Abstract. Introduction<\/i><\/b>. <\/span><\/i>The article focuses on modeling cash flows in investment projects using probabilistic distributions, which is a key aspect of increasing the accuracy of assessments and making well-informed decisions under uncertainty. The author proposes two approaches to cash flow modeling: the first approach involves modeling the cash flow value in a given period, while the second approach focuses on modeling the parameters that influence the cash flow components. Special attention is given to building models based on random variables that reflect the specific characteristics of the project, taking into account unpredictable changes and risks inherent in the economic environment.<\/span><\/i><\/p>\n

Purpose. <\/i><\/b>An important part of the study is the classification of probability distributions, including discrete and continuous, symmetric and asymmetric, and single and multi-parameter models, which are selected depending on the nature of the variables and specific conditions of project implementation. Such classification allows for a more detailed analysis and understanding of possible project development scenarios under uncertainty.<\/span><\/i>
\nThe article emphasizes the importance of accurate estimation of parameters of probability distributions, since the accuracy of modeling results depends on it. Methods such as the method of moments and maximum likelihood are used to determine distribution parameters.<\/span><\/i><\/p>\n

Results.<\/i><\/b> To ensure modeling reliability, it is essential to test the consistency of selected distributions with real data. Visual and statistical tools can be used to assess how well the theoretical distribution matches empirical data, which is a critical step in building a reliable model.<\/span><\/i>
\nThe author suggests using the Monte Carlo method as the primary tool for simulating results, allowing the creation of multiple project development scenarios. This method makes it possible to determine the probability of achieving certain financial results and to assess the sensitivity of the project to changes in key parameters.<\/span><\/i><\/p>\n

Conclusions. <\/i><\/b>The proposed cash flow modeling algorithm contributes to improving the substantiation of investment decisions and optimizes the management of project risks and resources.<\/span><\/i><\/p>\n

Keywords: <\/b>cash flow; model; modeling; probabilistic distribution; random variable; distribution function; probability density function.<\/span><\/i><\/p>\n

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