Measuring fund performance using multi-factor models: evidence for the Portuguese market

This paper examines the performance of Portuguese equity funds investing in the domestic and in the European Union market, using several unconditional and conditional multi-factor models. In terms of overall performance, we find that National funds are neutral performers, while European Union funds under-perform the market significantly. These results do not seem to be a consequence of management fees. Overall, our findings are supportive of the robustness of conditional multi-factor models. In fact, Portuguese equity funds seem to be relatively more exposed to smallcaps and more value-oriented. Also, they present strong evidence of time-varying betas and, in the case of the European Union funds, of time-varying alphas too. Finally, in terms of market timing, our tests suggest that mutual fund managers in our sample do not exhibit any market timing abilities. Nevertheless, we find some evidence of timevarying conditional market timing abilities but only at the individual fund level.

Models for the Reverse Engineering of Java/Swing Applications

Abstract. Interest in design and development of graphical user interface (GUIs) is growing in the last few years. However, correctness of GUI's code is essential to the correct execution of the overall software. Models can help in the evaluation of interactive applications by allowing designers to concentrate on its more important aspects. This paper describes our approach to reverse engineering abstract GUI models directly from the Java/Swing code.

Color Models and Illumination Changes

Color model representation allows characterizing in a quantitative manner, any defined color spectrum of visible light, i.e. with a wavelength between 400nm and 700nm. To accomplish that, each model, or color space, is associated with a function that allows mapping the spectral power distribution of the visible electromagnetic radiation, in a space defined by a set of discrete values that quantify the color components composing the model. Some color spaces are sensitive to changes in lighting conditions. Others assure the preservation of certain chromatic features, remaining immune to these changes. Therefore, it becomes necessary to identify the strengths and weaknesses of each model in order to justify the adoption of color spaces in image processing and analysis techniques. This chapter will address the topic of digital imaging, main standards and formats. Next we will set the mathematical model of the image acquisition sensor response, which enables assessment of the various color spaces, with the aim of determining their invariance to illumination changes.

Extracting and verifying coordination models from source code

Current software development relies increasingly on non-trivial coordination logic for com- bining autonomous services often running on di erent platforms. As a rule, however, in typical non-trivial software systems, such a coordination layer is strongly weaved within the application at source code level. Therefore, its precise identi cation becomes a major methodological (and technical) problem which cannot be overestimated along any program understanding or refactoring process. Open access to source code, as granted in OSS certi cation, provides an opportunity for the devel- opment of methods and technologies to extract, from source code, the relevant coordination information. This paper is a step in this direction, combining a number of program analysis techniques to automatically recover coordination information from legacy code. Such information is then expressed as a model in Orc, a general purpose orchestration language

Medição do capital Intelectual – uma meta da Nova Economia

A informação e a comunicação provocaram a abertura a um mercado global, produziram e semearam conhecimento e os mercados evoluíram e incentivaram a competitividade e a inovação. As empresas mais competitivas são as que usam novas tecnologias de informação, as que apostam nos recursos humanos qualificados, as que evoluem na investigação e as que se centram na estratégia e nas ligações estratégicas. Estas empresas debatem-se com um novo activo que adquire cada vez mais importância e relevo no seu dia-a-dia, tornando-se, em algumas delas, o principal factor de produção. Segundo Stewart (1998, p.92) “o capitalismo intelectual necessita desesperadamente de uma linguagem prática, caso contrário cairá num charco de lodo verbal sem sentido“. O objectivo deste trabalho centra-se na análise de alguns dos métodos desenvolvidos para valorizar o activo já consagrado como o que mais contribui para o desenvolvimento empresarial e social: o capital intelectual.

O Conhecimento Organizacional nas empresas da Nova Economia – uma revisão da literatura

O conhecimento está intrisecamente associado à acção, ou seja, a práticas executadas tendo por base informação relevante e contextual. O conhecimento organizacional é obtido pelo desempenho na aplicação de experiências adquiridas, capacidades e habilidades individuais ou colectivas e knowhow técnico que conduz à inovação e à obtenção de rendimentos de forma sustentada. A inovação é um imperativo nas empresas de hoje e, sendo inovação sinónimo de utilidade, então a chave da competitividade das empresas é o conhecimento útil (Odriozola, 2000). O conhecimento é um activo organizacional, pois os processos, rotinas e cultura empresariais têm fixado em si conhecimento. As organizações são o local ou contexto onde se processa a interacção de diversos saberes, devendo as mesmas, incentivar a sua concretização e partilha para que a inovação e competitividade sejam conseguidas. Só assim conseguirão sobreviver numa nova economia onde o principal factor de produção é o conhecimento.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.