Capital budgeting is an important area in Financial Management. Capital Budgeting means the investment in capital projects and identify the projects, which has the highest value adding to the company at the cost of capital. It uses net present value of future cash flows discounted at the appropriate cost of capital and compares it with initial investment and to see whether it is a positive net present value. If the present value is less than the initial investment then the project is rejected. That is the net present value is dependent on future cash flows.
In a deterministic model the cash flows are forecasted as a single figure and scenarios are considered one by one and uncertainty of cash flow is not considered. That is in a traditional analysis of net preset value all possible combinations are not considered simultaneously and there is not assessment of the likelihood of getting a positive net present value. In effect the traditional analysis is not adequate and may mislead the decision maker and the net present value may not materialize and there fore the company may looses due to error in judgment using the incorrect analysis which overlooks the complexity of combination of scenarios.
Say for example a person is throwing a die. One knows the possible out comes that is the person may get 1 to 6 but the person will not know which one will materialize. In capital budgeting also a person knows the possible outcomes but don't know which outcome will occur due to randomness and uncertainty. In such situation Monte Carlo Simulation allows a person to model all potential combination of scenarios for the uncertain variables. After specification of the uncertain variables characteristics the Monte Carlo Simulation generates random numbers, which reflects the chosen characteristics. In Monte Carlo Simulations the uncertain variable characteristics are defined in terms of probability distribution such as normal distribution, uniform distribution or triangular distribution depending on the nature of variable concerned. They may look like the diagrams below.
Once the distribution characteristics are defined in terms of probability distribution then the Monte Carlo Simulation produces in iteration random values for each variable. It also produces thousands of interaction with different combinations of scenarios. As well, the simulation calculates the corresponding outcome. After all iterations are completed the simulation produces the probability distribution of outcomes and depicts in a graphical manner.
For example in a capital budgeting analysis the net present value of the project after simulation using the Mote Carlo method will reveal graphically there is only 70% chance
of getting a positive net present value and 30% chance of getting a negative net present value. That is using th Monte Carlo method it not only give a proactive decision maker the vital information of the possibility of getting the results as a positive net present value in the future. Say if the company only chooses projects if they have a 90 % chance of getting a net present value then this project will be rejected. However, based on other methods it will be accepted. The results can be depicted as follows in a graphical manner as follows.
Conclusions
The above discussion highlights the usefulness of Monte Carlo Simulation in Financial management as opposed to other analytical methods or deterministic methods in some situations where the model is very complex by nature. It also can highlight in a model what constrains are present and the possible obstacles and try to develop strategies to overcome such issues in a cost effective manner. In this respect Monte Carlo Simulation if it can be completed within the reasonable time and cost effective compared to other methods it is valuable tool to analyze business issues and other operational issues in Industry and other fields of study such as Science and in Social sciences particularly in economics and management.
