From Static to Dynamic Valution: Unlocking the Potential of Monte Carlo Simulations for Discounted Cash Flow Model

The thesis explores the use of Monte Carlo simulation to improve the reliability of the DCF method, which is commonly used in business valuation. The object of the valuation is SKODA Auto, the largest car manufacturer in the Czech Republic. The theoretical part of the thesis explains the fundamental concepts of valuation and underlines some limitations of the deterministic approach of the DCF methodology. Monte Carlo simulation is introduced as a tool to approach the variability of model parameters and as a potential tool for generating a range of possible outcomes. In the practical part, the classical DCF model of SKODA is explained, and the Monte Carlo simulation is incorporated into the model using R, the statistical programming language. The simulation produces a sampling distribution of enterprise value probabilities, which, in turn, helps to identify the 95% confidence intervals of the final value. Different scenarios assigned varying probabilities were also tested during the Monte Carlo simulation. The analysis of the results contains measures of central tendency, variability, and shape, as well as the Kernel density estimate. In addition, these findings are further assessed through a finance sensitivity analysis, which aims to identify the most critical components of the entire model. Specifically, the sensitivity analysis revealed that the EBITDA margin is the most critical variable. In summary, the Monte Carlo simulation produced a range of Enterprise value between 92 €mm and 487 €mm, with an average value of 297 €mm.