Intermediate Financial Management (with Thomson One)

Recent developments in technology have
made it easier for corporations to utilize
complex risk analysis techniques. New software
and higher-powered computers enable
financial managers to process large amounts
of information, so technically astute finance
people can consider a broad range of scenarios
using computers to estimate the effects of
changes in sales, operating costs, interest
rates, the overall economy, and even the
weather. Given such analysis, financial managers
can make better decisions as to which
course of action is most likely to maximize
shareholder wealth.
Risk analysis can also take account of the
correlation between various types of risk. For
example, if interest rates and currencies tend
to move together in a particular way, this
tendency can be incorporated into the
model. This can enable financial managers to
make better estimates of the likelihood and
effect of “worst-case” outcomes.
While this type of risk analysis is undeniably
useful, it is only as good as the information
and assumptions used in the models.
460 • Part 3 Project Valuation
HIGH-TECH CFOs
Also, risk models frequently involve complex
calculations, and they generate output that
requires financial managers to have a fair
amount of mathematical sophistication.
However, technology is helping to solve
these problems, and new programs have
been developed to present risk analysis in an
intuitive way. For example, Andrew Lo, an
MIT finance professor, has developed a program
that summarizes the risk, return, and
liquidity profiles of various strategies using a
new data visualization process that enables
complicated relationships to be plotted
along three-dimensional graphs that are
easy to interpret. While some old-guard
CFOs may bristle at these new approaches,
younger and more computer-savvy CFOs are
likely to embrace them. As Lo puts it:
“The video-game generation just loves these
3-D tools.”
Source: “The CFO Goes 3-D: Higher Math and Savvy Software Are
Crucial,” BusinessWeek, October 28, 1996.
NPVs is determined. The mean, or average value, is used as a measure of the project’s
expected NPV, and the standard deviation (or coefficient of variation) is used
as a measure of risk.
Using this procedure, we conducted a simulation analysis of RIC’s proposed
project. As in our scenario analysis, we simplified the illustration by specifying the
distributions for only four key variables: (1) sales price, (2) variable cost, (3) Year
1 units sold, and (4) growth rate.
We assumed that sales price can be represented by a continuous normal distribution
with an expected value of $3.00 and a standard deviation of $0.35. Recall
from Chapter 2 that there is about a 68 percent chance that the actual price will
be within one standard deviation of the expected price, which results in a range of
$2.65 to $3.35. Put another way, there is only a 32 percent chance that the price
will fall outside the indicated range. Note too that there is less than a 1 percent
chance that the actual price will be more than three standard deviations of the
expected price, which gives us a range of $1.95 to $4.05. Therefore, the sales price
is very unlikely to be less than $1.95 or more than $4.05.
RIC has existing labor contracts and strong relationships with some of its suppliers,
which makes the variable cost less uncertain. In the simulation we assumed
that the variable cost can be described by a triangular distribution, with a lower