Explicações para a divulgação voluntária ambiental no Brasil utilizando a análise de regressão em painel

O objetivo no estudo aqui apresentado foi identificar os fatores que determinam a divulgação voluntária ambiental pelas empresas brasileiras potencialmente poluidoras. Para tanto, foram analisa- das as Demonstrações Financeiras Padronizadas (DFPs) e os Relató- rios de Sustentabilidade (RS) do período de 2005 a 2007 das empresas abertas com ações listadas na Bolsa de Valores de São Paulo (Bovespa) e pertencentes a setores de alto impacto ambiental, que compreendem extração e tratamento de minerais; metalúrgico; químico; papel e celulose; indústria de couros e peles; transporte, terminais, depósitos e comércio (de combustíveis, derivados de petróleo e produtos químicos). Com o intuito de explicar a evidenciação ambiental divulgada pelas empresas investigadas, foram formuladas sete hipóteses testadas a partir de instrumentos de análise estatística. Essas hipóteses referem-se a fatores individuais das empresas, que englobam tamanho, rentabilidade, endividamento, empresa de auditoria, sustentabilidade, internacionalização e publicação do RS. Os resultados mostram que, nos três anos analisados, as 57 empresas que compõem a amostra do estudo evidenciaram um total de 6.182 sentenças ambientais, 73% delas divulgadas nos RS e 27% nas DFPs. A análise de regressão em painel demonstrou que as variáveis tamanho da empresa, empresa de auditoria, sustentabilidade e publicação do RS são relevantes a um nível de significância de 5% para a explicação do disclosure voluntário de informações ambientais. Concluiu-se que os achados da pesquisa corroboram a teoria positiva da contabilidade, e parcialmente a teoria da legitimidade.

Are sustainable companies less risky and more profitable?

The purpose of this study was to determine whether there were significant differences in accounting indicators when comparing sustainable enterprises to other similar companies that are not considered as sustainable. The Corporate Sustainability Index of BM (São Paulo Stock, Commodities and Futures Exchange) was the criterion selected to break down the samples into sustainable and non-sustainable enterprises. The accounting indicators were separated into two kinds: risk (dividend payout, percentage growth of assets, financial leverage, current liquidity, asset size, variability of earnings, and accounting beta) and return (ROA, ROE, asset turnover, and net margin). We individually analyzed the companies in the energy sector, followed by those in the banking sector, as well as the entire ISE portfolio as of 2008/2009, including all the sectors. Mann-Whitney tests were performed in order to verify the difference of the means between the groups (ISE and non-ISE). The results, considering the method chosen and the time span covered by the study, indicate that there are no differences between sustainable companies and the others, when they are assessed by the accounting indicators used here.

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.