., & Charnes, J. M. (2000). Real-Options Valuation for a Biotechnology
Company. Financial Analysts Journal, 56(3), 76-84.
Kitapbayev Y, Moriarty J, Mancarella P, Blochle M. (2013) A real option assessment
of flexible district energy systems. Proceedings of the 10th international conference on the
European Energy Market;
Kodukula, P., & Papudesu, C. (2006). Project valuation using real options: A
practitioner's guide. Ft. Lauderdale, FL: J. Ross Pub.
Limtitovskiy, M. A. (2004). Investment projects and real options in emerging
markets. Moscow: Delo.
Loukianova, A. E., & Vasiliev, S. I. (2015). Methods of assesment and risk
management: Application of the concept of real options for evaluation of investment projects
in real estate. Grebennikon.
Managarov, R. (2011). Review of methods for calculating the discount rate. Retrieved
May 12, 2016, from http://www.cfin.ru/finanalysis/math/discount_rate.shtml
Mathews, S., Datar, V., & Johnson, B. (2007). A Practical Method for Valuing Real
Options: The Boeing Approach. Journal of Applied Corporate Finance 19(2), 95-104.
Mcgrath, R. G. (1997). A Real Options Logic For Initiating Technology Positioning
Investments. Academy of Management Review, 22(4), 974-996.
Ministry of Economic Development of Russian Federation. (2016). Retrieved April
25, 2016, from http://economy.gov.ru/minec/activity/sections/macro/prognoz/
Morano, P., & Tajani, F. (2014). Urban Renewal and Real Option Analysis: A Case
Study. Computational Science and Its Applications, 148-160.
Moscow
Exchange
-
Indices.
(2016).
Retrieved
April
25,
2016,
from
http://moex.com/en/index/RGBITR
57
Möst, D., & Keles, D. (2010). A survey of stochastic modelling approaches for
liberalized electricity markets. European Journal of Operational Research, 207(2), 543-556.
Myers SC, Turnbull SM. (1977). Capital budgeting and the capital asset pricing
model: good news and bad news. Journal of Finance;32(2):321–33.
Myers, S. (1984). Finance theory and financial strategy, Interfaces. Journal of
Finance. Vol. 14, January-February, 126–137.
Myers, S.C., and C.D. Howe. (1997). A Life-Cycle Financial Model of
Pharmaceutical R&D. Program on the Pharmaceutical Industry, Massachusetts Institute of
Technology.
Nigro, G. L., Morreale, A., & Enea, G. (2014). Open innovation: A real option to
restore value to the biopharmaceutical R&D. International Journal of Production
Economics, 149, 183-193.
Oriani, R., & Sobrero, M. (2008). Uncertainty and the market valuation of R&D
within a real options logic. Strategic Management Journal, 29(4), 343-361.
Pichler, O. R. Bellmann (1958)., Dynamic Programming. 342 S. m. Abb. Princeton,
New Jersey, 1957. Princeton University Press, 38(9-10), 410-410.
Prediction of long-term socio - economic development of the Russian Federation for
the period till 2030 year. Retrieved April 20, 2016, from
http://economy.gov.ru/minec/activity/sections/macro/prognoz/doc20131108_5
Raiffa, H. (1968) Decision Analysis: Introductory Lectures on Choices Under
Uncertainty, Readings, MA: Addison-Wesley.
Risk Management Definition | Investopedia. (2005). Retrieved February 23, 2016,
from http://www.investopedia.com/terms/r/riskmanagement.asp
Rogers, M. J., Maranas, C. D., & Ding, M. (2004). Valuation and design of
pharmaceutical R&D licensing deals. AIChE Journal, 51(1), 198-209.
Russia
Biotechnology
Market.
(2014).
Retrieved
April
https://www.rusventure.ru/ru/programm/analytics/docs/20141020_Russia
04,
2016,
from
Biotechnology
Market_fin.pdf
Schachter, J., & Mancarella, P. (2016). A critical review of Real Options thinking for
valuing investment flexibility in Smart Grids and low carbon energy systems. Renewable and
Sustainable Energy Reviews, 56, 261-271.
