Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 April 2018 / Approved: 2 May 2018 / Online: 2 May 2018 (08:12:01 CEST)

How to cite: Das, S.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments using Deep Learning for Market Movement Prediction. Preprints 2018, 2018050015 (doi: 10.20944/preprints201805.0015.v1). Das, S.; Mokashi, K.; Culkin, R. Are Markets Truly Efficient? Experiments using Deep Learning for Market Movement Prediction. Preprints 2018, 2018050015 (doi: 10.20944/preprints201805.0015.v1).

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.

Subject Areas

deep neural nets; market efficiency; market prediction

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