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Capital Budgeting under Risk and Uncertainties^145

Step 2 Refer to the standard normal distribution table and find the probability to the

left (or right depending on our interest) of the Z value obtained in step 1.

To illustrate the above procedure suppose that a projectís NPV and s(NPV) are Rs

96,000 and Rs 60,000 respectively and we want to find the probability that NPV will

be less than 0. This may be done as follows:

Step 1 The standardised difference between the specified point (NPV = 0) and

NPV = 96,000 is

0 96000
6000

• =- 1. 6

Step 2 The cumulative probability up to Z = -1.6 as seen from the standard normal

distribution given in Appendix A is 0.55. This means that there is a 5.5 per

cent chance that NPV will be equal to or less than 0.

Simulation Analysis

Sensitivity analysis indicates the sensitivity of the criterion of merit (NPV, IRR, or any

other) to variations in basic factors and provides information of the following type: If

the quantity produced and sold decreases by 1 per cent, other things being equal, the

NPV falls by 6 per cent. Such information, though useful, may not be adequate for

decision making. The decision maker would also like to know the likelihood of such

occurrences. This information can be generated by simulation analysis which may be

used for developing the probability profile of a criterion of merit by randomly combining

values of variables which have a bearing on the chosen criterion.

Procedure

The steps involved in simulation analysis are as follows:

1. Model the project. The model of the project shows how the net present value
   is related to the parameters and the exogenous variables. (Parameters are input
   variables specified by the decision maker and held constant over all simulation
   runs. Exogenous variableís are input variables which are stochastic in nature
   and outside the control of the decision maker).


2. Specify the values of parameters and the probability distributions of the exog-
   enous variables.


3. Select a value, at random, from the probability distributions of each of the
   exogenous


4. Determine the net present exogenous variables and pre-specified parameter
   values.


5. Repeat steps (3) and (4) a number of times to get a large number of simulated
   net present values.


6. Plot the frequency distribution of the net present value.