Modelo preditivo para geração de informações gerenciais para empresas patrocinadoras de entidades fechadas de previdência complementar - Cruz, Vera Lúcia (2023)
The objective of the research was to identify a model, among the existing models, which, using the time series of the sponsoring companies of the Closed Supplementary Pension Entities, generate, with greater precision, the possible future results of the variation between the present value of the defined benefit obligation with the fair value of the plan's assets. Through a quasi-experimental action in sponsoring companies listed in B3, two models were used, the integrated auto-regressive moving average (ARIMA) and the Vector Auto-regression model (ARV). Based on the predictive results of the models, it was possible to identify that the data run by the ARIMA method did not present a good fit and it was possible to conclude that the model created by ARV was more robust in predicting the future situations of the analyzed companies, and that, decisions based on predictive models, pointed to the need to anticipate decisions on the discount rate, the inflation rate, the rate of salary increase and on the guaranteeing assets.