The DCF is a common valuation tool that estimates the intrinsic value of companies based on future cash flows, discounted for the time value of money and risk. However, its deterministic application relies on single-point estimates. The DCF is based on numerous forecast assumptions and constant parameters across all projected years. Consequently, this approach may be deemed somewhat unrealistic and limited in its scope. This limitation is addressed by integrating MCS, which allows for generating multiple scenarios and probability distributions of potential outcomes.

Building on these concepts and further highlighting the benefits of extending DCF with MCS, this thesis adopted a case study approach, focusing on valuing a long-standing Swiss industrial group. The methodology involved historical review and future projection, relying on historical data of the last decade. The R programming language was employed for valuation and simulation, as well as the development of the application. The DCF model was built in Excel, replicated and validated in R, and enhanced with MCS. The R Shiny app facilitated user interaction and scenario exploration.

The comparison of share price calculation under both methods reveals that while the deterministic DCF method yields a share price nearly identical to the mean of the output range generated by the simulation, the MCS provides additional valuable information, such as confidence intervals and probabilities of the estimated equity value. This underscores the importance of the MCS extension in the valuation process, offering profound insights into potential value outcomes. By integrating MCS and generating thousands of variations of key drivers, this approach resulted in distributions of potential DCF outcomes, providing a more comprehensive and insightful analysis and increasing confidence in the valuation process. This combination is invaluable in addressing uncertainties in input variables and future cash flows and in highlighting potential risks and opportunities. This extended analysis enabled a better assessment of the Group's equity value and corresponding share prices. The co-developed Shiny prototype application facilitated the entire simulation process by providing summary tables, probabilities, confidence intervals and insightful visualizations of the outcome distributions.