Monte Carlo Simulations for Resolving Verifiability Paradoxes in Forecast Risk Management and Corporate Treasury Applications

Forecast risk management is central to the financial management process. The study aims to apply Monte Carlo simulation to solve three classic probabilistic paradoxes and their implementation in corporate financial management. The article presents Monte Carlo simulation as an advanced tool for risk management in financial management processes. This method allows for a comprehensive risk analysis of financial forecasts, making it possible to assess potential errors in cash flow forecasts and predict the value of corporate treasury growth under various future scenarios. In the investment decision-making process, Monte Carlo simulation supports the evaluation of the effectiveness of financial projects by calculating the expected net value and identifying the risks associated with investments, allowing more informed decisions to be made on project implementation. The method is used in reducing cash flow volatility, which contributes to lowering the cost of capital and increasing the value of a company. Simulation also enables more accurate liquidity planning, including forecasting cash availability and determining ap-propriate financial reserves based on probability distributions. Monte Carlo also supports the management of credit and interest rate risk, enabling simulation of the impact of various economic scenarios on a company's financial obligations. In the context of strategic planning, the method is an extension of decision tree analysis, where subsequent decisions are made based on the results of earlier ones. Creating probabilistic models based on Monte Carlo simulations makes it possible to take into account random variables and their impact on key financial management indicators, such as free cash flow (FCF). Compared to traditional methods, Monte Carlo simulation offers a more detailed and precise approach to risk analysis and decision-making, providing companies with vital information for financial management under uncertainty. The article emphasizes that the use of Monte Carlo simulation in financial management not only enhances the effectiveness of risk management, but also supports the long-term growth of corporate value. The entire process of financial management moves into the future and is based on predicting future free cash flows discounted at the cost of capital. We used both numerical and analytical methods to solve verdict-type paradoxes. We used Monte Carlo simulation as the numerical method. The following analytical methods were used: conditional probability, Bayes' rule and Bayes' rule with multiple conditions. We solved all truth-type paradoxes and discovered why the Monty Hall problem was so widely discussed in the 1990s. We differentiated Monty Hall problems using different numbers of doors and prizes.