1

Valuation of Corporate Innovation and the Pricing of Risk in the

BioPharmaceutical Industry

Richard E. Ottoo

Global Association of Risk Professionals (GARP)

111 Town Square Place, 14th Floor

Jersey City, New Jersey 07310

USA

Phone: +1 201.719.7247

Fax: +1 201.222.5022

Email: richard.ottoo@garp.com

Abstract

A plethora of theories and practices in finance have shown that value and risk are inextricably

linked. For instance, a key principle of investing requires that an investor determine if the

potential payoff on any underlying investment justifies the risk. However, a challenging

empirical question still remains as to whether the risk of technological innovation is fully

reflected in the quoted market values of the stocks of high-tech firms and if these stocks are fairly

valued. In this paper I apply a novel methodology that blends the contingency-claims model and

the discounted cash flow technique and present a step-by-step approach to value a new product

of a firm operating in a highly regulated, risky and research-intensive BioPharmaceutical

industry. I show how the risk of corporate innovation, which is not fully captured by the standard

valuation models, should be priced into the value of its growth opportunity. In the proposed

framework, the discounted cash flow approach permits top-down estimation of the size of the

industry-wide growth opportunity that competing firms must race to capture, while the

contingency-claims technique allows bottom-up incorporation of the firm’s successful R&D

investment, timing of introduction of the new product to market, and the pricing of the innovation

risk. Overall, I am able to estimate the value contribution per share of a new product for the firm.

JEL Classification: G02; G13; G32.

Keywords: Valuation, Risk, Innovation, Regulation, Investment Decision.

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Valuation of Corporate Innovation and the Pricing of Risk in the

BioPharmaceutical Industry

1. Introduction In November 2015, The Wall Street Journal reported two separate but related events coming from

the U.S. Securities Exchange Commission (SEC) that attracted the attention of the investment

industry. The first was a review-call issued by the SEC requiring that mutual fund analysts (buy-

side analysts) must now disclose the methodology they apply in determining the valuation of shares

of privately-held technology companies that they purchase for their portfolios. The news of this

guidance caught the industry by surprise given that the SEC in the past had mostly monitored sell-

side analysts who have been cited for possible inflation of the values of stocks that they recommend

to investors.1 The second development was a disclosure by the newspaper that the SEC had begun

an investigation into the business model of Valeant Pharmaceutical International Inc. Again, this

news shocked the market given that the stock of Valeant (NYSE: VRX) was one of the best

performers in the first three quarters of 2015, generating a return of 43.58% compared to a loss for

the S&P 500 index of 3.02%. Over the next few months as the process of the investigation

continued, the stock of Valeant plummeted, falling from its lifetime high of $346.32 per share on

August 5, 2015 to a 52-week low of $34.05 on March 31, 2016. On January 13, 2017 the stock

closed at $15.33.

The two actions of the SEC cited above and the resulting dramatic decline in the stock price of

Valeant have renewed investor concerns about corporate valuation and the possible existence of

price bubbles especially for hard-to-value stocks such as high-tech companies and firms with

complex asset structures. A large body of research has shown that the high value of a company

stock is often the result of analysts overestimating the growth potential of the firm or overextending

the period of its high-growth path (see, for example, Cusatis and Woolridge, 2008). Financial

analysts have been found to exhibit excessive overconfidence when uncertainty, which is defined

by technology intensity, is higher (Bessiere and Elkemalia, 2015). Hooke (2010) reports that sell-

1 In April 2003, twelve Wall Street investment banks and firms paid a total of $1.4 billion to federal regulators

to settle charges that they misled investors by issuing overly optimistic reports on technology companies,

which resulted in inflated stock prices and created stock price bubbles.

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side analysts rarely project negative earnings per share growth, even for cyclical companies, a

reflection of the “hockey stick phenomenon” (see Appendix).

What exactly explains the very high value of a company stock? Are the risks of innovation for

high-tech, research-intensive, and highly regulated companies fairly priced into their market

capitalization? Is there a disconnect between the value of a discovery-dependent stock and

traditional tools of security analysis?

In this paper, I address these questions by focusing on the BioPharmaceutical industry and

modeling the valuation of new product of a BioPharmaceutical firm. I resolve the highlighted

concerns in four important ways by applying a real growth options model. First, I apply a forecast

of industry-wide (rather than firm-specific) operating cash flows of growth opportunities as a direct

input of the model. Second, I model a firm’s competitive advantage as a joint-conditional

probability of success in making the required discovery and being first in introducing the innovative

product to market. Third, I price the risk of innovation as a combination of the expert validation

and the customer validation of the new technology. Finally, I bound the period of high growth in

the model and not allow for unlimited years of near monopoly by adopting the standard regulated

period of exclusivity for a patented product. By focusing on the BioPharmaceutical industry, for

illustration, I am able to price the risk of innovation and demonstrate what a new product of a

BioPharmaceutical firm is really worth. Specifically, the implementation of the model follows a

five-step approach:

Step 1: Forecasting the operating cash flows for the industry’s growth opportunities

Step 2: Estimation of the cost of capital for the industry’s growth opportunities

Step 3: Estimation of the industry’s capital investment for the innovative product

Step 4: Determining the present value of the industry’s growth operating cash flow

Step 5: Estimation of the price of risk of innovation for the firm

Step 6: Derivation of the firm’s value of innovation using the real growth option model

Despite the difficulty in pricing high-tech firms, the need for developing a good valuation

model is compelling. Investors and the capital markets have acknowledged that high-tech

companies that successfully build on their patented ideas are now a permanent feature of long-term

investing and the marketplace and have substantially transformed the global economy. Moreover,

for companies heavily dependent on trading technology products and related services, constant

innovation is a requirement for survival. History has shown that innovation is the engine of

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industrial and economic growth and development. Indeed, good valuation is the cornerstone of

good decision-making in lending, investing, restructuring, retirement, and risk management.

