Classificadores de polaridade de notícias utilizando ferramentas de machine learning : o caso da Vale S.A.

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia de Produção, 2014.A dificuldade de se prever o movimento das ações é objeto de estudo de vários autores. A fim de obter ganhos imediatos, se faz necessário estimar a direção do movimento de curto-prazo para decisão do momento mais apropriado para negociar ações. A proposta desse trabalho consiste em selecionar a ferramenta de machine learning mais adequada para classificar a polaridade notícias da empresa Vale divulgadas para os investidores em geral. Também serão utilizadas ferramentas de Natural Language Processing (NLP) para pré-processar o texto e definir os parâmetros de pré-processamento que geram melhores resultados para o classificador. Para chegar a esta proposta, foi feita uma ampla revisão bibliográfica sobre NPL, Machine Learning, text mining e a influência de fatores macroeconômicos no valor das ações e vice versa. Dessa forma, foi possível selecionar as ferramentas mais adequadas para a realização da segunda etapa do projeto, que consistiu em gerar diversos classificadores e compará-los a fim de identificar os melhores parâmetros para pré-processamento, seleção de atributos e processamento das notícias.The difficulty of predicting the stock prices movement is studied by several authors. In order to obtain immediate gains, it is necessary to predict the direction of the movement of short-term to decide the most appropriate time to trade the stocks. The purpose of this project is to select the most appropriate machine learning tool to classify the news polarity from VALE S.A. available online to the investors. The project used as well, natural language processing tools pro preprocess the text and define the best parameters to preprocessing. To achieve these proposal, an extensive literature review about NPL, Machine Learning, text mining and the macroeconomic factors that influence the stock prices and vice versa. Thus, it was possible to select the most appropriate tools to perform the project, which covered the generation of several classifiers and compare them to identify the best parameters of pre-processing, attribute selection and processing the news

An electronic financial system adviser for investors: the case of Saudi Arabia

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonFinancial markets, particularly capital and stock markets, play an important role in mobilizing and canalising the idle savings of individuals and institutions to the investment options where they are really required for productive purposes. The prediction of stock prices and returns is carried out in order to enhance the quality of investment decisions in stock markets, but it is considered to be tricky and complicates tasks as these prices behave in a random fashion and vary with time. Owing to the potential of returns and inherent risk factors in stock market returns. Various stock market prediction models and decision support systems such as Capital asset pricing model, the arbitrage pricing theory of Ross, the inter-temporal capital asset pricing model of Merton ,Fama and French five-factor model, and zero beta model to provide investors with an optimal forecast of stock prices and returns. In this research thesis, a stock market prediction model consisting of two parts is presented and discussed. The first is the three factors of the Fama and French model (FF) at the micro level to forecast the return of the portfolios on the Saudi Arabian Stock Exchange (SASE) and the second is a Value Based Management (VBM) model of decision-making. The latter is based on the expectations of shareholders and portfolio investors about taking investment decisions, and on the behaviour of stock prices using an accurate modern nonlinear technique in forecasting, known as Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of the Saudi Arabian Stock Exchange from January 2007 to December 2011. The stock returns were predicted using the linear form of asset pricing models (capital asset pricing model as well as Fama and French three factor model). In addition, non-linear models were also estimated by using various artificial neural network techniques, and adaptive neural fuzzy inference systems. Six portfolios of stock predictors are combined using: average, weighted average, and genetic algorithm optimized weighted average. Moreover, value-based management models were applied to the investment decision-making process in combination with stock prediction model results for both the shareholders’ perspective and the share prices’ perspective. The results from this study indicate that the ANN technique can be used to predict stock portfolio returns; the investment decisions and the behaviour of stock prices, optimized by the genetic algorithm weighted average, provided better results in terms of error and prediction accuracy compared to the simple linear form of stock price prediction models. The Fama and French model of stock prediction is better suited to Saudi Arabian Stock Exchange investment activities in comparison to the conventional capital assets pricing model. Moreover, the multi-stage type1 model, which is a combination of Fama and French predicted stock returns and a value-based management model, gives more accurate results for the stock market decision-making process for investment or divestment decisions, as well as for observing variation in and the behaviour of stock prices on the Saudi stock market. Furthermore, the study also designed a graphic user interface in order to simplify the decision-making process based upon Fama and French and value-based management, which might help Saudi investors to make investment decisions quickly and with greater precision. Finally, the study also gives some practical implications for investors and regulators, along with proposing future research in this area