Using the Monte Carlo Method to Value Early Stage, Technology-Based Intellectual Property Assets

Bruce W. Burton, CPA, CFF, CMA, CLP – bburton@srr.comScott Weingust – sweingust@srr.comJake M. Powers – jpowers@srr.com

1 ©2013

Valuing early stage, technology-based intellectual property assets

is challenging, in large part due to the difficulty in incorporating

the effects of risk and uncertainty inherent in these assets into

their valuation. Monte Carlo methods were originally designed to

model physical and mathematical problems. However, variations

of this method also provide valuation analysts with a powerful

tool to effectively address risk and uncertainty, particularly in

the context of determining intellectual property values related to

transactions or strategic decision-making.

The challenge of assessing and incorporating risk into various methods used for valuing intellectual property n n n

Technology-based intellectual property (“IP”) assets, usually protected as patents and/or trade secrets, are typically valued using the same three common approaches as are used to value businesses or other assets. These approaches include,

1) income approach,1 2) market approach,2 and 3) cost approach.3

However, technology-based IP assets (and many other IP assets

including patents and trade secrets unrelated to technology,

along with trademarks and copyrights) pose many unique

challenges to a valuation analyst. A few illustrative examples of

such challenges include:

n Income approaches are often difficult to implement for a variety of reasons, including the difficulty in quantifying the portion of a product or service’s cash flows that are attributable to the subject IP asset.

n Market approaches are often difficult to implement for many reasons, including the fact that IP assets are, by definition, unique. As such, comparable market transactions are often difficult or impossible to find. In addition, because IP assets are not traded on public markets and the transactions themselves are typically confidential, there are few public sources that reveal deal details that would be sufficiently comparable to be used to implement a market approach, and the data available from sources that do exist is often incomplete.

n Cost approaches are often difficult to implement because the cost to create the subject assets is almost always unrelated to the value of the asset (e.g., income generation, cost savings, etc.) that can be gained from use of the asset.

1 Per the International Glossary of Business Valuation Terms, Appendix B to the Statement on Standards for Valuation Services (“SSVS”) promulgated by the American Institute of Certified Public Accountants (“AICPA”), the income approach is defined as “a general way of determining a value indication of a business, business ownership interest, security or intangible asset using one or more methods that convert anticipated economic benefits into a present single amount.”

2 Per the International Glossary of Business Valuation Terms, Appendix B to the SSVS promulgated by the AICPA, the market approach is defined as “a general way of determining a value indication of a business, business ownership interest, security or intangible asset by using one or more methods that compare the subject to similar businesses, business ownership interests, securities or intangible assets that have been sold.”

3 Per the International Glossary of Business Valuation Terms, Appendix B to the SSVS promulgated by the AICPA, the cost approach is defined as “a general way of determining a value indication of an individual asset by quantifying the amount of money required to replace the future service capability of that asset.”

2©2013

However, in addition to these challenges, perhaps the most

difficult issue associated with valuing technology-based IP assets

is accounting for the significant risks associated with many of

these assets. Accounting for risk is particularly difficult in the

very common situation when technology-based IP assets are

valued prior to any (or significant) commercialization success;

i.e., when the assets are “early stage.”

Early stage, technology-based IP assets are inherently risky for a

variety of reasons, including, but not limited to:

n Claims included in the patent applications may not survive to the issued patents and the scope of surviving claims may be uncertain

n Issued patents may prove to be invalid when challenged

n Trade secret protections are not guaranteed

n Successful completion of an in-process technology is not guaranteed

n Implementation of the subject technology into products and services may be difficult or impossible

n Manufacturing scale-up may not be technically viable

n Costs of R&D, product integration, and manufacturing scale-up may be much higher than anticipated, perhaps even prohibitively high

n Market success has not been convincingly proven and often cannot even be tested until late in the product development process

n Anticipated regulatory approvals may be delayed or denied

n Unanticipated safety and efficacy issues may arise related to the in process or finished product

n Non-infringing alternatives to the subject assets or design-around options may be difficult to identify and assess

n Innovation may be moving at a rapid pace, causing the economic life of a particular technology to be unknown and, perhaps, short-lived

