Uncertainty in Valuation: Expansion of the DCF Model with Monte Carlo Simulations of Enterprise Value and Share Price

Abstract

The objective of this thesis is to incorporate the Monte Carlo simulation method into the discounted cash flow (DCF) model in order to shed light on uncertainty and risk transparency in valuation.
The traditional DCF model is set up by estimating the most likely point estimates as inputs. This approach results in the most likely expected value as an output. However, this approach hides valuable information about risk to the decision maker. Including risk in the discount rate or in the underestimation of cash flows doesn’t provide the decision maker a full picture about the embedded uncertainty of the valuation and its risk. To increase risk transparency in valuations, the Monte Carlo approach is incorporated in the traditional DCF. This thesis assesses how Monte Carlo simulations can provide a more informative and flexible valuation output.
In order to conduct the analysis, this paper is built on the valuation of Vestas Wind Systems A/S (referred to as “Vestas” throughout this thesis) as a case study. The fair share price of Vestas is valued both under the deterministic DCF model and the Monte Carlo simulations method. The employment of two different valuation methods allows for the comparison between the two different approaches and for the discussion of potential benefits delivered by the Monte Carlo approach. The discussion of results revolves around which additional information is revealed by the application of Monte Carlo and how information about uncertainty can help make more informed investment decisions.