Stock Price Forecasting and Hypothesis Testing Using Neural Networks

20 Pages Posted: 5 Jun 2020

See all articles by Kerda Varaku

Kerda Varaku

Rice University - Department of Economics

Date Written: May 10, 2020

Abstract

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

Suggested Citation

Varaku, Kerda, Stock Price Forecasting and Hypothesis Testing Using Neural Networks (May 10, 2020). Available at SSRN: https://ssrn.com/abstract=3597684 or http://dx.doi.org/10.2139/ssrn.3597684

Kerda Varaku (Contact Author)

Rice University - Department of Economics ( email )

6100 South Main Street
Houston, TX 77005
United States

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