IAG Company's Optimal Capital Structure reached through a Multi-objective Genetic Algorithm

Abstract

The thesis consists of three phases. First phase is the theoretical explanation of the two main themes: firstly, the theory of the firm's value maximization through the optimal adjustment of the proportion of its capital structure's components (debt and equity) and, secondly, the Genetic Algorithm (GA) method, which is a trial-and-error stochastic research technique inspired by Charles Darwin’s theory of natural evolution, together with its operators usage, their effect and GA-implementation advantages. Phase two is performing the analysis on the specific case study of International Consolidated Airlines Group (IAG) Company, through the computation of its Weighted Average Cost of Capital (WACC). IAG’s different components of the Cost of Capital, calculated in phase two, are then used as model’s inputs for phase three. Since several capital structure theories have been provided over the years and the results between each other are often controversial, in order to provide a real optimized model, Phase three is bringing a Multi-objective Genetic Algorithm in application, combining two different functions: the minimization of the WACC and the maximization of the Interest Coverage Ratio. This model works on the proper mix of debt and equity such that it is possible to reach the optimal capital structure under specific conditions (considering the profitability maximization from the equity holder’s point of view, meanwhile keeping a proper level of debt repayment ability).