Since Tobin (1958) and Myers (1977), analysts and investors have long acknowledged the

dominance of growth opportunities in the existing value of a business enterprise. This is especially

true for companies at the early and expansive stages of their development, and especially for those

that are innovation-based. But even for well-established and mature firms, the need to replenish

depreciating assets in addition to compensating for the costs of financing the business requires that

they continue generating growth. These real growth options may be developed organically through

a firm’s entrepreneurial activities or acquired externally as a result of mergers, acquisitions and

restructuring transactions. The focus of this paper is on the value of a firm derived from growth

opportunities acquired internally through making successful competitive innovations.

Traditional valuation models such as the discounted cash flow (DCF) methods and the relative

valuation approaches are by themselves limited in scope in effectively measuring the value of

corporate innovation, which is essentially embedded option (see Myers (1977), Trigeorgis (1987),

Damodaran, 2001). The real growth options model presented in this paper combines the DCF

techniques and the contingency-claims model (CCM) and provides a better approach to valuing

growth opportunities in line with Copeland and Antikarov (2001) and McDonald (2006). The

methodology greatly minimizes the weaknesses often associated with the DCF methods that

include, among others, inability to capture managerial flexibilities. The proposed framework also

is able to avoid the complexities and rigidities in assumptions, such as the fixed maturity structure

of holdings, which have always been attributed to the CCM. More importantly, by simultaneously

applying both top-down and bottom-up procedures, I incorporate the critical elements of

competition, speed of innovation, market risk, and financing need, marking valuable improvements

to Copeland and Antikarov (2001) and McDonald (2006).2

To demonstrate tractability of the model’s application, this study takes a clinical approach in

analyzing one BioPharmaceutical company, Gilead Sciences Inc., with the aim of deriving the

value of the company’s potential new and innovative medicinal product. The product is a potential

breakthrough medical cure for osteoporosis, which I assume would soon acquire patent protection

2 The financial and other product data attributed to Gilead Sciences Inc. and its competitors are actual but

the illustration of Gilead Sciences Inc. and the name of the innovative product “Chogobelyn” is

hypothetical.

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after a period of Gilead Science’s’ successful investments in research and development (R&D).3

The new product would be branded as Chogobelyn and the company will soon receive the Unites

States Federal Drug Administration (FDA) regulatory approval to proceed with commercial

production. I further assume that Chogobelyn is the only innovative product the firm is introducing

and that all the current company sales are generated by existing pipeline products (assets-in-place).

By applying the model, which captures the key factors that drive valuation of the real growth

opportunities, I specifically estimate the current overall market value for the cure of Osteoporosis,

the size of the industry-wide value of investment opportunity (S), to be equal to $174.979 billion

and derive the market value of Gilead Sciences’ corporate innovation (Chogobelyn) to be equal to

$96.997 billion.

The rest of the paper is organized as follows. In the next Section, I introduce and discuss the

contingency-claims valuation technique. Section 3 describes data sources. Section 4 presents the

integrated model, outlines practical steps in the model implementation process and discusses the

results. Section 5 concludes the paper. A review of the traditional valuation models is presented in

the Appendix.

2. Contingency-Claims Models

While the growth factor in the traditional models mirrors the various combinations of cash flow

patterns that reflect the ebb and flow of strategic decisions, it is still framed in a static framework.

This obviously poses a major limitation in estimating the value of investment growth opportunities,

hence total enterprise value (V0), especially for high-tech firms. Their linear and static nature and

the inherent assumption of the now-or-never investment decision, therefore makes these stand-

alone models incapable of adequately handling the valuation of new innovation, especially the key

role that managerial flexibility, skill and competition play in driving growth under uncertainty. The

model proposed in this paper provides a resolution for these drawbacks.

With the emergence and the pervasiveness of high-tech enterprises and in industries marked by

high levels of risks, uncertainty, competition and innovation, there is evidence that models relying

solely on the discounted cash flow approaches or the use of relative multiples have limitations in

application. In a pioneering research evaluating intangible assets of a firm, Myers (1977) utilized

3 Osteoporosis is a degenerative bond-linked disease that primarily affects adults especially women over

the age of 60.

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the ground-breaking work of Black and Scholes (1973) and Merton (1973) and showed that

investments in innovation such as R&D could be priced as real call options expressed as:

max[S – X, 0] (1)

where S represents the market value of the underlying asset, measured by the present value of the

operating cash flows, and X is the required capital investments, a proxy for the strike price of the

call option.

The real option valuation model assumes that the expected value of the net operating cash flows V

evolves according to the following diffusion process:

)(

)(

tS

tdS = dt + dz (2)

where is the instantaneous expected return on the business venture, ² is the instantaneous

variance of its return, and dz is the Gauss-Wiener process. It is also assumed that S(t) is spanned by

the cash flows of traded securities whose instantaneous equilibrium rate of return equals .

Several scholarly and professional valuation articles and text books including Trigeorgies (1995),

Brealy and Myers (1996), Amram and Kulantilaka (1998), Copeland and Antikarov (2001),

Damodaran (2002), Koller, Goedhart and Wessels (2010) have demonstrated the application of the

option pricing technique in valuing a firm’s growth opportunities as real call options. Ottoo (1998)

further refined the basic framework of this model (Equation (1)) by incorporating the critical

elements of competition and the speed of innovation, clearly defining the role of both technical

factor and market factor risks. In the Ottoo model (which I discuss briefly below), technical factor

risk, which is firm specific, has two sides. The first one refers to the firm’s uncertainty about its

own ability to succeed in striking the much desired commercial innovation. The other is the fear

that the firm may not be the first, among its competitors, to make the breakthrough discovery. On

the other hand, market factor risk is an industry-wide risk, reflecting the uncertainty surrounding

the size of the potential market.

The effects of private factor risk are often difficult to track by traded securities even in developed

capital markets, which raises the question of fair estimation. However, private risks are quantifiable

in the model. It is assumed that the high-tech firm and other competitors all act rationally, each

initiating investment in own R&D at date t0, hoping to be the first in announcing the success of its

innovation, which is assumed to occur at t = t1, when the government recognizes the winner and

awards a patent to protect its product or business model. To simplify the model it is further assumed

that the high-tech firm perceives that the other competitors all have same private risk

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characteristics, acting uniformly as a cartel. Ottoo (1998) shows that if the high-tech firm is to be

successful, it would need to make the patentable innovation at t = before any other competitor.

