The Normal Distribution Formalization for Investment Economic Project Evaluation Using the Monte Carlo Method

Investment plays a very important role in the economy, ensures its sustainable growth, contributes to the improvement of the living standards of the population. The most common mistake of planning investment projects is the insufficient development of risks that may affect the profitability of projects. The purpose of the paper is the formalizing the normal distribution for investment project evaluation using the Monte Carlo method. Such formalizing should allow to present normal distribution in a form that is understandable for nonspecialists in mathematical statistics. A user can easily calculate the standard deviation value and determine the limits of the confidence interval and the range of deviation from the mean value. Such mistakes can lead to incorrect investment decisions and significant losses. The desire to minimize risk requires developing a risk model. One of the risk assessment tools is the Monte Carlo method, which combines and develops both methods of sensitivity analysis and scenario analysis. In the Monte Carlo method, risk analysis is performed using models of possible outcomes where any factor that is characterized by uncertainty is replaced by a probability distribution. Some types of distributions such as normal distribution is used less frequently, because their use requires special knowledge in the field of mathematics. In this paper, the aim is to formalize the normal distribution for use of non-specialists in mathematical statistics. Object of study is the risk assessment of investment projects. Subject of study is the normal distribution formalization for investment project evaluation. As the result the formulas for investment project variables and the form for normal distribution formalization in MS Excel are proposed. The empirical result is an experiment, which identify a pseudo-random numbers sequence as normally distributed. It facilitates the work of an expert and allows him to use the normal distribution variables correctly.