Incorporando incertezas no valuation: uma abordagem com cadeias de Markov na TAESA

Abstract

Valuation is the process of estimating the economic value of a company or asset based on a detailed analysis of its finances, market, growth prospects, and other relevant factors. However, this method alone does not incorporate uncertainties related to the variability of cash flows. Therefore, to practically demonstrate the incorporation of uncertainties into the calculation of a company's value, this study employed the stochastic methodology known as Markov Chains to complement the valuation conducted through the discounted cash flow method. This approach allows modeling the impact of a relevant variable on the company's operations by identifying the states that this variable can assume and the transition probabilities between them. This methodology was applied to Transmissora Aliança de Energia Elétrica S.A. (TAESA), where different states were assumed regarding the company’s availability to transmit electricity: High Availability, Medium Availability, Critical Failure, and Recovery. For each state, an impact on the Variable Portion of the company's Gross Revenue was assumed. The lower the availability of transmission lines, the higher the Variable Portion value and, consequently, the greater the deduction from the company’s Gross Revenue. Following this, scenarios were developed with different transition probabilities between the mentioned states, based on assumed climatic and operational conditions. Three scenarios were created: a Base Scenario, in which the company operates exclusively in High Availability, and two stochastic scenarios varying between the other states (Conservative Scenario and Pessimistic Scenario). The impacts of variations in these transition probabilities were observed in the company's economic values for each scenario: in the Base Scenario, the valuation was R$ 24.6 billion, in the Conservative Scenario, R$ 24.1 billion, and in the Pessimistic Scenario, R$ 23.2 billion. These results demonstrate that Markov Chains can be used to model factors that are not typically incorporated in commonly used valuation methods.