Das, S.R.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction. Algorithms2018, 11, 138.
Das, S.R.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction. Algorithms 2018, 11, 138.
Das, S.R.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction. Algorithms2018, 11, 138.
Das, S.R.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction. Algorithms 2018, 11, 138.
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. 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&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.
Keywords
deep neural nets; market efficiency; market prediction
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
