Financial Modeling with Crystal Ball and Excel Chapter 7 pptx

CHAPTER 7 Net Present Value and Internal Rate of Return Now that we have completed your introduction to Crystal Ball, we will begin looking at several different types of situations for w

CHAPTER 7 Net Present Value and Internal Rate

of Return

Now that we have completed your introduction to Crystal Ball, we will begin looking at several different types of situations for which Crystal Ball models are useful We start with net present value (NPV) models, because using Monte Carlo simulation to develop distributions of NPV is a source of controversy among some academics even though it is done routinely by practitioners In this chapter, we will consider both sides of the controversy and see some models where the distribution of NPV can help the decision maker gain insight into the problem at hand We will also consider the pros and cons of using internal rate of return (IRR) as a Crystal Ball forecast It is assumed that you are already familiar with these concepts For more background information on NPV and IRR, see any introductory finance textbook such as Melicher and Norton (2006)

DETERMINISTIC NPV AND IRR

Suppose that you have the opportunity to purchase an annuity that costs you $100

at Year 0, and is certain to return $30 to you at the end of each Year 1 through

5 These cash flows are depicted in the Excel chart on the spreadsheet segment in

Figure 7.1 Denote the cash flow at the end of Year t as C t, and the relevant annual

rate of interest as r Then the NPV of the annuity is defined as

NPV=

5

t=1

C t

For the cash flows in Figure 7.1, if r= 10 percent then NPV = $13.72 as shown

in cell B11 Therefore, the annuity is a good investment for any individual with a required minimum rate of return of 10 percent because the investment’s NPV of

$13.72 is greater than zero at that rate

Be aware that the definition of NPV in Expression 7.1 is slightly different from that used by the Excel NPV function To find the NPV of the annuity in Figure 7.1,

105

FIGURE 7.1 Spreadsheet segment to model annuity with deterministic cash flows of−$100 at the end of Year 0, and $30 at the end of Years 1 through 5

we use the Excel formula

which is entered in cell B11 of NPV.xls Most finance textbooks refer to the quantity calculated in this example by Excel’s NPV function as the present value

at end of Year 0 of the cash flows obtained at the ends at Years 1 through 5

To get the net present value, we also consider the investment (negative cash flow)

Net Present Value and Internal Rate of Return 107

FIGURE 7.2 Spreadsheet segment to model stochastic cash flows at the end

of Years 1 through 5 Model 1 cash flows in Years 1 through 5 are IID normal(30,3) Model 2 cash flows follow an additive random walk with normal(0,3) increments

at Year 0, denoted by C0 in Expression 7.1 This can be confusing, but the NPV function has been defined this way for so many versions of Excel that Microsoft is understandably loath to change it at this point because so many of their existing customers are accustomed to the nontextbook definition and use it in many of their existing models

As an alternative to NPV, we can also help decide whether to purchase the

annu-ity by calculating its IRR The IRR is defined to be the value of r in Expression 7.1

that makes NPV = 0 Because there is no convenient closed-form expression for calculating IRR, we use Excel’s IRR function to find it for us Notice that there is con-sistency between the financial definition of IRR and Excel’s IRR function Cell B12

in Figure 7.1 shows that the IRR for the annuity is 15.24 percent You can check this by replacing 0.1 with B12 in Formula 7.2 for cell B11 and seeing that NPV=0 SIMULATING NPV AND IRR

Now let’s assume that we can purchase an investment product for $100 that has stochastic cash flows in Years 1–5 We will use two different stochastic processes for the risky cash flows, and compare the results to the annuity described in the previous section

Model 1 The cash flows at the end of Years 1–5 are independent and

iden-tically distributed (IID) over time Specifically, each cash flow is calculated

as C = 30 + 3Z for t = 1, 2, 3, 4, and 5, where each Z is drawn from a

normal(0,1) distribution independently of the Z ts for the other years The Model 1 cash flows are in cells C5:C9 of file NPVModels.xls shown in Figure 7.2

Model 2 The cash flows at the end of Years 1–5 are linked over time

in an additive random walk model Year 1 cash flow is computed as

C1= 30 + 3Z1, so is equal to Model 1’s Year 1 cash flow Years 2–5 cash

flows are computed as C t = C t−1+ 3Z t where the Z t s for t= 2, 3, 4, and 5 are the same normal(0,1) random variates used to generate Model 1’s cash flows The Model 2 cash flows are in cells D5:D9 of file NPVModels.xls shown in Figure 7.2

