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.

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.