Risk and uncertainty associated with early stage, technology-

based IP assets can be addressed by the valuation analyst through

a number of methods, including:

n Performing significant due diligence to identify, understand, and assess the various areas of risk and uncertainty

n When using an income approach, adjusting the discount rate used as part of a discounted cash flow (“DCF”) model upward to reflect the identified and assessed risks4

n Using sensitivity analysis to understand the effect on value from changing certain variables

n Developing various scenarios (best, likely, worst case, etc.)

n Implementing decision-tree analysis

n Using option pricing techniques

In addition to these and other methods, the use of Monte Carlo

simulations in conjunction with the Income Approach provides

the valuation analyst with a flexible, powerful tool for performing

valuations of early stage, technology-based IP assets. Given the

nature of Monte Carlo simulations, they are particularly useful

when the valuation is being performed to support transactions or

strategic decision-making.

An explanation of the Monte Carlo method n n n

The Monte Carlo method is a probabilistic technique that allows the

analyst to run many “what-if” scenarios to arrive at a probability-

weighted distribution of possible asset values rather than arriving

at a single value as is the case for many other valuation methods.

The Monte Carlo method is most often used in conjunction with

the application of an Income Approach to valuing early stage,

technology-based IP assets. Compared to a traditional DCF

model that generates a single net present value (“NPV”) result, the

Monte Carlo model, available through various software programs

and Microsoft Excel plug-ins, gives the user the flexibility to

assign various probability distributions to key assumptions and

run a large number of trials to determine a distribution of NPVs

based on the variability assigned to key assumptions. In doing

so, the users of the model are able to better account for the

inherent uncertainty in predicting the future value of key

assumptions and, therefore, provide a more holistic look at the

potential value of relevant IP assets.

As mentioned earlier, estimating the value of early stage,

technology-based IP assets often involves a considerable degree

of uncertainty, given the vast number of possible values of many

of the key assumptions that can affect a DCF model. As one

example, an estimate of total future research and development

(“R&D”) costs expected to be incurred to complete or implement

an in-process technology, and the timing of such expenditures,

could vary significantly as of the date of the valuation. The Monte

Carlo method allows the analyst to account for this inherent

uncertainty of values related to key DCF assumptions in the model

by assigning 1) various potential values, or a range of values, for

each relevant assumption/variable and 2) a probability distribution

of varying types. The DCF model can then be run multiple times

to generate a range of potential values using these different

potential inputs. It is not uncommon to run tens of thousands of

trials, if not more, to generate an accurate distribution of possible

4 From our experience, and supported by various third parties, discount rates used in conjunction with discounted cash flow models for valuing early stage, technology-based intellectual property assets commonly range from 20 percent to 75 percent (and sometimes higher). This is in stark contrast to discount rates used, for example, when valuing businesses, which typically reflect the subject business’ weighted average cost of capital (“WACC”). Per Morningstar, as of December 31, 2012, the median WACC for a sampling of 381 large-capitalization companies was 7.73 percent.

3 ©2013

NPVs. Essentially, the program is simulating all the possible NPV

outcomes, given the variables, variable ranges, linkages between

the variables, and distributions of these variables provided by the

valuation analyst.

Many assumptions are used when valuing early stage, technology-

based IP assets. When using the Monte Carlo method, the user

has the capability to decide whether each assumption is a single

value or whether it would be best to use a probability distribution

to assign a range of values to an assumption. The type of

probability distribution assigned to the assumptions are flexible

in that the user can define the type and shape of the distribution5

along with the mean, standard deviation, and any upper

or lower bounds. For instance, one of the reasons we decided to

use the Monte Carlo method in the example we describe later in

this article was the significant variability of the possible outcomes

from our key value drivers.

