Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices

Orimoloye, LO, Sung, M-C, Ma, T and Johnson, JEV ORCID: https://orcid.org/0000-0003-3594-4696, 2020. Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. Expert Systems with Applications, 139: 112828. ISSN 0957-4174

Preview

Text 1209592_Johnson.pdf - Post-print Download (1MB) | Preview

Abstract

Many existing learning algorithms suffer from limited architectural depth and the locality of estimators, making it difficult to generalize from the test set and providing inefficient and biased estimators. Deep architectures have been shown to appropriately learn correlation structures in time series data. This paper compares the effectiveness of a deep feedforward Neural Network (DNN) and shallow architectures (e.g., Support Vector Machine (SVM) and one-layer NN) when predicting a broad cross-section of stock price indices in both developed and emerging markets. An extensive evaluation is undertaken, using daily, hourly, minute and tick level data related to thirty-four financial indices from 32 countries across six years. Our evaluation results show a considerable advantage from training deep (cf. shallow) architectures, using a rectifier linear (RELU) activation function, across all thirty-four markets when ‘minute’ data is used. However, the predictive performance of DNN was not significantly better than that of shallower architectures when using tick level data. This result suggests that when training a DNN algorithm, the predictive accuracy peaks, regardless of training size. We also examine which activation function works best for stock price index data. Our results demonstrate that the RELU activation function performs better than TANH across all markets and time horizons when using DNN to predict stock price indices.

Item Type:	Journal article
Publication Title:	Expert Systems with Applications
Creators:	Orimoloye, L.O., Sung, M.-C., Ma, T. and Johnson, J.E.V.
Publisher:	Elsevier BV
Date:	January 2020
Volume:	139
ISSN:	0957-4174
Identifiers:	Number Type 10.1016/j.eswa.2019.112828 DOI 1209592 Other S0957417419305305 Publisher Item Identifier
Divisions:	Schools > Nottingham Business School
Record created by:	Jill Tomkinson
Date Added:	30 Oct 2019 15:20
Last Modified:	31 May 2021 15:18
URI:	https://irep.ntu.ac.uk/id/eprint/38082

Actions (login required)

Edit View

Statistics

Views

Downloads