The scope of the technical uncertainty a firm faces before it succeeds is defined by two important

sources. One is the fact that none of the competing firms has any knowledge of the exact date it

will succeed in making a discovery. The other is the uncertainty surrounding the possible date a

rival would succeed.4 It is assumed that each firm’s discovery time t follows an exponential

distribution. The probability of success for each firm, its hazard rate, is a function of its scale of

investment effort. Suppose p and q denote the rate of R&D investments for the individual high-tech

firm and the combined competitors, respectively. Then, λ(p) and λ(q) represent their respective

conditional probabilities of success.

Suppose t = T is the maximum time of innovation beyond which all growth opportunities are

assumed to vanish. Therefore, for a firm whose hazard rate is denoted as λ(p) to win the competitive

innovation race, it must fulfill the condition that it wins the race before any other competitor does

and before the targeted growth opportunity dissipates, or λ(p) < min[t(q), T]. Thus, the optimal

discovery date is estimated as:

2)()(

)(,

qp

pTqtptpt

(3)

Equation (3) is the resulting model of technical (private) risk, which essentially measures

competitive advantage. It states that the expected time a successful firm makes the discovery is

estimated by the ratio of its hazard rate to the squared sum of hazard rates of all the competing

firms.

Specifically, the success of innovation grants the firm the right, but not the obligation, to acquire

the resulting net operating cash flow S(t). The acquisition of S(t) is only achieved by exercising the

real call option and making an immediate required capital investment X at time . The exercise

decision is not automatic and is largely a function of the relative magnitudes of S and X. To the

innovating company, there are clear benefits to exercising the option only if the real option is deep

in-the-money (S > X). The value of the corporate innovation (G) following a successful exercise of

the investment option is therefore computed as a real call option:

4 See, for instance, Kamien and Schwartz (1972), Loury (1979), Lee and Wilde (1980), and Dasgupta and

Stiglitz (1980) on modeling optimal timing. The usual assumptions that the function λ is twice continuously

differentiable and that it satisfies the following boundary conditions: λ(0) = 0; λ(p) = 0 as p ; and

λ(p) < 0, hold.

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ZXeZS

qpr

pG r*

* (4)

where:

G = market value of the new venture;

S = present value of growth opportunities’ net operating cash flows on the date

of innovation, which is also the date of production launch;

X = the strike price, total capital investment requirement;

r* = the risk-free rate of interest;

2 = (2 + 2 – 2), the conditional variance of the underlying cash flows

and development costs, a measure of total market uncertainty, where is

the correlation between dz and d;

= cumulative standard normal distribution function;

=

2qp

p

, amount of time it takes for the innovation to

breakthrough, a measure of competitive advantage; and

Z =

2*

2

*r

Xe

Sln

r .

[Table 1 Here] [Figure 1 Here]

3. Data Sources

Data for this study is gathered from multiple sources on both the subject firm and constituents of

the BioPharmaceutical industry sample. I use Capital IQ quarterly and annual data from 1990 to

2014 as well as individual company annual reports for the period. These data points allow me to

derive important parameter values including revenues from individual BioPharmaceutical products,

dates of patent expiration, Tobin’s Q, capital structure, long-term cost of debt, operating cash flows,

capital investments, tax shield on debt, cash flow return on investments, and the weighted average

cost of capital. Together with stock prices and capital markets data from Bloomberg LP, I am able

to compute asset betas for both the entire firm and its component assets-in-place. Data on company

patents are extracted from the U.S. Patents and Trademark Office website (www.uspto.org) which

I relate to the information on company products from Capital IQ to compute patent productivity

and the overall probability of success in innovation. I construct and use a simple value-weighted

index composed of selected fifteen multinational firms in the industry. The companies are all listed

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in the U.S. and trade either on the New York Stock Exchange (9) or on NASDAQ (6) (see Table

1). While the dominant part of the data comes from public sources, I however acknowledge data

limitations in this area of research given that companies usually strive to preserve the secrecy of

internal information on valuable R&D findings and intellectual property.

[Table 2 Here]

4. The Model and the Valuation Analysis

In this paper, I take the view that a blended model of both the contingency-claims technique and

the discounted cash flow method is the most appropriate approach for valuing future growth

opportunities (see Copeland Antikarov (2001), and McDonald (2006)). I argue that valuation of

corporate innovation in the contingency-claims framework is best represented by Equation (4)

whose main contribution to the seminal Black and Scholes (1973) and Merton (1973) model, and

hence the modified asset disclaimer (MAD) approach of Copeland and Antikarov (2001), is the

incorporation of competition and optimal timing. This paper therefore addresses the overall

objective of optimizing the strengths and overcoming the drawbacks of both discounted cash flow

and contingency-claims models. As mentioned earlier, I illustrate the application of the integrated

growth model with Gilead Sciences Inc.’s valuation of Chogobelyn, its innovative new product, a

potential breakthrough cure for osteoporosis. The valuation process combines a top-down

discounted cash flow technique, presented in Steps 1 to 4, and a bottom-up contingency-claims

model, discussed in Steps 5 and 6 below.

Step 1: Forecast Net Operating Cash Flows (CF) for the Entire Industry

To determine S, the present value of the net operating cash flow of the innovation product, I set to

determine the market potential for this product and begin with a top-down forecast of sales by first

asking the following question: what would the total first year industry-wide sales be if a cure for

this disease were to found today? While companies compete for control of the market share and the

right to own that control is acquired and provided by securing a patent award and protection, at this

stage it is critical to determine the level of overall sales to the industry that every firm is competing

for and not just the revenue accruing to a single company. I limit the study’s consideration to the

U.S. BioPharmaceutical market for ease of exposition and assume that analysts’ consensus estimate

for total industry sales if the product were to come to market this year (year 0) is $8.950 billion.

The next challenge is to determine the forward looking annual growth rates for the industry sales

over the forecast period. Using historical average growth patterns for patented products for each of

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the firms and the entire BioPharmaceutical industry (data not shown), I generate year-by-year

growth rates in sales throughout the patent protected period and ten years following patent

expiration.

