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- ItemEstágios do ciclo de vida definidos com base nos fluxos de caixa como fator de risco no modelo de precificação de ativos(Universidade Federal do Espírito Santo, 2017-04-27) Faller, Renato Loureiro; Bortolon, Patrícia Maria; https://orcid.org/0000-0001-7965-9577; Cunha, Cláudio Márcio Pereira da; Motoki, Fábio Yoshio SuguriThis study investigates the capacity of firms' life cycle stages to describe the return of stocks, specifically, combined in the three-factor model of Fama and French. The approach proposes the use of a factor constructed from the stages of the life cycle, called MMG - Maturity Minus Growth -, which is the difference between the returns of the portfolios composed of mature firms’ stocks and the returns of the portfolios formed by growth firms’ stocks as an alternative to HML - High Minus Low - due to possible distortions arising from the book-to-market in order to test whether there is gain in the capacity of model to capture the return of the stocks. The sample is composed of non-financial companies listed on the BM&FBOVESPA in the period from 2008 to 2016. For the classification of companies in stages of the life cycle is employed the method of Dickinson (2011), in which combinations of the cash flow signals are used to determine the stage the company is. In determining of the market factor, the Ibovespa is used as benchmark and the T-Bond nominal rate (issued by the Treasury of the United States of America) plus Brazil country risk is used as risk-free rate. Three regression models are estimated: the first is the three-factor model in its traditional form; The second is a four-factor model, in which there is the addition of factor derived from the life cycle stage - called MMG; And the third is the modified three-factor model in which the HML factor is replaced by the factor constructed from the stages of the life cycle. First, the regressions are estimated for the period from 05/2009 to 10/2011, in an in-sample procedure. The results of this approach indicate that the MMG factor is positively related to the returns of the study stocks. Then the out-of-sample analysis is performed for the period from 11/2011 to 04/2016, testing which of the models provides better forecasts for the portfolios. In the comparison of the predictions related to the effective returns, the test of Diebold and Mariano (1995) is used to verify which of the models presents precision in the forecast statistically superior to the others. It is observed that the models 1 and 3 show balanced performance in the predictions of returns. Further, the results, both in the in-sample approach and in the out-of-sample approach, indicate a complementary relationship between HML and MMG factors. That is, the MMG factor worked well for the portfolios where HML did not work, and the opposite also occurred