Fatores determinantes da política de distribuição de dividendos: Evidência empírica para as empresas não financeiras da Euronext Lisbon

Dividends and their distribution decisions, being a component of the compensation of investors are a constant financial worry within companies, thus revealing one of the themes highlighted in the context of the financial literature. Study will address the factors determining the dividend policy practiced by companies listed in the Portuguese stock market. The latter will be 47 non-financial companies listed on the Euronext Lisbon during 2009 until 2011. The two samples that have been investigated include the representative of the majority of non-financial companies listed on Euronext Lisbon and the other financial companies members of the PSI 20. The methodology adopted is one of the ordinary least squares regression and the amount of dividends per share distributed was used in determining the dependent variable. In relation to the independent variables, six explanatory factors were chosen. These include profitability, stability of dividend policy, size, growth, risk and investment opportunities. The conclusion suggests that the most important factors to explain the amount of dividends distributed are profitability and stability of dividend policy. There after, growth and risk factors, as well as factors that explain the amount of dividends distributed are also relevant. The remaining variables obtained were insufficient evidence pointing to a significant effect in explaining the dividend policy of Portuguese companies in the sample. The conclusion also states that differences exist in the importance of the explanatory factors to the amount of dividends distributed between the study samples, given the differentiation of dividend policies, followed by companies from each group analyzed.

Determinantes da politica de dividendos: evidência empírica para as empresas não financeiras cotadas na Euronext Lisbon

The problematic of dividends paid out by firms has deserved the attention of several studies, theoretical and empirical, on corporate finance. This article intends to contribute to the theme by determining the factors that influence a firm’s dividends` policy. In this sense, it investigates the effect of a set of factors on the dividends paid out by issuing non financial firms belonging to Euronext Lisbon. Results suggest the existence of firm specific characteristics influencing its dividends policy. A firm’s Cash-flow and its stocks` market price seem to have a positive impact on the dividends paid out to stockholders. In issuing non financial firms that belong to the PSI 20 Index results additionally show the existence of a negative effect of net profits on dividend’s payment.

Criminalidade Económica e Lavagem de Dinheiro: prevenção pela aprendizagem

1 – Resumo: a criminalidade económica tem uma relação muito próxima com o branqueamento de capitais ou lavagem de dinheiro. O sistema económico capitalista está relacionado de forma próxima com o branqueamento de capitais ou a lavagem de dinheiro. O branqueamento de vantagens – como por exemplo capitais -, é um crime que pode atingir um carácter mundial. Também o financiamento do terrorismo surge aqui com um papel importante. É muito importante punir a ilicitude do crime de branqueamento. Mas mais importante ainda é a prevenção do branqueamento de capitais. Neste sentido, o dever de formação é fundamental. E é um dever fundamental que é importante sobretudo no contexto de determinadas entidades. Entidades financeiras e entidades não financeiras. §1.1 Abstract: economic crime has a close relationship with money laundering. The capitalist economic system is related closely with money laundering. Bleaching of advantages - such as capital - is a crime that can reach a global nature. Also the financing of terrorism comes here with an important role. It is very important to punish the unlawfulness of laundering crime. But more important is the prevention of money laundering. In this sense, the duty of training is critical. It is a fundamental duty that is especially important in the context of certain entities. financial institutions and non-financial entities.

Criminalidade Económica e Lavagem de Dinheiro: prevenção pela aprendizagem

Resumo: a criminalidade económica tem uma relação muito próxima com o branqueamento de capitais ou lavagem de dinheiro. O sistema económico capitalista está relacionado de forma próxima com o branqueamento de capitais ou a lavagem de dinheiro. O branqueamento de vantagens – como por exemplo capitais – é um crime que pode atingir um carácter mundial. Também o financiamento do terrorismo surge aqui com um papel importante. É muito importante punir a ilicitude do crime de branqueamento. Mas mais importante ainda é a prevenção do branqueamento de capitais. Neste sentido, o dever de formação é fundamental. E é um dever fundamental importante sobretudo no contexto de determinadas entidades. Entidades financeiras e entidades não financeiras.§ Abstract: Economic crime has a close relationship with money laundering. The capitalist economic system is related closely with money laundering. Bleaching of advantages – such as capital – is a crime that can reach a global nature. Also the financing of terrorism comes here with an important role. It is very important to punish the unlawfulness of laundering crime. But more important is the prevention of money laundering. In this sense, the duty of training is critical. It is a fundamental duty that is especially important in the context of certain entities. financial institutions and non-financial entities.

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.