There is a pattern of growth in BioPharmaceutical product sales over time as examined year-by-

year during the legal patent protected period of 20 years. On average, industry revenue always

grows initially at a high rate of approximately 20% following the product launch (patent grant).

The growth rate then reaches a peak in the 10th year at about 40% before beginning a notable period

of decline. In year 20, the last year of patent protection, the growth rate is down to about -4.0%.

And in the first year of the off-cliff period (year 21), immediately following the patent expiration,

industry sales fall significantly to an annual rate of -19.0 percent. During the next ten years of patent

expiration the average annual growth rate for the industry is approximately -14%. The overall

revenue cycle and trend is similar at the firm level as can be seen in Figure 1 although individual

variations may exist. Considering the three largest firms in the sample, I determine that Pfizer Inc.’s

growth in sales peak at about 25% in year 9. It eventually falls to -15% and -32% in year 20 and

year 21, respectively. For Bristol-Myers Squibb, the growth peaks in year 12 at 58%, and drops to

4% and -7% in year 20 and year 21, respectively. Eli Lilly’s growth peaks in the 7th year at 36%,

and then falls to 12% and 61% in year 20 and year 21, respectively. The growth rates for the market

opportunities that I eventually use in forecasting industry sales are presented in Table 3.

Using Capital IQ data for the sample firms (from 2000 to 2011), I estimate the industry’s effective

gross operating margin and marginal tax rate at 28 percent and 26.25 percent, respectively. Industry

net operating cash flows for Chogobelyn are then projected accordingly for the explicit forecast

period. Results are presented in Table 4 and depicted in Figure 1.

[Table 3 Here] [Table 4 Here] [Figure 2 Here]

Step 2: Estimate the Cost of Capital for the Industry’s Growth Opportunities

The derivation of the net operating cash flows to be used in a discounted cash flow methodology

obviously presumes a requirement for a discount rate for those future cash flows. I determine a

long-term cost of debt and a cost of equity in estimating the weighted average cost of capital

(WACC) for the industry. It is important to note that the WACC for the industry’s growth

opportunities is the relevant parameter to be determined and not the WACC for the industry’s

existing assets-in-place.

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The capital asset pricing model (CAPM) is the better approach to use in estimating the cost of

equity capital, implying that the discount rate for the operating cash flows will depend on the

systematic risk for the investment opportunities. Notwithstanding notable shortfalls in beta

construction such as the need for long historical data, existence of potential jump events, influence

of liquidity constraints, and beta decay phenomenon, the CAPM is still widely used in finance for

a number of reasons. First, CAPM is grounded on a solid theoretical principle. Second, it affirms

an intuitive relationship between securities and benchmark returns. And, third, it is empirically

supported. In computing the cost of equity relevant to the industry’s growth opportunities, I

therefore need to first obtain the beta for the growth opportunities, an important factor in deriving

the cost of equity.

Given that a BioPharmaceutical firm’s enterprise value consists of the value of its pipeline products

(assets-in-place) and the value of growth opportunities (expected new innovations), it is appropriate

to assume that total systematic risk of firm value is a weighted average of the systematic risks of

the asset-in-place and growth opportunities. Put differently, the beta of a firm or industry is a

weighted average of the beta of assets-in-place and the beta of growth opportunities. I first measure

the systematic risk of the assets-in-place by taking the covariance of the BioPharmaceutical

industry returns on asset (changes in asset value) with the return on equity of the market index

(S&P 500 index) and dividing it by the variance of the return on equity of the market index. The

returns data (not shown) are then applied to Equation (5) below. I derive the value of beta of assets-

in-place (a) to be equal to 0.0955:

m

ma

aRVar

RRCov , (5)

where:

a = beta of the assets-in-place;

Ra = return on assets-in-place; and

Rm = return on equity of the S&P 500 (market) index.

The total systematic risk of equity, comprising both the component of growth opportunities and the

assets-in-place, is proxied by the beta of traded equity (i), which is measured by the covariance of

the returns of stock prices (Ri) with market index price returns (Rm) as a ratio of the variance of the

market index price returns. Equation (61) utilizes the returns data and yields total asset beta i =

0.4002.

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m

mi

iRVar

RRCov , (6)

Since the asset of the firm or industry is believed to consist of two components, the assets-in-place

and growth opportunities, then the total asset beta (i) can be expressed as a weighted average of

the beta of the asset-in-place (a) and the beta of growth options (g). I use Tobin’s q measure and

compute industry value-weights for the assets-in-place and for the growth opportunities as 56.02

percent (wa) and 43.9 percent (wg), respectively. Furthermore, utilizing Equation (7) below and the

values for i, g, wa, and wg, I then obtain the beta of the growth opportunities (g) to be equal to

0.7884:

g

aai

gw

w (7)

Finally, I apply the capital asset pricing model and derive the industry’s cost of equity for the

growth opportunities to be equal to 8.34 percent. Using Capital IQ data, as of December 31, 2012,

our analysis determines that the average long-term cost of debt (RD) for the BioPharmaceutical

industry is 4.46 percent and the weights of debt (wD) and equity (I) are 12.77 percent and 87.23

percent, respectively. Given these derived model parameter values and risk-free rate r* = 4.00%,

tax rate = 26%, and equity market risk premium of 5.50 percent (see Table 5), the resulting value

of the weighted average cost of capital (WACC) for the industry’s real growth opportunities

(Equation (8)) is found to be equal to 7.69 percent. The WACC for the industry’s assets-in-place

by comparison is 5.83 percent.

EgmDD wrRrwtRWACC **1 (8)

The cost of capital analysis for Gilead Sciences Inc. is similarly conducted. The results presented

in Table 6 show relevant values for the firm’s weight of equity, weight of debt, levered beta, cost

of debt, cost of equity, and WACC. The main distinction with the industry cost of capital analysis

is that I do not need to compute the beta of growth opportunities specific to Gilead Sciences Inc.

