Preditor de alto desempenho para retornos de ações baseado em redes neurais sem peso - Almeida, André Gustavo Coelho de (2011)
This work presents a new weightless neural network-based time series predictor that uses Virtual Generalized Random Access Memory weightless neural network, which does not store knowledge in their connections but in Random Access Memories (RAM) inside the network’s nodes, or neurons. This new predictor was evaluated in predicting future weekly returns of 46 stocks from the Brazilian stock market and compared with neural autoregressive predictors based on feedfoward neural networks trained with the backpropagation algorithm. Our results showed that weightless neural network-based predictors can produce predictions of future stock returns with the same error levels and properties of baseline autoregressive neural network predictors, however, running 5.000 times faster.