using monte carlo method value early stage ip assets
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Monte carlo valuationTRANSCRIPT
Using the Monte Carlo Method to Value Early Stage, Technology-Based Intellectual Property Assets
Bruce W. Burton, CPA, CFF, CMA, CLP – [email protected] Weingust – [email protected] M. Powers – [email protected]
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 [email protected].
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 [email protected].
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.