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Algorithms 2018, 11(9), 138; https://doi.org/10.3390/a11090138

Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction

1,†,* , 1,†
and
2,†
1
School of Business, Santa Clara University, Santa Clara, CA 95053, USA
2
School of Engineering, Santa Clara University, Santa Clara, CA 95053, USA
Current address: 500 El Camino Real, Santa Clara, CA 95053, USA.
*
Author to whom correspondence should be addressed.
Received: 30 April 2018 / Revised: 2 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Abstract

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests. View Full-Text
Keywords: deep neural nets; market efficiency; market prediction deep neural nets; market efficiency; market prediction
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Das, S.R.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction. Algorithms 2018, 11, 138.

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