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.

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

A Caregiver Support Platform within the Scope of an Ambient Assisted Living Ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.

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.