Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Balanced Scorecard: fatores relevantes de sua implantação na diretoria de mercado de capitais do Banco do Brasil

Este trabalho objetiva determinar que características do corpo funcional da DIMEC (Diretoria de Mercado de Capitais do Banco do Brasil) estão associadas aos principais entraves considerados relevantes pelos funcionários da área durante a implementação do Balanced Scorecard (BSC). Pretende-se salientar como entraves tais como a falta de apoio de Tecnologia de Informação e a falta de hábito dos funcionários em lidar com indicadores não financeiros podem se relacionar com as características de interesse do corpo funcional, tais como nível hierárquico , formação acadêmica e nível de conhecimento da ferramenta BSC , de forma a influenciar na implementação do modelo. Para tanto foi realizada revisão de literatura e estudo de caso no qual figuraram, como instrumentos de coleta de dados, a aplicação de questionários e a entrevista não-estruturada através dos quais funcionários da DIMEC demonstraram as relações entre as variáveis descritas de forma a destacar fatores de relevância para o desenrolar do processo de implantação da ferramenta BSC na referida empresa

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Determinantes da Estrutura de Capital de Companhias Abertas Brasileiras Não-Financeiras: Um Estudo Empírico com Dados em Painel

When a company desires to invest in a project, it must obtain resources needed to make the investment. The alternatives are using firm s internal resources or obtain external resources through contracts of debt and issuance of shares. Decisions involving the composition of internal resources, debt and shares in the total resources used to finance the activities of a company related to the choice of its capital structure. Although there are studies in the area of finance on the debt determinants of firms, the issue of capital structure is still controversial. This work sought to identify the predominant factors that determine the capital structure of Brazilian share capital, non-financial firms. This work was used a quantitative approach, with application of the statistical technique of multiple linear regression on data in panel. Estimates were made by the method of ordinary least squares with model of fixed effects. About 116 companies were selected to participate in this research. The period considered is from 2003 to 2007. The variables and hypotheses tested in this study were built based on theories of capital structure and in empirical researches. Results indicate that the variables, such as risk, size, and composition of assets and firms growth influence their indebtedness. The profitability variable was not relevant to the composition of indebtedness of the companies analyzed. However, analyzing only the long-term debt, comes to the conclusion that the relevant variables are the size of firms and, especially, the composition of its assets (tangibility).This sense, the smaller the size of the undertaking or the greater the representation of fixed assets in total assets, the greater its propensity to long-term debt. Furthermore, this research could not identify a predominant theory to explain the capital structure of Brazilian

Prevalência e fatores associados ao Near Miss Materno: inquérito populacional em uma capital do Nordeste Brasileiro

The scope of this study was to determine the prevalence of near misses and complications during pregnancy and the puerperal period, identifying the main clinical and intervention markers and socioeconomic and demographic factors associated with near misses. It involved a cross-sectional, population-based and probabilistic study with multi-stage complex sampling design conducted in Natal, State of Rio Grande do Norte, Brazil. A validated questionnaire was given to 848 women aged 15 to 49 identified in 8,227 households in 60 census sectors. In theanalysis of associations, the Chi-square test applied and calculated the prevalence ratio (PR) with Confidence Interval (CI) of 95% and 5% significance. The prevalence of maternal near misses was 41.1/1000LB, with hospitalization in an Intensive Care Unit (19.1/1000LB) and eclampsia (13.5/1000LB) being the most important markers. The prevalence of complications during pregnancy and the puerperal period was 21.2%. The highest prevalence of near misses was observed in older women, of black/brown race and low socioeconomic status. Conducting population surveys is feasible and may add important information to the study of near misses and the markers highlight the need for enhancing maternal care to reduce health inequality.

Prevalência e fatores associados ao Near Miss Materno: inquérito populacional em uma capital do Nordeste Brasileiro

The scope of this study was to determine the prevalence of near misses and complications during pregnancy and the puerperal period, identifying the main clinical and intervention markers and socioeconomic and demographic factors associated with near misses. It involved a cross-sectional, population-based and probabilistic study with multi-stage complex sampling design conducted in Natal, State of Rio Grande do Norte, Brazil. A validated questionnaire was given to 848 women aged 15 to 49 identified in 8,227 households in 60 census sectors. In theanalysis of associations, the Chi-square test applied and calculated the prevalence ratio (PR) with Confidence Interval (CI) of 95% and 5% significance. The prevalence of maternal near misses was 41.1/1000LB, with hospitalization in an Intensive Care Unit (19.1/1000LB) and eclampsia (13.5/1000LB) being the most important markers. The prevalence of complications during pregnancy and the puerperal period was 21.2%. The highest prevalence of near misses was observed in older women, of black/brown race and low socioeconomic status. Conducting population surveys is feasible and may add important information to the study of near misses and the markers highlight the need for enhancing maternal care to reduce health inequality.

Determinantes da Estrutura de Capital de Companhias Abertas Brasileiras Não-Financeiras: Um Estudo Empírico com Dados em Painel

When a company desires to invest in a project, it must obtain resources needed to make the investment. The alternatives are using firm s internal resources or obtain external resources through contracts of debt and issuance of shares. Decisions involving the composition of internal resources, debt and shares in the total resources used to finance the activities of a company related to the choice of its capital structure. Although there are studies in the area of finance on the debt determinants of firms, the issue of capital structure is still controversial. This work sought to identify the predominant factors that determine the capital structure of Brazilian share capital, non-financial firms. This work was used a quantitative approach, with application of the statistical technique of multiple linear regression on data in panel. Estimates were made by the method of ordinary least squares with model of fixed effects. About 116 companies were selected to participate in this research. The period considered is from 2003 to 2007. The variables and hypotheses tested in this study were built based on theories of capital structure and in empirical researches. Results indicate that the variables, such as risk, size, and composition of assets and firms growth influence their indebtedness. The profitability variable was not relevant to the composition of indebtedness of the companies analyzed. However, analyzing only the long-term debt, comes to the conclusion that the relevant variables are the size of firms and, especially, the composition of its assets (tangibility).This sense, the smaller the size of the undertaking or the greater the representation of fixed assets in total assets, the greater its propensity to long-term debt. Furthermore, this research could not identify a predominant theory to explain the capital structure of Brazilian