[pt] Como os retornos das carteiras formadas por ações de setores econômicos brasileiros são utilizados pelos investidores? As informações contidas nesses retornos são capazes de explicar os movimentos das ações brasileiras? O objetivo do presente trabalho é ajudar a responder a essas perguntas ao pesquisar se os retornos e a volatilidade dos fatores SMB e HML do modelo de três fatores de Fama e French podem ser previstos pelos retornos passados de 16 carteiras formadas por empresas de um mesmo setor econômico listadas na BM&FBOVESPA no período de 1995 a 2012. A análise revela que 14 de 16 setores preveem o retorno do SMB para um mês à frente. Ademais, os retornos de um número significante de setores preveem a volatilidade do SMB e HML para até três meses adiante. Considerando a capacidade explicativa do modelo de Fama e French para o mercado brasileiro, os resultados desta pesquisa indicam que os retornos setoriais brasileiros contêm informações valiosas para os fatores SMB e HML, demonstrando que os investidores não conseguem absorver todas as informações disponíveis em um tempo hábil, fazendo com que estas se difundam gradualmente no mercado. / [en] How are the brazilian industry returns used by investors? Can the information contained in these returns explain the movements of Brazilian shares? The purpose of this work is to help answer these questions by examining whether the returns and the volatility of SMB and HML factors of Fama-French threefactor model can be predicted by past returns of 16 portfolios formed by companies from the same industry listed on São Paulo Stock Exchange (BM&FBOVESPA) between 1995 and 2012. The analysis reveals that 14 of 16 industries predict the SMB returns one month ahead. Furthermore, the returns of a significant number of industries predict the volatility of SMB and HML factors up to three months ahead of time. Considering the explanatory capability of the Fama-French model for the Brazilian market, the results of this research show that Brazilian industry returns contain valuable information for the SMB and HML factors, demonstrating that investors cannot absorb all the information in a timely manner, resulting in their gradual diffusion throughout the market.

Literature reveals studies on mutual fund performance analysis and persistency, with various results. Some studies support hort term performance persistence, while the rest claiming no such persistency among the portfolios. This thesis is an attempt to analyze the performances of Turkish open-end mutual funds for the period of 2003-2010 and search for persistency by extending the time period to June 2011. For performance evaluation, single factor CAPM and ama-French&rsquo / s Three Factor Model are applied. Persistency analysis is done by tracking the relative fund performances on a monthly basis. The results of this study indicate that for the sample period, Turkish A Type mutual funds neither overperform nor underperform the overall market. Nearly all Jensen&rsquo / s alphas are found to be zero, statistically significant. This is also an implication that the mutual funds are earning their expected returns in an efficient mutual fund market in Turkey. The Fama-French&rsquo / s three factor model shows slightly better performance, on the other hand. The size and book to market equity factors are not found significant in general, however they are found jointly significant in all regressions. Persistency is analyzed by tracking the mutual fund erformances on monthly basis. When some mutual funds showed negative or positive performance persistency during the period individually, but the overall picture demonstrates a balanced distribution of performance groups. The number Loser-Loser performances is slightly more than the other three groups, resulting in a tendency for short term negative persistency for the sample analyzed between the period of January 2003 to June 2011.

NY, Columbia Univ., Graduate School of Arts and Sciences, Diss.--New York, 2003. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich. - Enth. 3 Beitr.

Submitted by Yugo Watari (ywatari@gmail.com) on 2017-09-19T16:23:48Z No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) / Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2017-09-19T16:31:51Z (GMT) No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) / Made available in DSpace on 2017-09-19T17:31:18Z (GMT). No. of bitstreams: 1 main.pdf: 2611498 bytes, checksum: 1f50a4c20e7433334a4e2b45acd23424 (MD5) Previous issue date: 2017-08-21 / We approach portfolio construction with risk based allocation, using volatility as the measure of risk, and applying to the stock markets. We start by obtaining generic risk factors based on the approach of Fama&French; and them we decompose the volatility in risk contributions of those generic risk factors. Differing from previous works, instead of allocating in indexes that represent the generic risk factors, we allocate at the asset level, in hopes that this will lead to reproducing the effects of inveting on those indexes, which brings additional complexity to the problem. This was motivated by investors not always having access to invest in theses indexes. Finally, for the purpose of illustration, we apply the metodology to the brazilian stock markets, selecting as risk factors, the five Fama&French risk factors. We obtain portfolios with the desired risk contributions, but as we look in to the weights of each risk factor, there is alocations of weights in the risk factors not related to those of Fama&French, even though the risk contributions are neutralized. We argue that these allocations are preventing from obtaining exposures to the distinct characteristics of each Fama&French risk factor. / A construção de portfólios, ou seja, a definição da composição de uma carteira de ativos, é abordada, nesse trabalho, pela ótica da alocação baseada em contribuições do risco, medida via volatilidade, aplicada a uma carteira de ações. O objetivo é a construção de portfolios, via as contribuições de riscos; para isto construímos fatores de riscos genéricos baseados na abordagem de Fama&French; na sequência aplicamos uma metodologia para distribuir a volatilidade como contribuições de risco destes fatores genéricos. Diferentemente de outros trabalhos, ao invés de alocar em índices que representem estes fatores de riscos genéricos, alocamos diretamente nos ativos na expectativa de conseguirmos reproduzir o efeito de investir nestes índices, o que traz uma complexidade adicional. Esta abordagem foi motivada por nem sempre termos acesso à investir nesses índices. Finalmente, a título de ilustração, a metodologia foi aplicada ao mercado brasileiro de ações, em particular utilizando os fatores do modelo Fama&French de 5 fatores. Obtivemos portfolios com as contribuições de riscos desejadas em relação aos fatores de Fama&French, mas ao se analisar a alocação dos pesos dos fatores de riscos sobre os portfolios obtidos, verificamos que são alocados pesos a fatores que não estão relacionados aos de Fama&French, apesar das contribuições de risco destas estarem neutralizadas. E por fim argumentamos que estas alocações evitam a captura das características distintas de cada fator que gostaríamos de reproduzir.