Some of the variables and related potential outcomes were

discreet such as, “Will the product receive U.S. Food and Drug

Administration (“FDA”) approval? The answer to this question would

be a simple “yes” or “no” with assigned ranges of probabilities

associated with each. However, other variables had three or four

possible outcomes with differing probabilities of occurrence. In

addition, other variables had continuous distributions of various

kinds, such as a “normal” or “Pareto” distributions.

Once all variables have been identified and their ranges and

probability distributions selected, the valuation analyst can

perform many “runs” of the model to determine the resulting

unique NPV. For each “run” the software selects a specific value

for each of the variables based upon the range, distribution, and

probability of outcomes provided for each variable. The distribution

of possible NPVs, or outcomes, generated as a result of running

the DCF model many, many times with various combinations of

values for the variables provides a probability-weighted range

of NPV outcomes accurately reflecting the myriad combinations

of the ranges, distributions, and probabilities input for each the

key variables.

As mentioned earlier, risk and uncertainty are often addressed in

a DCF model through the determination of a single, appropriate

discount rate. However, when dealing with early stage, technology-

based IP assets, this approach may have certain challenges. In

particular, by compressing many individual risk elements into one

discount rate, the analyst may be challenged to focus on and

evaluate any one individual risk when the risks are many and the

future is very uncertain. An advantage of the Monte Carlo method

is that it allows the valuation analyst to shift the recognition of

risk and uncertainty away from the discount rate to the cash

flow projections. This is an advantage because the specific risks

formerly bundled together in the discount rate can be much more

closely analyzed and quantified through their effect on the NPV

of projected future cash flows. Especially where there is great

uncertainty and complexity, the Monte Carlo method allows the

user to explicitly model the distribution of risks around key value

drivers based on the best current information and expectations.

The software performs the tens of thousands of computations

necessary to model the interactions of the various key variables

into a resulting range of probability-adjusted NPV outcomes. As

a result, the Monte Carlo method allows the valuation analyst to

visualize and make statistical statements around various predicted

outcomes of the DCF model.

A Case Study for the Application of the Monte Carlo Method n n n

By way of illustration, we present below an example of one of our

actual applications of the Monte Carlo method.6

We were asked to assist a medium-sized medical device company

– “ExampleCo” – in its evaluation of the possible introduction of a

new, patent-protected, cutting-edge medical device. Introduction of

this product was capital intensive, requiring substantial long-term

expenditures in R&D as well as investment in a capital-intensive

manufacturing process. At the date of the valuation, investment-

to-date was over $150 million and prospective investment was

expected to be another $100 million. This investment was viewed by

our client’s management and their board of directors as a “bet the

company” decision and they invited us in to help them to research,

evaluate, and model their options so that they could make a well-

informed decision regarding how to proceed with the project.

The company was facing substantial uncertainties on many fronts

related to its prospective new product. To name but a few, it was

facing such issues as:

n Its ability to complete the product and make it function properly

n Its ability to complete the project on time and on budget

n Market acceptance and the level of worldwide demand for its product

n The extent of cannibalization of its own existing products by the new product

n Emerging competing products and technologies

n Regulatory acceptance such as FDA approval

n Reimbursement under federal medical insurance programs

n Eligibility for, and rate of reimbursement under, medical insurance coverage

5 Illustrative standard distribution types that can be used include: Normal, Triangular, Uniform, Lognormal, Beta, Gamma, Exponential, Pareto, Poisson, etc. In addition, the valuation analyst can typically create his/her own custom distribution type.

