Aplicação do método de Monte Carlo na avaliação de empresas: desenvolvimento de um modelo de simulação em Python

Abstract

Valuation, crucial for investors and managers, highlights the essential role of the Monte Carlo Method (MMC) in enhancing risk analysis. Originating in nuclear physics, MMC employs stochastic sampling to simulate complex scenarios, playing a critical role in evaluating companies in volatile economic environments. This work aims to develop a simulation model in Python using MMC to calculate the Discounted Cash Flow (DCF), using real data from publicly traded companies. Additionally, it explores the theoretical foundations of MMC and analyzes key variables that influence DCF calculations. Implementation was carried out on Google Colab, employing 10, 100, 1000, and 5000 simulations to analyze the results, with a focus on Grendene company data. The results demonstrate the effectiveness of the proposed model, validating it as a valuable tool for strategic decision-making related to company valuation.