Aplicação do valor presente ajustado (APV) para avaliação do fluxo de caixa de uma empresa do setor automotivo no sistema inovar auto

The automobile industry shows relevance inside the Brazilian industrial scenario since it contributes with the development of a significant chain of supply, distributors, workshops, publicity agencies and insurance companies in the internal market, aside from being one of the five biggest worldwide market. Thereby, the federal government decreed in Dec, 17th 2012 by Law nº 12.715 the Inovar-Auto Program. As the Adjusted Present Value (APV) is highly recommended, although not yet widespread to public politics of tax reduction, this work intends to apply the APV method on the cash flow analysis of an automobile sector's company, which has recently installed in national territory and wants to rely with governmental incentives proposed by Inovar-Auto Program. The developed work evaluates the company's current cash flow stochastically from mathematical modeling of variables such as price, demand and interest rate through probability distributions with the assist of Crystal Ball software, a Microsoft Excel Add-in, generating different scenarios from Monte Carlo Simulation. As results probabilities situations have been evaluated until the end of the Inovar-Auto's conducted period, in 2017. Beside APV others indicator such as Internal Rate of Return (IRR) and payback period were estimated for the investment project. For APV a sampling distribution with only 0.057% of risk, IRR of 29% were obtained and estimated project payback period was 4.13 years

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model