High performance prediction of stock returns with VG-RAM weightless neural networks

Abstract

This work presents a new weightless neural network-based time series predictor that uses Virtual Generalized Random Access Memory weightless neural network to predict future stock returns. This new predictor was evaluated in predicting future weekly returns of 46 stocks from the Brazilian stock market. Our results showed that Virtual Generalized Random Access Memory weightless neural network 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.