Principles of Corporate Finance_ 12th Edition

262 Part Three Best Practices in Capital Budgeting

bre44380_ch10_249-278.indd 262 09/30/15 12:45 PM

is a simple matter to analyze the principal sources of uncertainty in the cash flows and to see

how much you could reduce this uncertainty by improving the forecasts of sales or costs. You

may also be able to explore the effect of possible modifications to the project.

Simulation may sound like a panacea for the world’s ills, but, as usual, you pay for what

you get. Sometimes you pay for more than you get. It is not just a matter of the time spent in

building the model. It is extremely difficult to estimate interrelationships between variables

and the underlying probability distributions, even when you are trying to be honest. But in

capital budgeting, forecasters are seldom completely impartial and the probability distribu-

tions on which simulations are based can be highly biased.

In practice, a simulation that attempts to be realistic will also be complex. Therefore, the

decision maker may delegate the task of constructing the model to management scientists or

consultants. The danger here is that even if the builders understand their creation, the decision

maker cannot and, therefore, does not rely on it. This is a common but ironic experience.

◗ FIGURE 10.4

Simulation of cash

flows for year 10 of

the electric scooter

project.

Cash flow, billions of yen

Year 10: 10,000 Trials

8.07.57.06.56.05.55.04.54.03.53.02.52.01.51.00.50 8.5 9.0

Frequency

0.050

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.045

0.040

0.035

10-4 Real Options and Decision Trees

When you use discounted cash flow (DCF) to value a project, you implicitly assume that the firm

will hold the assets passively. But managers are not paid to be dummies. After they have invested

in a new project, they do not simply sit back and watch the future unfold. If things go well, the

project may be expanded; if they go badly, the project may be cut back or abandoned altogether.

Projects that can be modified in these ways are more valuable than those that do not provide such

flexibility. The more uncertain the outlook, the more valuable this flexibility becomes.

That sounds obvious, but notice that sensitivity analysis and Monte Carlo simulation do

not recognize the opportunity to modify projects.^13 For example, think back to the Otobai

electric scooter project. In real life, if things go wrong with the project, Otobai would abandon

(^13) Some simulation models do recognize the possibility of changing policy. For example, when a pharmaceutical company uses simu-
lation to analyze its R&D decisions, it allows for the possibility that the company can abandon the development at each phase.