6 Note that the facts and results regarding the project have been modified to preserve confidentiality.

4©2013

Initially, we created a traditional multi-year DCF model addressing

issues such as sales revenues, services revenues, costs of goods

sold (“COGS”), service and warrants costs, selling, general and

administrative (“SG&A”) costs, royalties payable,7 development

costs, taxes, and other cost items specific to the new product

introduction. When completed, this model provided us with a

“point-estimate” of the IP embodied in the new product under

development. In this initial valuation analysis, all of the risk and

uncertainty associated with the values of each of these variables

was incorporated into the discount rate used to discount future

cash flows to the present in the form of an NPV. Because risks

associated with many or all of the variables were pervasive,

complex, and/or interactive among the variables, we decided to

use the Monte Carlo method. We took advantage of the initial DCF

analysis that generated the “point estimate” and used it as the

base upon which we built the Monte Carlo simulation.

As an early step in the process, we described the expected range

of possible outcomes and the expected “shape” of the distribution

of the reasonably possible outcomes associated with the key

value drivers. See Figure 1 below as an illustration of assigning a

distribution to a value driver. This figure represents the distribution

of outcomes and their related probabilities associated with total

R&D costs. The graph depicts a Pareto distribution of outcomes,

with a 50 percent probability that the total R&D costs would finish

on budget (we assigned no chance that the new product research

would be completed under budget) and generally diminishing

probabilities of overrun amounts up to approximately a maximum

50 percent overrun of the R&D budget. Our R&D cost estimation

was informed by discussions with the project leaders, study of the

client’s similar prior projects, and identification and examination of

competitors’ comparable projects.

An attractive feature of the Monte Carlo method is that it allows

the valuation analyst to establish a positive correlation, or linkage,

between value drivers. Both the number of the linkages to other

variables and the extent of correlation between variables can be

determined and specified by the modeler. In this particular case,

there was a linkage with a positive 50 percent correlation between

total R&D cost overruns and another relevant variable, “number of

months delay in product launch.”

After following a similar process of assigning low and high values

and distributions to the other value drivers, we proceeded to run

the DCF model using the Monte Carlo tool for individual lines on

the cash flow forecast such as price per unit, unit sales, COGS,

and SG&A expenses. This intermediate step was performed to, 1)

understand how these revenue or cost items were behaving based

on the modeled distributions and linkages between variables, and

2) determine the relative effect of the individual value drivers within

each revenue or cost category. For instance, Figure 2, shows an

illustrative distribution of unit sales in thousands.

After the DCF model was run through the Monte Carlo simulator

10,000 times, the unit sales summary variable had the distribution

shown in Figure 2. The distribution of results ranges from 0 units

sold to almost 140,000 units sold. As can be seen from the figure,

this distribution turned out to resemble a “normal” distribution

with, 1) an outlier probability of 10 percent that there would be

zero units sold, and 2) a slight skew toward the higher-value side

of the distribution. In this example, both the mean and median was

56,000 units sold as indicated by the tall dark blue bar. The other

tall bar at zero units sold represents our judgment that there was

a 10 percent chance that the project would fail and, as a result,

never produce any commercial sales.

7 Material royalties were payable on licensed technologies embedded in the products being introduced.

Source: U.S. Bureau of Economic Analysis, University of Michigan Consumer Confidence Report

Figure 1 – Distribution and Probability of R&D Expense Variable

1.000 1.040 1.080 1.120 1.160 1.200 1.240 1.280 1.320 1.360 1.400 1.440 1.480

Prob

abilit

y

Pareto Distribution

5 ©2013

When all variables were combined, our total project NPV estimate

looked like the distribution shown in Figure 3, which portrays the

results of 100,000 separate simulations of the project’s results for

the company. As can be seen, the project’s distribution of NPV

results is still approximately a “normal” distribution skewed slightly

to the higher project values

with an offsetting pillar of

negative NPV outcomes

assuming project failure

and zero commercial sales.

The mean of the distribution

is $40.6 million and the

median is $39.0 million.

The point where the bars

change in color from orange

to blue represents the

NPV at which the client

determined a go/no-go

decision would be made.

