Are mutual fund investors in jail?

The absence of investor reaction to the poor performance of mutual funds is a widely reported phenomenon. This paper investigates the role of load costs as an explanation for the phenomenon and concludes that back-end load fees are an obstacle to reaction. We find that investors with a high likelihood of undergoing a liquidity crisis, preferring liquidity in decision making, act contrary to the reaction hypothesis, and investors with broader investment horizons do not react to poor performances due to the fact that they are “imprisoned” by back-end load fees.Mutual Fund, Performance Reaction, Load Costs, Investor Behaviour

Mutual fund flows’ performance reaction: does convexity apply to small markets?

In this paper we study the performance reaction of investors in a small market context. Instead of the asymmetrical investors’ reaction to winners and losers, as usually documented for the US, an absence of risk-adjusted performance reaction was observed. The absence of reaction can be attributed to either lower investor sophistication, conflicts of interests in the context of the Portuguese universal banking industry, or the existence of relevant back-end load cost which prevent investors from reacting. A high persistence of net investment flows was also noted. Our results are consistent with the idea that the financial groups with larger market shares have the capacity “to drive” their customers to funds with larger fees. This practice emerges as a non-transparent means of increasing prices.Mutual Funds, Performance Reaction, Investor Behaviour, Small Markets and Regulation

Knowledge discovery methodology for medical reports

Medical reports contain valuable information, not only for the patient that waits for the results but also the latent knowledge that is possible to extract from them. The recent introduction of standard structured formats like the Digital Imaging and Communications in Medicine Structured Report and the Clinical Document Architecture Health Level Seven provide an efficient generation, distribution, and management mechanism. Also, they provide an intuitive and effective manner of information representation, unlike the traditional plain text format. In this paper we present a knowledge discovery methodology for structured report interchange based on plain text medical reports using YALE, a leading open-source data mining tool and Open-ESB platform that provides conversion, parsing, different protocols and message formats interchange capabilities.Centro de Imagiologia da Trindade (CIT

Computer-aided diagnosis in Brain Computer Tomography screening

Currently, interpretation of medical images is almost exclusively made by specialized physicians. Although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. Assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. From a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using Artificial Intelligence (AI) based applications that will contribute to the reduction and eventually the elimination of perception errors. This article describes how machine learning algorithms can help distinguish normal readings in Brain Computer Tomography (CT) from all its variations. The goal is to have a system that is able to identify abnormal appearing structures making the reading by the radiologist unnecessary for a large proportion of the brain CT scans.(undefined

Case based reasoning versus artificial neural networks in medical diagnosis

Embedding Machine Learning technology into Intelligent Diagnosis Systems adds a new potential to such systems and in particular to the imagiology ones. In our work, this is achieved using the data acquired from MEDsys, a computational environment that supports medical diagnosis systems that use an amalgam of knowledge discovery and data mining techniques, which use the potential of an extension to the language of Logic Programming, with the functionalities of a connectionist approach to problem solving using Artificial Neural Networks. One’s goal aims to conceive an alternative method to detect medical pathologies, as an alternative to the one in use in the actual medical diagnostic system; i.e., Case Based Reasoning versus Artificial Neural Networks. A comparative study of these two approaches to machine learning will be presented, taking into account its applicability in MEDsys

Development Scenarios of Sustainability for Golf: The Algarve Case

Golf is becoming a major industry worldwide. The majority of the Portuguese golf courses are located in the Algarve region. Golf tourism has a great economic impact on the Algarve and is regarded by local tourism developers as a vehicle for tackling the seasonal patterns of “mass tourism”. In consideration of the concerns of those involved in regional tourism and golf, the University of Algarve has developed a prospective study on the sustainability paths of this activity, starting from an integrated analysis of the reference conditions of golf in the Algarve, with respect to environmental, economic and social dimensions. This paper presents the sustainability assessment framework developed in this study and the results from its application to the Algarve’s golf courses through the definition and evaluation of three alternative development scenarios and their associated impacts. The application of economic, social and environmental indicators was a key tool for the construction of the “baseline”, “moderate” and “intensive scenarios”. It was concluded that the development of further golf activity in the Algarve should be framed within high service and environmental quality standards. The sustainability area for golf course development should vary between 29 and 41 gold courses (equivalent of 18 holes).

Forecast in the pharmaceutical area – Statistic models vs deep learning

The main goal of this work was to evaluate the application of statistical and connectionist models for the problem of pharmacy sales forecasting. Since R is one of the most used software environment for statistical computation, we used the functions presented in its forecast package. These functions allowed for the construction of models that were then compared with the models developed using Deep Learning algorithms. The Deep Learning architecture was constructed using Long Short-Term Memory layers. It is very common to use statistical models in time series forecasting, namely the ARIMA model, however, with the arising of Deep Learning models our challenge was to compare the performance of these two approaches applied to pharmacy sales. The experiments studied, showed that for the used dataset, even a quickly developed LSTM model, outperformed the long used R forecasting package ARIMA model. This model will allow the optimization of stock levels, consequently the reduction of stock costs, possibly increase the sales and the optimization of human resources in a pharmacy.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

A multi-agent based medical image multi-display visualization system

The evolution of equipments used in the medical imaging practice, from 3-tesla Magnetic Resonance (MR) units and 64-slice Computer Tomography (CT) systems to the latest generation of hybrid Positron Emission Tomography (PET)/CT technologies is fast producing a volume of images that threatens to overload the capacity of the interpreting radiologists. On the other hand multi-agents systems are being used in a wide variety of research and application fields. Our work concerns the development of a multi-agent system that enables a multi-display medical image diagnostic system. The multi-agent system architecture permits the system to grow (scalable) i.e., the number of displays according to the user’s available resources. There are two immediate benefits of this scalable feature: the possibility to use inexpensive hardware to build a cluster system and the real benefit for physicians is that the visualization area increases allowing for easier and faster navigation. In this way an increase in the display area can help a physician analyse and interpret more information in less tim

Some considerations on the estimation of the value associated to a clinical act

The assignment of a value to any economic system, especially in healthcare management, is the topic of this article. The assignment of a value to a clinical act is a very complex process, as it joins the complexity of estimating value in an economic system with the estimation of the value of well-being. An interdisciplinary approach joining disciplines such as Philosophy, Business, Psychology and Physics is used to analyse the assignment of a value; and it is obtained that it is necessary the integrated use of three concepts; viz., Truth, Good, and Beauty. It is also obtained that the concept of Beauty has the biggest difficulty in being computationally represented, and that to achieve such representation it is necessary the use of Statistical Philosophy, a here-proposed branch of the Philosophy of Information. Moreover, it is obtained that value is made of three types of value; viz., Truth-value, Good-value, and Beauty-value. Finally, it is made an assessment of the difficulty in choosing the appropriate necessary projection of the 3-vector value into a worthiness-scalar, a projection that is necessary because the choice of a best option, e.g. a best clinical act, always requires that the option is quantified by a scalar. (C) 2020 The Authors. Published by Elsevier B.V.NFL thanks Eduarda Sousa for support. Thanks to Sandra Lori for the drawings. All the funding was provided by FCT (Fundacao para a Ciencia e a Tecnologia): NFL was funded by a fellowship of project MEDPERSYST-POCI01-0145-FEDER-016428 and by the INESC-ID multiannual funding from the PIDDAC program (UID/CEC/50021/2020); and the work of both JN and VA has been supported within the project scope of UID/CEC/00319/2020