# A Comprehensive Methodology for Investment Project Assessment Based on Monte Carlo Simulation
## Materials and Methods
### Methodology for Investment Assessment  
The proposed methodology provides a structured guidance for a comprehensive assessment of an investment problem. It includes (see Figure 1) quantitative forecasting to obtain input data necessary for the creation of a financial model (to determine the economic efficiency of the investment); Monte Carlo simulations focused on the analysis of investment-related risks; an optimization process aimed at improving the economic efficiency of the investment under limiting conditions; finally, archiving the financial model including results from simulations and optimization of the investment problem. The individual steps of the methodology are defined in more detail in the following text. 1 st step: Forecasting. In many cases, while creating a financial model to evaluate the economic efficiency of the considered investment, it is necessary to predict the main parameters, such as product demand, product prices, product costs, and others, using the appropriate forecasting method. Forecasting methods can be applied via various software programs such as Crystal Ball, @Risk, etc., which include a forecasting module. As a rule, these programs allow forecasting methods such as Non-seasonal Methods, Seasonal Methods, ARIMA, and Multiple Linear Regression. The output of forecasting from time series is a pie chart and forecast values are presented in a table. For forecasting itself, it is not necessary to define the uncertainties for the input variables in the form of distribution functions. However, if the uncertainties are defined, the program produces, in addition to the graph from the forecasting, a histogram and other related outputs described in the section of simulations. Forecast results obtained by selected methods are assessed based on the forecasting error. The forecasting results can be displayed for any method selected in the context menu at the beginning of forecasting. 2 nd step: Monte Carlo simulation. _Creating a financial model._ The starting point is to determine the simulated value, which represents the output financial criterion (for example, Net Present Value) and will be the subject of a risk analysis. A deterministic financial model is established based on input variables of a deterministic nature. In this case, the financial criterion can be calculated deterministically, without uncertainty. If at least one of the input variables is variable or stochastic in nature, the financial criterion can only be determined with a certain probability. Simulations are logically possible only in the case of such variables. For this reason, it is necessary first to create a register of input risk variables and examine their nature. In the identification, it is necessary to go into considerable depth, preferably in such a way that the root risk factors are identified. In the next step, it is necessary to determine the risk criteria. They will be assessed based on their occurrence and severity. That is important if the output quantity depends on many inputs. Risk criteria relate to expressing the probability and impact of risks and how the risks are assessed in terms of severity (or acceptability/unacceptability). In the analysis of risk variables probability of occurrence is mainly assessed. Impacts are either not assessed at this stage or only as a preliminary estimate. The impact analysis itself is performed only on the basis of the simulation results. The analysis result is a register of risks, based on the risk criteria, that have been evaluated as unacceptable or of such a level that it is necessary to deal with them. Subsequently, it is necessary to create a stochastic financial model for the output quantity. The financial model should go into such depth and detail so as to include all input variables that have been identified as significant risk factors. It is necessary to define probability distributions for them.