The operating cash flows and systematic risk for Gilead’s growth opportunities are measured at the

industry level (Steps 1, 2, 3 and 4). However, its idiosyncratic exposure, which captures the firm’s

private or technical risk, is measured by the competitive advantage factor in Step 5 below, at the

firm-level.

[Table 5 Here]

Step 3: Estimate the Industry’s Total Capital Investments for the Innovative Product

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Based on the annual BioPharmaceutical industry surveys conducted by Deloitte and

Reuters/Thompson (Table 6) , and given Gilead Sciences’ historical investment rate, I apply $1.3

billion as the present value of the expected capital investments (X) required to effect production of

this new product. The $1.3 billion represents the value of the strike price of the real growth options.

[Table 6 Here]

Step 4: Determine the Present Value of the Industry Net Operating Cash Flows (S)

Table 5 presents the present value of the operating cash flows, amounting to $174.797 billion,

which is the discounted value of the cash flow forecasts derived in Step 1 for the entire industry

and the weighted average cost of capital obtained in Step 2 serves as the discount rate. It should be

noted that $174.797 billion represents the estimated value of total investment opportunity that each

firm in the industry will be competing to capture.

[Table 7 Here]

Step 5: Estimate the Firm’s Competitive Advantage ()

A company’s competitive advantage (), given by Equation (3), is defined by the quality and stock

of human capital the firm owns, which drives its entrepreneurial activities. In this model, a company

that succeeds in the competitive R&D investment race and becomes the first to make a discovery

for a cure of osteoporosis is considered highly skillful and is said to have a competitive advantage

over its peers. By implication, the firm is also said to have a lower technical factor risk relative to

its competitors. High competitive advantage (i.e., shorter time it takes to make a discovery and

introduce a product to market) is directly driven by the firm’s high probability of success, which

has two inextricably linked parts. The first one is that the firm must win the race to bring the

innovative technology to market before any other competitor does. I characterize this skill set as

the expert validation of technology (EVT). The second part is the requirement that the innovative

product be valuable to the marketplace. I refer to this skill set as the customer validation of

technology (CVT). In essence, the two conditions must be met before any FDA approval. I therefore

measure a firm’s overall sustainable probability of success (λ) by the product of the two conditions

as follows:

EVTCVT

PPRARA (9)

where:

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A = alpha = (IRR – WACC);

IRR = internal rate of return;

WACC = weighted average cost of capital;

R = phase-3-and-submission success rate;

= new patent productivity (1, if patent is awarded (firm wins) and 0,

otherwise);

PP = productivity of existing (stock of) patents.

The proxy for competitive advantage in this paper is motivated by the work of Kritzman (1986)

and Grinold (1989), among others, in evaluating portfolio manager skills. The first term, the

customer validation of technology, CVT = {A + R – (A)(R)}, represents the competitive information

coefficient, which defines the joint probability that the firm is capable of generating return in excess

of its cost of capital and its average success rate during both phase-3 clinical trial and submission.

The second factor, the expert validation of technology, EVT = (ω + PP), measures the breadth of

innovation and is calculated by the square root of the sum of newly acquired patent productivity

() and productivity of existing patents (PP) as expressed in Equation 9. PP is the ratio of a firm’s

total number of patents to its total number of products. We justify the scaling of patent count on

the ground that a firm’s number of patents by itself does not empirically guarantee value creation.

I allow a possibility of the rest of the competitors in the industry to collude in order to challenge

Gilead Sciences, Inc. The data and the specification of Equation (8) produce the following values

for the probability of success: λ(p) = 61.69 percent (for the winner, Gilead Sciences), and λ(q) =

44.67 percent (combined for the losing competitors). Using Equation 3 and the parameter values

thus obtained for λ(p) and λ(q), the value of Gilead Sciences’ competitive advantage factor () is

found to be equal to 0.5454 (see Table 8) as follows:

5454.01

1

2

qqqqqppppp

ppppp

PPRARAPPRARA

PPRARA

[Table 8 Here]

Step 6: Derive the Firm’s Value of Innovation (G) using the Real Growth Option Model

The final step is to compute the value of corporate innovation for Gilead Sciences Inc’s

Chogobelyn. I input all the parameter values derived from all the steps above and apply the

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valuation model specified in Equation (4). Table 9 presents the final valuation results whose key

inputs include:

The size of the market for the cure of Osteoporosis, the total investment opportunity

(S) for the industry, which is estimated at $174.797 billion (from Step 3);

Risk () of the underlying growth operating cash flows for the industry is 19.18

percent, measured by the standard deviation;

Capital investment (X) required to exploit the investment opportunity, which represents

the strike price of the real growth option, amounts to $1.3 billion, using an estimate by

Deloitte and Reuters/Thompson5 (from Step 5);

The risk-free rate of interest (r) is 4.00 percent (assumed);

Competitive advantage factor () or skill for Gilead Sciences Inc. is computed as

0.5454 (from Step 4) and it is a function of:

­ The conditional probability of success (λ(p)) of Gilead Sciences, Inc., which I

derive to be equal to 61.69 percent; and

­ The joint conditional probability of success (λ(q)) of the industry competitors,

determined to be 44.67 percent;

The overall current valuation (G) of Gilead Sciences’ corporate innovation

(Chogobelyn) is finally determined to be equal to $96.997 billion (see Equation (4)),

making a contribution of $63.85 per share, based on the current outstanding common

shares totaling 1.5192 billion.

[Table 9 Here]

5. Conclusion

In this paper, I present a step-by-step application of an integrated approach in valuing future growth

opportunities of an enterprise. I show how, in practice, an analyst can determine the inputs and

implement the model. In principle, the methodology melds the traditional discounted cash flow

method with the real options valuation technique. The approach in this paper, which focuses on the

BioPharmaceutical industry, explicitly captures competition, speed of innovation, risk, financing

need, and the size of the market potential in valuing corporate innovation. I clearly demonstrate the

identification and measurement of relevant model inputs, using Gilead Sciences Inc. as the subject

firm, and caution that the measurement of some of these parameters may differ if applied to other

5 See Deloitte LLP and Reuters Thompson Research (2010, 2011, and 2012).

16

high-tech industries. The blended methodology for valuing technology-intensive enterprises is far

superior to the stand-alone DCF approach, which is vulnerable to the excessive inflation of terminal

values, and to the CCM, which is not capable of accurately estimating the industry-wide value of

growth operating cash flows.