To compare the effect of the IID model to that of the additive random walk model, look at the differences between distributions of NPVs and IRRs in Figure 7.3 Overlay Chart 1 in Figure 7.3 compares the distributions of NPVs for the two models

of cash flow Each distribution has the same true expected value, which is $13.72

as it is for the annuity shown in Figure 7.1 However, a large difference in the variability of the two distributions is evident in the overlay chart As you can see

in Figure 7.4, the standard deviation of the distribution of Model 1 NPV is $5.14, while in Figure 7.5 the standard deviation of the distribution of Model 2 NPV is

$16.11 This difference in variability is explained by the difference in the models used to calculate the cash flows Because the cash flows are linked to each other in the random walk model (Model 2), their variability increases from year to year For

example, in Model 1 the cash flow for Year 5 is calculated as C5= 30 + 3Z5, so has

a true variance of 32= 9 and standard deviation of 3 In Model 2, the cash flow for Year 5 is linked to all previous years’ cash flows:

C5= C4+ 3Z5

= C3+ 3Z4+ 3Z5

= C2+ 3Z3+ 3Z4+ 3Z5

= C1+ 3Z2+ 3Z3+ 3Z4+ 3Z5

= 30 + 3Z1+ 3Z2+ 3Z3+ 3Z4+ 3Z5,

so has a true variance of 5(32)= 45 and standard deviation 6.708 The increasing dispersion of cash flow distributions over time in Model 2 reflects the decision maker’s increased uncertainty about the cash flows the farther into the future he or she looks This increased uncertainty in cash flows causes the standard deviation for Model 2 NPV to be larger than the standard deviation for Model 1 NPV in Figure 7.2 Overlay Chart 2 in Figure 7.3 shows similar differences in dispersion for the distributions of IRR

Using simulation to find a distribution of net present value is heresy to some finance professors, yet many analysts do this routinely without knowing that it is con-troversial When the concept of using distributions of NPV to compare investments

Net Present Value and Internal Rate of Return 109

FIGURE 7.3 Overlay charts to compare distributions of NPV (Overlay Chart 1) and IRR (Overlay Chart 2) for Models 1 and 2 in Figure 7.2 In both charts the distribution for Model 1 cash flows has much less dispersion than the distribution for Model 2 cash flows

was first promoted some 40 years ago by Hertz (1968), computers were not widely available to managers as they are now At that time, the only practical method of calculating present value available to financial analysts was to estimate the expected value (mean) of potential cash flows for each future period and discount them as

we did for each deterministic C tin Expression 7.1 Doing so ignores the variation in potential future cash flows, and could lead the uninitiated to conclude that there is no difference between the annuity and the investment with stochastic cash flows How-ever, there is clearly a difference in the nature of these investments, and using simu-lation to help illustrate the differences can be eye-opening for many decision makers The controversy over whether to use simulation to calculate a distribution of NPV stems from the definition of NPV long ago as the sum of discounted expected cash flows Under this definition, the NPV of any investment is a single number and some adherents of this definition bristle at talk of a distribution of NPV Proponents

of simulation, however, advocate finding the distribution of the sum of discounted potential cash flows from an investment as we did in Figures 7.4 and 7.5, then using the distribution for analyzing the investment’s riskiness When speaking to those who bristle, you may find it helpful to refer to a distribution such as those in Figure 7.4

or 7.5 as a distribution of potential NPV rather than a distribution of NPV.

Notice that in this case the means of the distributions of potential net present value are the same (within sampling error) as the NPV calculated as the sum of discounted expected cash flows, $13.72 This will not hold true for all models, and

in the next section we will see a model for which the sum of the discounted expected cash flows will not be equal to the expected value of the distribution of the sum of potential cash flows, even after accounting for sampling error

FIGURE 7.4 Split view of forecast chart and statistics window for Model 1 NPV in Figure 7.2

Net Present Value and Internal Rate of Return 111

FIGURE 7.5 Split view of forecast chart and statistics window for Model 2 NPV in Figure 7.2

CAPITAL BUDGETING

For an illustration of using simulation for capital budgeting decisions, consider an example from Chapter 10 of the excellent and popular finance textbook by Brealey, Meyers, and Allen (2006) I have tried to make the description below and the Excel models self-complete, but for a fuller discussion of this project see BMA 2006 Figure 7.6 shows a spreadsheet model in ScooterNPV.xls for the Otobai Com-pany, who are considering the introduction of an electrically powered motor scooter for city use The inputs to be varied and their base-case values are in cells A4:B8 The five variable inputs are: market size, market share, unit price (yen), unit variable cost (yen), and fixed cost (billions yen) The model represented in cells A14:C24 can be stated in the following expressions for Investment in Year 0 and the other variables in Years 1–10:

Investment= 15 billion yen Revenue= Market size × Market share × Unit price Variable cost= Market size × Market share × Unit variable cost Depreciation= Investment ÷ 10

Pretax profit= Revenue − Variable cost − Fixed cost − Depreciation

Tax= 0.5 × Pretax profit

Net profit= Pretax profit − Tax Net cash flow= Net profit + Depreciation

FIGURE 7.6 ScooterNPV.xlsspreadsheet model.

