A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Análise crítica do modelo de solvência das empresas de seguros "não vida": a conta "provisão para sinistros"

Neste artigo descreve-se e analisa-se, numa perspectiva crítica, o modelo de solvência actualmente em vigor na União Europeia, particularizando para o caso português. Este modelo, ao basear-se em rácios, apresenta algumas debilidades uma vez que, para modificar o resultado obtido, as empresas de seguros podem baixar o nível de solvência exigida com base na alteração do numerador ou denominador de fracção. Centramo-nos na manipulação do denominador, designadamente através de processos de sub-provisionamento. Esta comunicação discute, do ponto de vista teórico e prático, a utilização deste pressuposto, com base na conta "provisão para sinistros", que pode colocar em risco a principal função das garantias financeiras: a protecção dos tomadores de seguro.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Parallel hyperspectral unmixing method on GPU

Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times.