17

Table 1. Attractive Innovation Scenarios

Technological

Progress

Product Market

Demand

Competition

Capital Requirement

Success High Low Low

Success High Moderate Low

Success Moderate Low Moderate

Success Moderate Moderate Moderate

Combination of high demand, low competition, and low capital requirement point to a high

margin, which is a measure of “good business.”

Combination of technological success, low competition, and low capital requirement point to a

high capacity for a sustainable business model, which is a measure of/constitutes “good business.”

18

Figure 1. R&D Productivity and Stages of Clinical Trials

Source: U.S. National Institute of Health (www.clinicaltrials.gov) and author illustration.

t = 0 t =

FDA approves

or rejects

application to

market product

Firm initiates

research and

development

(R&D)

investment to

find a cure for

the disease.

Firm believes R&D

investment is

successful but not

proven; receives

FDA approval to

conduct exploratory

studies involving

very limited human

exposure to the

medicine, with no

therapeutic or

diagnostic goals.

Firm conducts

studies with healthy

volunteers and that

emphasize safety.

The goal is to find

out what the

medicine’s most

frequent and serious

adverse

events/effects are

and how it is

metabolized and

excreted.

Firm conducts studies to

gather preliminary data on

whether the medicine is

effective (i.e., works in

people who have the

disease or condition). For

example, participants

receiving the medicine

may be compared with

similar participants

receiving a different

medicine. Safety

continues to be evaluated,

and short-term adverse

events/effects are studied.

Firm carries out

further studies that

gather more

information on safety

and effectiveness by

studying different

populations and

different dosages and

by using the medicine

in combination with

other medicines. If the

studies are successful,

FDA approves

marketing of the

medicine

Firm conducts studies

after FDA has

approved the medicine

for marketing. These

include post-market

requirements and

commitment studies

that are required of or

agreed to by the

firm/sponsor. These

studies gather

additional information

about the medicine’s

safety, efficacy, or

optimal use.

Phase 3 Phase 0 Phase 1 Phase 2 Phase 4

19

Table 2. Growth Opportunities as a Percentage of Total Enterprise Value for Components

of the BioPharmaceutical Index (Measured by Tobin’s q) as at December 31, 2014

Company Ticker

Symbol

Stock

Exchange

Tobin’s q

(%)

Number of

Products

Number of

Patents

1. Abbott Laboratories ABT NYSE 57 368 1,084

2. Actavis Inc. ACT NYSE 25 197 55

3. Amgen Inc. AMGN NASDAQ 60 89 766

4. Biogen Idec Inc. BIIB NASDAQ 55 62 313

5. Bristol-Myers Squibb BMY NYSE 48 82 902

6. Eli Lilly and Company LLY NYSE 55 105 252

7. Forest Laboratories Inc. FRX NYSE 67 45 75

8. Gilead Sciences Inc. GILD NASDAQ 71 72 256

9. Johnson & Johnson JNJ NYSE 63 299 878

10. Life Technologies Corp LIFE NASDAQ 18 151 322

11. Merck & Company Inc. MRK NYSE 44 326 1,395

12. Pfizer Inc. PFE NYSE 33 222 1,675

13. Regeneron Pharmaceuticals REGN NASDAQ 53 26 115

14. Teva Pharmaceuticals Ind. TEVA NYSE 45 353 148

15. Vertex Pharmaceuticals VRTX NASDAQ 67 14 377

20

Table 3. Growth Rate in Sales for Selected BioPharmaceutical Firms during the Patent-

Protected Period

The table presents the average annual growth rate in sales of 71 BioPharmaceutical products during a 20-

year patent protected period for a sample of eight peer competing firms in the industry: Pfizer Inc. (26

products), Bristol-Myers Squibb Company (7 products), Eli Lilly and Company (5 products), Amgen Inc. (6

products), Gilead Sciences Inc. (7 products), Biogen Idec Inc. (3 products), Merck & Company (8 products),

and Johnson & Johnson (9 products). Sales figures come from companies’ annual reports from 1991 to 2014.

Company

Year of

Peak

Growth

in Sales

Mean Annual Patent-Protected Growth Rate in Biopharma Sales (%)

Full Period

(Arithmetic)

Full Period

(Geometric)

Pre-to-Peak

Growth

Period

(Geometric)

Post-Peak

Growth

Period

(Geometric)

Point

Difference

Amgen, Inc. 11 10.26 9.33 12.49 5.48 7.10

Biogen Idec, Inc. 10 31.35 22.78 25.45 20.17 5.28

Bristol-Myers Squibb 11 18.92 16.44 20.35 11.83 8.52

Eli Lilly Corporation 12 21.34 17.84 25.70 6.96 18.74

Gilead Sciences, Inc. 7 16.40 13.86 20.78 10.31 10.48

Johnson & Jonson 16 2.07 1.92 2.21 0.78 1.43

Merck & Company 9 8.13 7.04 9.28 5.24 4.04

Pfizer Inc. 12 14.67 13.19 16.20 8.82 7.38

Industry 11 15.39 14.29 17.79 10.15 7.64

21

Table 4. Annual Growth Rate in Industry Sales

Table 4 presents the annual growth rate in BioPharmaceutical industry product sales during a 20-year patent

protected period. Annual revenues from a sample of 50 pipeline products from six competing firms is

analyzed and assumed to proxy industry sales: Pfizer Inc. (26 products), Bristol-Myers Squibb Company (7

products), Eli Lilly and Company (5 products), Amgen Inc. (4 products), Gilead Sciences, Inc. (7 products),

and Biogen Idec, Inc. (2 products). Sales figures come from the companies’ income statements from 1991 to

2014.