This ability to visualize and

make statistically valid

statements regarding the

results of the analysis is

one of the key advantages

of using the Monte Carlo

method over point-estimation techniques. Another major

advantage is the unbundling of risk adjustments from residing

solely in the discount rate. In fact, in this instance, we reduced the

discount rate we used when implementing the Monte Carlo method

from over 40 percent that was used in the initial DCF model that

generated a point-estimate NPV to just above 12 percent in our

Monte Carlo analysis.8

As an illustration of the

modeling capabilities of the

Monte Carlo software we

used, note that the zero-

value results in Figure 3

do not look the same as

the zero units bar in Figure

2. The reason is that we

modeled that the “no-go”

decision could be made at

different times after differing

types and amounts of

investments; hence there

are different levels of losses

associated with the different

dates at which the project

might be terminated. Figure

3 also shows that there is

some possibility that the

new product may actually

enter production but never make it to profitable levels. On the other

hand, the graphic in Figure 3 demonstrates that there is almost a 5

percent chance of NPV results in excess of $120 million.

In this article we introduced the Monte Carlo method, one of

several commonly used financial modeling tools employed by IP

valuation analysts. The Monte Carlo method is particularly effective

when used to determine the value of early stage, technology-

based IP assets and is well suited to address valuation issues

in the context of transactions and strategic decision-making.

However, compared to the use of many other valuation tools,

implementation of the Monte Carlo method has certain challenges.

8 The rationale and mechanics underlying this reduction is beyond the scope of this article.

Figure 2 – Distribution of ExampleCo’s Potential Expected Unit Sales

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00

1000

900

800

700

600

500

400

300

200

100

0

Prob

abilit

y

Freq

uenc

y

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

Total Cumulative Unit Sales – Distribution of Outcomes

Units Sold

Median = 56

Mean = 56

Figure 3 – Distribution of Total NPV (Before cost to exercise option)

0.03

0.02

0.01

0.00

3,800

3,600

3,400

3,200

3,000

2,800

2,600

2,400

2,200

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Prob

abilit

y

Freq

uenc

y $(90,000) $(60,000) $(30,000) $0 $30,000 $60,000 $60,000 $120,000 $150,000 $180,000

Total Value

Median = $39,021Mean = $40,562

6©2013

For instance, use of the Monte Carlo method can involve some

initial investment of time devoted to understanding the technique

and related software. In addition, the use of the technique often

requires additional time not necessarily required of other methods

to model the variables and to perform due diligence to support the

more detailed modeling. Consequently, it is prudent for the analyst

to carefully evaluate each particular valuation opportunity in light

of the particular costs and benefits associated with the Monte

Carlo method before making the choice to use this method. With

that said, it has been the authors’ experience that if the choice is

made to invest in the Monte Carlo method, the analyst is typically

rewarded with insightful and intuitive outputs accurately reflecting

the various risks associated with the IP being valued.

Bruce W. Burton, CPA, CFF, CMA, CLP is a Managing Director

in the Dispute Advisory & Forensic services Group at Stout Risius

Ross (SRR). The focus of Mr. Burton’s practice is commercial

litigation with a special emphasis on IP litigation and IP valuation.

Mr. Burton can be reached at +1.312.752.3391 or bburton@srr.com.

Scott Weingust is a Director in the Dispute Advisory & Forensic

services Group at Stout Risius Ross (SRR). He has over 16 years

of experience providing consulting services to corporations, law

firms, and universities primarily in the areas of intellectual

property litigation and valuation. Mr. Weingust can be reached at

+1.312.752.3388 or sweingust@srr.com.

This article is intended for general information purposes only and is not intended to provide, and should not be used in lieu of, professional advice. The publisher assumes no liability for readers’ use of the information herein and readers are encouraged to seek professional assistance with regard to specific matters. Any conclusions or opinions are based on the individual facts and circumstances of a particular matter and therefore may not apply in other matters. All opinions expressed in these articles are those of the authors and do not necessarily reflect the views of Stout Risius Ross, Inc. or Stout Risius Ross Advisors, LLC.