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.

GUI Inspection from Source Code Analysis

Graphical user interfaces (GUIs) are critical components of todays software. Given their increased relevance, correctness and usability of GUIs are becoming essential. This paper describes the latest results in the development of our tool to reverse engineer the GUI layer of interactive computing systems. We use static analysis techniques to generate models of the user interface behaviour from source code. Models help in graphical user interface inspection by allowing designers to concentrate on its more important aspects. One particularly type of model that the tool is able to generate is state machines. The paper shows how graph theory can be useful when applied to these models. A number of metrics and algorithms are used in the analysis of aspects of the user interface's quality. The ultimate goal of the tool is to enable analysis of interactive system through GUIs source code inspection.

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

Profiling consumer behaviour in cross-border tourism destinations

Tourism is a phenomenon that moves millions of people around the world, taking as a major driver of the global economy. Such relevance is reflected in the proliferation of studies in the overall area known as tourism, under various perspectives and backgrounds. In the light of such multitude of insights our study aims at gaining a deeper understanding of customer profiling and behavior in cross-border tourism destinations. Previous studies conducted in such contexts suggest that cross-border regions (CBRs) are an attractive and desirable idea, yet requiring further theoretical and empirical research. The new configuration of many CBRs calls for a debate on issues concerning its development, raising up important dimensions, such as, organization and planning of common tourism destinations. There is still a gap in the understanding of destination management in CBRs and the customer profile and motivations. Overall this research aims at attaining a deeper understanding of the profile and behavior of consumers in tourism settings, addressing the predisposition for the destination. The study addresses the following research question: “What factors influence customer behavior and attitudes in a CBRs tourism destination?” To address our question we will take an interdisciplinary perspective bringing together inputs from marketing, tourism and local economics. When addressing consumer behavior in tourism previous studies considered the following constructs: involvement, place attachment, satisfaction and destination loyalty. In order to establish the causal relationships in our theoretical model, we intend to develop a predominant quantitative design, yet we plan to conduct exploratory interviews. In the analysis and discussion of results, we intend to use Structural Equation Modeling. It will further allow understanding how the constructs in the research model relate to each other in the specified context. Results are also expected to have managerial implications. Consequently our results may assist decision makers in developing their local policies.

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.

The determinants of international equity and bond investment

The purpose of this research is fourfold. First, to investigate whether the determinants of international equity investment differ between investors with different degrees of information, experience and sophistication. For this purpose, the determinants of international equity investment of institutional and noninstitutional investors from 20 OECD countries, in the period 2001-2009, were analysed and compared. The results show that there are significant differences in the determinants of international equity investment between institutional and noninstitutional investors. Particularly, noninstitutional investors exhibit a more pronounced preference for equities of geographical nearby, contiguous and more transparent countries than institutional investors, suggesting that the effect of information costs and familiarity on international equity investment is stronger for less informed, experienced and sophisticated investors. Moreover, the preference for more developed equity markets and the contrarian behaviour are more severe for noninstitutional investors. Hence, the heterogeneity of institutional and noninstitutional investors in international equity investment is not negligible and therefore should be taken into account. Second, to investigate whether the determinants of international bond investment differ between investors with different degrees of information, experience and sophistication. For this purpose, the determinants of international bond investment of institutional and noninstitutional investors from 20 OECD countries, in the period 2001-2009, were analysed and compared. The results show that there are few significant differences in the determinants of international bond investment between institutional and noninstitutional investors. Particularly, the preference for bonds of more transparent countries and the return chasing behaviour are more pronounced for noninstitutional investors, whereas the preference for bonds with lower risk diversification potential is more pronounced for institutional investors. Hence, not only the results for international bond investment do not allow to support (or reject) the argument that information costs and familiarity are more important for less informed, experienced and sophisticated investors, but also they are contrary to the idea that financial variables, namely return and risk diversification, are more important for more informed, experienced and sophisticated investors. Third, to investigate whether the determinants of international equity investment differ from the determinants of international bond investment. For this purpose, the determinants of both international equity and bond investment of institutional and noninstitutional investors from 20 OECD countries, in the period 2001-2009, were analysed and compared. The results show that, although the effect of information costs on international equity investment tends to be stronger than on international bond investment, the differences between assets are not usually statistically significant, especially when the influence of financial variables is taken into account. Hence, it is not possible to conclude that international equity investment is much more information intensive than international bond investment, as suggested by Gehrig (1993) and Portes, Rey and Oh (2001), among others. Fourth, to investigate whether the flight to quality phenomenon is also observable in international investment and whether the flight to quality phenomenon is more pronounced for more sophisticated than for less sophisticated investors. For this purpose, a two-factor and three-factor ANOVA models, respectively, were applied to the international equity and bond investment of institutional and noninstitutional investors from 20 OECD countries in the period 2001-2009. The results suggest that the flight to quality phenomenon is also observable in international investment, as a change from business cycle of expansion to recession causes investors to significantly decrease the average weight invested in more risky assets (equities) and increase the average weight invested in less risky assets (bonds). The results also show that the variation on the average weight assigned to each type of asset, due to changes in business cycles, is significantly stronger for institutional investors than for noninstitutional investors, thereby suggesting that the flight to quality phenomenon is more pronounced for more sophisticated than for less sophisticated investors.