Period Year Growth Rate (%)

1 0.00

2 0.74

3 0.62

4 3.30

5 8.57

6 17.30

7 33.89

8 28.66

9 29.35

Patent-protected 10 35.29

11 50.69

12 44.82

13 24.21

14 12.80

15 8.03

16 6.54

17 8.38

18 5.83

19 -3.58

20 -7.59

Average patent-protected growth rate (geometric) (20 years) 14.29

21 -28.58

22 -18.86

23 -8.81

24 -9.95

Off-cliff (patent-expiration) 25 -14.82

26 -13.12

27 -14.45

28 -9.73

29 -13.08

30 -6.52

Average off-cliff growth rate (geometric) (10 years) -14.01

22

Figure 2. Average Annual Growth Rate in BioPharmaceutical Product Sales

The graphs show mean growth rates in annual sales for the U.S. BioPharmaceutical Industry and for a sample

of six competing firms in the industry: Pfizer Inc. (26 products), Bristol-Myers Squibb Company (7 products),

Eli Lilly and Company (5 products), Amgen Inc. (4 products), Gilead Sciences, Inc. (7 products), and Biogen

Idec, Inc. (2 products). For each firm, only products whose annual sales records are available for the period

of the study are included. The annual sales are recorded for the year for each product since the product was

launched. BioPharmaceutical brands/products are considered to have tInty years of patent protection. Sales

figures come from the companies’ income statements for the years 1991 to 2014.

-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Biopharmaceutical Industry

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

23

-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

2 4 6 8 10 12 14 16 18 20

Pfizer Inc.

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2 4 6 8 10 12 14 16 18 20

Bristol-Myers Squibb

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

24

-80.00

-60.00

-40.00

-20.00

0.00

20.00

40.00

2 4 6 8 10 12 14 16 18 20

Eli Lilly and Company

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

2 4 6 8 10 12 14 16 18 20

Amgen Inc.

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

25

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

2 4 6 8 10 12 14 16 18 20

Gilead Sciences Inc.

An

nu

al G

row

th R

ate

in

Sa

les (

%)

Year Since Patent Grant

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

2 4 6 8 10 12 14 16 18 20

Biogen Idec Inc.

Year Since Patent Grant

An

nu

al G

row

th R

ate

in

Sa

les (

%)

26

Table 5. Risk, Return and Capital Structure Analysis as at Year-end December 2014

Measure Symbol Gilead Sciences Industry Industry

ex-Gilead

Weight of assets-in-place wa 32.85% 56.02% 57.67%

Weight of growth opportunities wg 67.15% 43.98% 42.33%

Market value ratio of equity-to-capital WE 92.15% 87.23% 86.98%

Market value ratio of debt-to-capital WD 7.85% 12.77% 13.02%

Beta (enterprise) βi 0.8496 0.4002 --

Beta (assets-in-place) βa -- 0.0955 --

Beta (growth opportunities) βg -- 0.7883 --

Cost of debt (enterprise) Rd 2.99% 4.46% --

Cost of equity (enterprise) Re 8.67% 6.20% --

Cost of equity (growth opportunities) Rg -- 8.34% --

WACC (enterprise) WACCa 8.17% 5.83% 5.83%

WACC (growth opportunities) WACCg -- 7.69% --

Internal rate of return IRR 21.03% 21.74% 21.75%

Effective tax rate t 26.43% 26.23% --

27

Table 6. Average Cost to Develop a Compound from Discovery to Product Launch

This table presents the average annual cost of bringing an asset to market for a sample of twelve global

BioPharmaceutical companies surveyed by Deloitte and Thomson Reuters Research.

Year

Average Cost per Asset

(US$ billion)

2010 1.089

2011 1.235

2012 1.137

2013 1.348

2014 1.401

Source: Deloitte LLP and Thomson Reuters Research Reports

28

Table 7. Forecast of Industry Operating Cash Flow of the Growth Opportunities (2015 – 2024)

(In $ million except rates as shown)

Item

Initial or

Annual Value

Assumption

Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Year 9

Year 10

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Growth rate in industry sales (%) 0.00 19.73 19.68 19.13 19.59 25.27 24.13 31.18 22.88 39.64

Growth rate in sales post-2034 period -13.72%

Total industry sales $8,950 8,950 10,716 12,825 15,278 18,271 22,888 28,411 37,270 45,797 63,951

Operating margin 28.00%

Operating cash flow before tax 2,506 3,000 3,591 4,278 5,116 6,409 7,955 10,435 12,823 17,906

Taxes 26.43% 657 787 942 1,122 1,342. 1,681 2,087 2,738 3,364 4,698

Net operating cash flow 1,849 2,213 2,649 3,156 3,774 4,727 5,868 7,698 9,459 13,209

Present value of capital investments $1,300

Depreciation expense 65 65 65 65 65 65 65 65 65 65

Total net operating cash flow 1,914 2,278 2,714 3,221 3,839 4,792 5,933 7,763 9,524 13,274

WACCg (growth opportunities) 7.69%

Present value of net operating cash flow $137,991 (assuming a 20-year forecast)

Present value of continuation value (CV) $36,806

Total value of underlying asset (S) $174,797

29

Table 8. Analysis of Competitive Advantage

Factor

Symbol

Gilead Sciences Inc.

Industry

Competition

Internal rate of return, enterprise IRR 21.03% 21.75%

Cost of capital, enterprise WACCe 8.17% 5.83%

Phase-3 plus submission success rate k 55.00% 35.00%

Customer validation of technology CVT 60.79% 45.35%

Expert validation of technology EVT 1.0148 0.9850

Patent productivity (stock) PP 0.0253 0.9747

Patent productivity (newly acquired) 1.0000 0.0000

Innovation probability of success λ 61.69% 44.67%

Competitive advantage factor 0.5454 --

30

Table 9. Valuation Summary for Gilead Sciences Inc.’s Real Growth Options as at Year-

End December 2014

Parameter Symbol Value

Underlying asset value of the industry growth

option

S $174,797 million

Exercise price for the real growth option X $1,300 million

Volatility of industry growth operating cash flow 19.18%

Risk-free rate of return r 4.00%

Competitive advantage factor for Gilead Sciences 0.5454

Number of outstanding shares 1,519.2 million

Value of corporate innovation (growth option) G $96,997 million

Value contribution per share of innovation $63.85

31

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33

Appendix

Review of the Traditional Valuation Models

By reviewing these popularly applied models, I intend to show two important limitations of the

traditional valuation techniques, which are vulnerability to the assumptions of (i) unreasonably high

growth rate (denoted by g), and (ii) unreasonably long growth period (n). The real growth options

model and methodology I introduce in this paper correct for these shortcomings.

