Stock Price Forecasting and Hypothesis Testing Using Neural Networks
20 Pages Posted: 5 Jun 2020
Date Written: May 10, 2020
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.
Keywords: Neural Networks, Hypothesis Testing, Stock Price Forecast
JEL Classification: C45, C12, G14, C53, C58
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