  In this work we use Recurrent Neural Networks and Multilayer Perceptrons to
predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment
with different architectures and compare data normalization techniques. Then,
we leverage those findings to question the efficient-market hypothesis through
a formal statistical test.
