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

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

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

Fertirrigação e regulador de crescimento na produção de mudas de café em tubete

The use of quality seedlings is important in establishing a productive coffee crop. However, the most widely used method to produce coffee seedlings is time consuming (6-12 months) and lacks new production technologies. This study aimed to assess the use of fertigation and a growth regulator in the production of coffee seedlings, in order to develop a system faster than the conventional method. For that, Topázio coffee cultivar seeds were pre-germinated and planted in tubes flled with substrate (composted pine bark), in a protected nursery. A randomized block design was used, in a 4 x 2 (fertigation levels x the use or not of growth regulator) factorial scheme, with four replications. Daily fertigation positively influenced all the growth variables evaluated. The foliar spraying of the growth regulator had little effect on seedling growth. When compared to the conventional system described in the literature, the coffee seedling production system described here reduced, by around 60 days, the production time and enables a largescale productionA utilização de mudas de boa qualidade é importante para a formação de uma lavoura produtiva de café. Entretanto, o método mais utilizado atualmente para a produção de mudas de cafeeiro é demorado (6-12 meses) e carece de novas tecnologias de produção. Objetivou-se avaliar a utilização de fertirrigação e regulador de crescimento na produção de mudas de cafeeiro, com vistas ao desenvolvimento de um sistema mais rápido que o sistema convencional de produção. Para isso, sementes da cultivar Topázio foram pré-germinadas e plantadas em tubetes com substrato (casca de pinus compostada), em viveiro coberto. Adotou-se o delineamento de blocos ao acaso, em esquema fatorial 4 x 2 (níveis de fertirrigação x utilização ou não de regulador de crescimento), com quatro repetições. A fertirrigação diária influenciou positivamente em todas as variáveis de crescimento analisadas. A pulverização foliar do regulador de crescimento apresentou pouco efeito sobre o crescimento das mudas. Quando comparado ao sistema convencional descrito na literatura, o sistema de produção de mudas de cafeeiro descrito neste trabalho reduziu, em cerca de 60 dias, o tempo de produção e permite a produção em larga escala.info:eu-repo/semantics/publishedVersio