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.

Details

Title
Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction
Author
Das, Sanjiv R 1 ; Mokashi, Karthik 1 ; Culkin, Robbie 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 
First page
138
Publication year
2018
Publication date
2018
Publisher
MDPI AG
e-ISSN
19994893
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.3390/a11090138
ProQuest document ID
2582790919
Copyright
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.