The single most powerful motive and objective underlying formation of any business is the creation

and maximization of value, which are attained primarily by growing the business through the

efficient management of innovation and entrepreneurship. From start-up to maturity stage, a

business is expected to grow on a sustainable basis in order to be able to recover the costs of capital

and operation and to reward the owners for their skill and risk-bearing efforts through value

addition. Thus, any credible business valuation model must explicitly present a dimension of

growth in its structure, whether that growth is expected to come internally or to be acquired

externally. Notwithstanding the shortcomings which this study overcomes, it is true that almost all

traditional valuation models do contain a measure of growth especially when the growth forecast

emanates from a firm’s pipeline products. This can be verified by decomposing the models often

used in practice which include the basic DCF, the adjusted net present value (APV), net residual

income (RIM), and the relative valuation (RV) models into their justified fundamental elements.

One of the most important principles underlying the robustness of the DCF, APV, RIM and RV

models is the assumption in Modigliani and Miller (1961) which states that, over the long run, the

growth rates in revenues, earnings and dividends are equal. I present below a careful reformulation

and examination of the stand-alone traditional valuation models and find common key-drivers of

growth for a firm. Here, growth can be identified by the level or size of revenues, which is the

dominant portion of free cash flow (CF), and/or the growth rate (g) in revenue. In estimating

enterprise value (V0) I show, in fact, that these models do provide for growth although in a very

limiting way especially for innovating firms, a problem I attempt to address in this paper:

I. Basic Discounted Free Cash Flow (DCF) Model

A general form of the DCF model can be expressed as:

(i)

gk

gCF

gk

ggHCF

k

CFV Hnsn

n

i

ii

1

1

2

1

0

34

where:

CF = free cash flow to the firm at period i, the cash flow that is expected to

be returned to the suppliers of capital, both equity and debt holders.

gs = abnormally high growth rate of free cash flow during a high-

growth stage;

g = normal, long-term growth rate of free cash flow;

n = explicit normal short- to intermediate-term forecast period;

H = half the length of the abnormally high-growth period;

k = the weighted average cost of capital.

In Equation (i), the first term on the right-hand side represents an early mixed growth stage, the

second term denotes a supernormal transition growth stage, and the third term represents a stable

long-term growth stage. Depending on the parameter values, practitioners usually employ different

formats of Equation (i) which can easily take the form either of the H-model, zero-growth model,

constant growth model, or multistage growth model. For instance, if gs = g for j = 1, 2, ….., n, then

H = 0 and the equation collapses to the traditional Gordon’s constant growth model. And if n = 1,

then the equation represents the H-model. In essence, gs, g and H are all measuring some

dimensions of growth. The last term in Equation (i) captures the hockey stick phenomenon.

II. Adjusted Net Present Value (APV) Model

Although usually considered a variation of the DCF formula given by Equation (1), a general form

of the adjusted present value (APV) model can be presented as follows:

gr

gUCF

gr

ggHUCF

r

UCFTDV Hnsn

n

i

ii

1

1

2

1

00 (ii)

where:

D0 = expected average debt level;

T = marginal tax rate for the firm;

UCF = unlevered free cash flow at period i;

gs = abnormally high growth rate of unlevered free cash flow during a

high-growth stage;

g = normal, long-term growth rate of unlevered free cash flow;

n = explicit normal short- to intermediate-term forecast period;

H = half the length of the abnormally high-growth period;

35

r = the unlevered cost of capital.

Equation (ii), however, shows an important distinction from the basic DCF method in that the APV

model demarcates the enterprise value into the total present value of interest tax shield (the first

term) and the value of unlevered firm. Again, parameters H, gs and g reflect the claim that the model

does capture components of firm value due to growth. And the last term in Equation (ii) captures

the hockey stick phenomenon.

III. Net Residual Income Model (RIM)

The RIM is expressed as:

or,

n

nnn

ii

i

r

BL

r

BrROEBDV

111

1

000

or,

(iii)

where:

D0 = estimated current value of debt;

B0 = current book value of equity;

Bi = expected book value of equity at any time i;

Ii = expected earnings (net income) for period i;

n = explicit forecast period;

Ln = estimated equity value at terminal period;

ROE = return on equity;

r = cost of equity capital;

g = sustainable growth rate;

b = retention ratio for the firm.

It is apparent from the model that an analyst can come out with an estimated value of the firm

assuming a constant growth forecast. And the last term in Equation (iii) captures the hockey stick

phenomenon.

n

nnn

ii

ii

r

BL

r

rBIBDV

111

1

000

nnn

n

ii

i

r

BL

rb

BbrgBDV

111

1000

36

IV. Relative Valuation (RV) Approach

The RV technique, sometimes referred to as the comparable firms analysis or the multiples

approach, takes the following form:

Compc

c

I

VIDV

00

(iv)

where:

D0 = estimated current value of debt;

(Vc /Ic) = price-to-earnings multiple of a benchmark (comparable);

I = current earnings (net income) of the firm;

b = retention ratio of the benchmark firm(s);

g = long-term growth rate of earnings of the benchmark;

r = the cost of equity capital for the benchmark.

Assuming a stable long-term condition for the benchmark firm where g represents its sustainable

growth rate; the firm’s return and cost on equity have converged; and considering that the

sustainable rate (g) can be estimated as the product of retention ratio (b) and return on equity (ROE),

then the enterprise value presented by Equation (iv) can simply be expressed as:

Compr

gID

gr

gbIDV

111000

(v)

As with the DCF, APV, and RIM, the RV model does actually capture growth when I make the

assumption of a long-term constant growth forecast. And the last term in Equation (v) captures the

hockey stick phenomenon.