Análise da relação entre as informações contábeis e o risco sistemático no mercado brasileiro

De acordo com a literatura existente, as informações contábeis representam um importante estimador dos fluxos de caixa futuros da empresa, servindo, portanto, para fins de avaliação do risco de um investimento em ações. Isso porque tais informações refletem a realidade econômico-financeira da empresa em um dado período, possuindo, consequentemente, relação com o risco sistemático de um investimento, o que justifica sua utilização para fins de decisões relacionadas à composição de um portfólio de ações. Dentro desse contexto, o presente trabalho busca apresentar evidências empíricas da relação entre as informações contábeis e o risco sistemático no mercado brasileiro. Mais especificamente, objetiva-se analisar a relação entre os betas contábeis e os betas de mercado de companhias no Brasil. Para isso, foram selecionadas 97 empresas, da Bolsa de Valores, Mercadorias e Futuros de São Paulo (BM&FBOVESPA), de 15 setores econômicos, entre o 1º trimestre de 1995 e o 3º trimestre de 2009. Foram utilizadas 468 variáveis contábeis. Para operacionalizar a relação entre as variáveis foi utilizado um modelo de regressão com dados em painel. Os resultados evidenciaram que alguns betas contábeis podem explicar o beta de mercado e podem fazê-lo de forma antecipada, podendo, ainda, melhorar a previsão do beta de mercado quando associados a betas de mercado históricos. Por outro lado, a maior parte das versões de betas contábeis apresentou relação pouco significativa ou mesmo inexistente.

Design-based random permutation models with auxiliary information

We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.

Impact of the Level of Disclosure of Financial Information on the Pricing of Shares in the Context of Adverse Selection: an experimental research

Managers know more about the performance of the organization than investors, which makes the disclosure of information a possible strategy for competitive differentiation, minimizing adverse selection. This paper's main goal is to analyze whether or not an entity's level of diclosure may affect the risk perception of individuals and the process of evaluating their shares. The survey was carried out in an experimental study with 456 subjects. In a stock market simulation, we investigated the pricing of the stocks of two companies with different levels of information disclosure at four separate stages. The results showed that, when other variables are constant, the level of disclosure of an entity can affect the expectations of individuals and the process of evaluating their shares. A higher level of disclosure by an entity affected the value of its share and the other company's.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.