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Trading real options analysis course - business cases and software applic - Mun P.D.

Mun P.D. Trading real options analysis course - business cases and software applic - Wiley publishing , 2003. - 318 p.
ISBN 047-43001-3
Download (direct link): tradingohnathan2003.pdf
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Phases
If the firm decides that its portfolio management and review process should include options analysis, you can stage the investment to allow options for continuing, delaying, or abandoning.
Exit and Abandonment
Early drug development holds numerous options to abandon if the compound does not look to be successful. The exit option increases the value of the project because it reduces the size of the investment at risk.
Learning
Before the firm launches its new drug advertising campaign across the United States, you release ads on a limited number of markets in select cities and then refine the marketing plans based on what is learned.
Expansion
Launch the injectable drug preparation to preserve dominant position in the marketplace while an inhalable drug preparation completes the final stages of development.
FIGURE 3.11 Framing real options in pharmacology.
developed only after the first two stages such that the company can license off its patent rights, etc.), or at a certain phase, the technology is mature enough such that it can be spun off into other ancillary products, thereby creating additional value to the existing project.
On occasion, strategy trees look similar to decision trees where probabilities of occurrence are created, as in Figure 3.15. Recall from my previous book, Real Options Analysis, that decision trees are a valuable tool, useful in setting up and visualizing real options strategies but cannot and should not be used to value real options projects. This restriction is due to the subjective probability estimates required on each branch of a tree, as well as
PHASE II
At the end of Phase I, the firm has the option either to continue on to Phase II or not. As an example, suppose Phase II is the actual development phase and Phase I is the market research phase.
What is the value of information given an uncertainty in the technology? How much would you be willing to pay to obtain the information?
END
START -$5M -$80M +$30M +$35M +$40M +$48M
V__________________J V. _ __>
V V
INVESTMENT CASH FLOW
PERIOD PERIOD
FIGURE 3.12 Simple two-stage R&D real options process.
In reality, an R&D project will yield intellectual property and patent rights that the firm can easily license off (Abandon). In addition, at any phase, the project's development can be slowed down (Contract) or accelerated (Expand) depending on the outcome of each phase.
PHASE III
PHASE I
START
CONTRACT
ABANDON
END
END
An NPV analysis cannot account for these options to make midcourse corrections over time, when uncertainty becomes resolved.
FIGURE 3.13 Complex multiple -stage process.
51
Another potential issue is synergy.
Even if the development of the current technology is unsuccessful, the knowledge and insights gained may be applicable to some other product (Technology B).
PHASE I TECH B
PHASE II TECH B
START
PHASE I
END
The new technology will yield a potential 15% increase in projected revenues if implemented. However, Technology B can be applied only after the success of Phase IIís R&D efforts.
FIGURE 3.14 Complex customized R&D options.
high
END
START
DRILL WILDCAT END
DRILL WILDCAT ABANDON ACREAGE
RUN
SEISMIC
DROP
ACREAGE
DRILL
WILDCAT
START
ACREAGE
FIGURE 3.15 Strategy tree with probabilities in oil and gas.
52
Step 5: Framing the Real Options
53
different market risk-adjusted discount rates required on each node of the tree. The errors in estimation compound significantly and the resulting expected value calculated will be in error.
Instead, if probabilities of success are required in the analysis, rather than making subjective guesses and scenarios (high, medium, and low estimates in the market) as seen in the upper decision tree in Figure 3.15, these subjective guesses should be collapsed into distributions of outcomes using Monte Carlo simulation and applied in the DCF models. For instance, use a Triangular distribution on the revenues and run 10,000 simulation trialsó this provides a higher level of accuracy as simulation run in this instance is simply recreating 10,000 scenarios based on these probabilities as opposed to running only 3 scenarios. In addition, any number of other probabilistic or stochastic variables can also be modeled and their interactions, comovements, or correlations can also be modeled easily, using Monte Carlo simulation (see the lower decision tree in Figure 3.15).
After identifying the real options in each project, the dynamics of the uncertainty and corresponding flexibility inherent in the projects should be determined. For example, Figure 3.16 shows the application of a feedback loop to model the qualitative and quantitative critical success drivers and uncertainty drivers in the project. Using techniques such as feedback loops and
54
FRAMING REAL OPTIONS
influence diagrams (similar to feedback loops but the influence of uncertainty does not loop back to itself over time), the required uncertainties can be determined and modeled back into the DCF. Based on this approach, all relevant data can then be collected, estimated, or simulated in the model.
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