Análise de algoritmos de machine learning e redes neurais para previsão de preços de ações do IBOVESPA

O foco deste trabalho consiste em utilizar uma variedade de modelos de machine learning e de redes neurais para investigar a conjectura, que encontra respaldo em outras áreas do conhecimento, que no tocante também à previsão de preços de ações é possível encontrar uma especificação ARIMA, portanto mais parcimoniosa, com melhor performance para períodos mais longos. O experimento foca em cinco ações do IBOVESPA (BBAS3, FLRY3, RENT3, VALE3, SLCE3), que representam setores diferentes da economia, durante o período de 3 de março de 2010 a 30 de janeiro de 2022, o que confere robustez ao experimento. De fato, ao buscar compreender quais algoritmos oferecem melhores resultados e com a maior precisão possível, pode-se adiantar que modelos ARIMA específicos foram os que performaram melhor no geral, segundo os critérios RMSE e MAE. Por outro lado, o exercício também inova ao usar modelos de aprendizado de máquina para estimar preços usando preços de dias anteriores ao invés de retornos para o mercado acionário brasileiro. Além disso, propõe um modelo híbrido de rede neural LSTM-GRU para tentar melhorar a previsão dos preços das ações.The focus of this work is to use a variety of machine learning models and neural networks to investigate the conjecture, which finds support in other areas of knowledge, that it is also possible to find an ARIMA specification with regard to stock price forecasting, therefore more parsimonious, with better performance for longer periods. The experiment focuses on five IBOVESPA stocks (BBAS3, FLRY3, RENT3, VALE3, SLCE3), which represent different sectors of the economy, during the period from March 3, 2010 to January 30, 2022, which gives robustness to the experiment. In fact, when trying to understand which algorithms offer the best results and with the greatest possible precision, it can be said that specific ARIMA models performed best in general, according to the RMSE and MAE criteria. On the other hand, the exercise also innovates by using machine learning models to estimate prices using prices from previous days instead of returns for the Brazilian stock market. Furthermore, it proposes a hybrid LSTM-GRU neural network model to try to improve stock price prediction

Influence of topcoat pigment particle size distribution on tail-edge pick resistance in sheet-fed offset printing

This paper describes ground calcium carbonate pigment particle size distribution and its influence on the tail-edge picking of pilot-coated paper as determined in full-scale sheet-fed offset printing. A tailor-made method was developed using a modified printing plate and high-tack inks to assess surface strength in terms of edge picking. In addition to the type, fineness, and particle size distribution of the ground calcium carbonate pigment, we also evaluated the solids content of the coating color, binder level, clay usage, and calendering. The printing test method provided differentiation relative to the investigated parameters, and it was possible to correlate these results with laboratory test data on ink-coating interaction and mercury intrusion porosimetry. Maximizing the solids content of the formulation to some extent compensated for the loss of pick resistance that followed binder reduction. Other laboratory tests showed poor correlation with the observed degree of edge picking.</jats:p