58
Sereno, L. (2010). Real Options Valuation of Pharmaceutical Patents: A Case Study.
SSRN Electronic Journal.
Sick GA, Gamba A. (2010) Some important issues involving real options: an
overview. Multinational Finance Journal. 255-291
Smith, J. K., Smith, R. L., & Bliss, R. T. (2011). Entrepreneurial finance: Strategy,
valuation, and deal structure. Stanford, CA: Stanford Economics and Finance.
Steffens, P. R., & Douglas, E. J. (2007). Valuing technology investments: Use real
options
thinking
but
forget
real
options
valuation.
International
Journal
of
Technoentrepreneurship IJTE, 1(1), 58.
Technolo
Timmons, J.A. and Spinelli, S. (2004) New Venture Creation: Entrepreneurship for
the 21st Century, 6th edition, Boston, MA: McGraw-Hill/Irwin.
Triantis A. (2005) Realizing the potential of real options: does theory meet practice?
Journal of Applied Corporate Finance;17( 2):8–16.
Valuing Startup Ventures | Investopedia. (2011). Retrieved February 02, 2016, from
http://www.investopedia.com/articles/financial-theory/11/valuing-startup-ventures.asp
Wang, J., Wang, C., & Wu, C. (2015). A real options framework for R&D planning
in technology-based firms. Journal of Engineering and Technology Management, 35, 93114.
Westland, C. (2002) Valuing Technology, Singapore: John Wiley & Sons (Asia).
Zabolotskij, A. A. (2008). Estimation of Option for Biotech Companies.
Microeconomic Analysis: Methods and Results.
59
APPENDICES
Appendix 1: DCF valuation
In thousands rubles
01.10.16
01.11.16
01.12.16
01.01.17
01.02.17
01.03.17
01.04.17
01.05.17
01.06.17 01.07.17
Contractor's fee
Administrative cost
Patent registration
Salary budget and the social
tax
Operating expenditures
Rent and following
acquisition of equipment
Capital expenditures
Income from selling the
technology license
Income tax (1,7% month)
FCF
DCF
NPV
-592
-20
-592
-20
-592
-20
-456
-20
-456
-20
-456
-20
-456
-20
-456
-20
-456
-20
-456
-20
-6
-6
-6
-6
-6
-6
-6
-6
-6
-6
-617
-8
-617
-8
-617
-8
-482
-8
-482
-116
-482
-125
-482
0
-482
0
-482
0
-482
0
-8
-8
-8
0
1250
1250
0
0
0
0
-11
-614
-410
316
-11
-614
-410
-11
-614
-410
-8
-474
-316
-8
-474
-316
-8
-474
-316
-8
-474
-316
-8
-474
-316
-8
-474
-316
-8
-474
-316
60
In thousands rubles
Contractor's fee
Administrative cost
Patent registration
Salary budget and the social
tax
Operating expenditures
Rent and following
acquisition of equipment
Capital expenditures
Income from selling the patent
rights
Income tax (1,7% month)
FCF
DCF
NPV
01.08.17
-456
-20
01.09.17
-456
-20
01.10.17
-456
-20
01.11.17
-456
-20
01.12.17
-456
-20
01.01.18
-600
-20
01.02.18
-600
-20
01.03.18
-300
-10
-6
-6
-6
-6
-6
-8
-8
-4
01.04.18 01.05.18
-300
-10
-200
-4
-482
0
-482
0
-482
0
-482
0
-482
0
-628
-463
-628
-463
-314
-231
-514
-231
0
0
0
0
0
-463
-463
-231
-231
15000
-8
-474
-316
-8
-474
-316
-8
-474
-316
-8
-474
-211
-8
-474
-211
-19
-1072
-476
-19
-1072
-476
-9
-536
-238
-9
-736
-327
255
14745
6553
61
Appendix 2: Decision tree of the PolySeed project
62
Appendix 3: Evolution of the underlying asset of the PolySeed project
63
Appendix 4: The binomial lattices of the forth and the fifth stages
64
Appendix 5: The binomial lattices of the first and the second stages
65
Appendix 6: Strategic lattice for the PolySeed project
66
Отзывы:
Авторизуйтесь, чтобы оставить отзыв