In the next section, we use this model to demonstrate the use of Crystal Ball’s Tornado Chart tool for a deterministic sensitivity analysis of the project’s NPV

to the model inputs As is done in BMA 2006, the Tornado Chart tool considers changes in each of the five inputs in turn while the other four are at their base-case values, and keeps track of the corresponding changes in NPV Note that for each

of the five inputs we have defined a Triangular Assumption with parameters shown

in cells C4:E8 This was done to facilitate use of the Tornado Chart tool, not to obtain a realistic simulation model for the investment See Figure 7.14 for a realistic

simulation model that was created for risk analysis of this project.

Tornado Chart Tool

To use the Tornado Chart tool, select Run→Tools→Tornado Chart from the top menu You will see a dialog window like that shown in Figure 7.7 Because NPV

is the only Crystal Ball forecast defined in the spreadsheet, click Next > to indicate

that you wish to analyze the sensitivity of NPV to the inputs

Figure 7.8 is the dialog window for Step 2 Click Add Assumptions to cause the inputs to appear in the list as shown in Figure 7.8 Note that we defined the inputs

as Crystal Ball assumptions only to make this step easier After the inputs appear in

the list, click Next >.

Net Present Value and Internal Rate of Return 113

FIGURE 7.7 Step 1 in using the tornado chart tool

FIGURE 7.8 Step 2 in using the tornado chart tool

The dialog for Step 3 of using the Tornado Chart tool is shown in Figure 7.9 In this window you can change some options that are self-explanatory Note that if you take the default values as shown in Figure 7.9, the tool will consider five different levels for each of the five inputs The levels are determined by the percentiles of the Crystal Ball assumptions that were defined in cells B4:B8 Click Start to get the results

A tornado chart like that shown in Figure 7.10 will appear in a new Excel workbook This chart lists the inputs from top to bottom in decreasing order of the sensitivity of NPV to each input Thus, Figure 7.10 indicates that NPV is most sensitive to unit variable cost, and least sensitive to market size The size of any bar corresponds to the magnitude of change in NPV The color corresponds to the direction of the change in NPV caused by an increase in the input Because the biggest bars are at the top and the smallest bars are at the bottom, the result is a figure that resembles a tornado The tornado chart is useful for initial investigation

of sensitivities to suggest the order in which we should be concerned with the inputs

The spider chart in Figure 7.11 depicts the same information as the tornado chart In this figure, it is the slope of any line that indicates the sensitivity of NPV to

FIGURE 7.9 Step 3 in using the tornado chart tool

Net Present Value and Internal Rate of Return 115

FIGURE 7.10 Tornado chart Darker bars indicate a positive change in the corresponding input

changes in the corresponding input For example, Figure 7.11 indicates that NPV is most sensitive to Unit variable cost because the slope of that line is greatest As the slope of the line is negative, we know that there is an inverse relationship between unit variable cost and NPV The inputs appear in the same order in the spider chart

as they do in the tornado chart

The Tornado Chart tool is useful for a preliminary, deterministic sensitivity analysis of any spreadsheet cell to its precedents Although the tool did not require

us to do so, it made our job easier here to define NPV as a Crystal Ball forecast and the inputs as Crystal Ball assumptions When we do so, the output from the Tornado Chart tool will be labelled with the names that we specified when we defined the forecast and assumptions

A tornado chart resembles the chart provided by Crystal Ball’s Sensitivity Analysis feature, but it differs in how it obtains its results Whereas the Tornado Chart tool obtains results by varying the inputs one at a time deterministically, the Sensitivity Analysis feature of Crystal Ball obtains its results through a statistical

FIGURE 7.11 Spider chart.

analysis of the relationships between the randomly generated inputs and outputs calculated during a set of simulation trials

Risk Analysis

In this section, we consider a simulation model of Otobai’s electric scooter project that follows the suggestions in Chapter 10 of Brealey, Meyers, and Allen (2006) Instead of assuming that the inputs are constant for Years 1–10 as we did above,

we will allow them to vary by year Furthermore, we will link them over time in a multiplicative random walk, and correlate the inputs for each year

Figure 7.12 shows the five stochastic inputs and their base-case values in cells A4:B8 However, we will introduce randomness through ‘‘forecast errors’’ for

each input denoted by e i,t for Inputs i = 1, 2, 3, 4, 5 and Years t = 1, 2, , 10 The

indexing is as follows:

e 1,t = Market size forecast error at year t

e 2,t = Market share forecast error at year t