Stock Price Forecasting and Hypothesis Testing Using Neural Networks
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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.
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1908.11212
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