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

Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices
Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices
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.
0957-4174
1-44
Orimoloye, Olanrewaju
9e09fc96-21a8-4106-b347-59c390a61f5f
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4
Orimoloye, Olanrewaju
9e09fc96-21a8-4106-b347-59c390a61f5f
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Johnson, Johnnie E.V.
6d9f1a51-38a8-4011-a792-bfc82040fac4

Orimoloye, Olanrewaju, Sung, Ming-Chien, Ma, Tiejun and Johnson, Johnnie E.V. (2019) Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. Expert Systems with Applications, 1-44. (doi:10.1016/j.eswa.2019.112828).

Record type: Article

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.

Text
ESWA-D-19-01131R1_20190719 (005) - Accepted Manuscript
Restricted to Repository staff only until 19 July 2020.
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Accepted/In Press date: 19 July 2019
e-pub ahead of print date: 23 July 2019

Identifiers

Local EPrints ID: 432578
URI: https://eprints.soton.ac.uk/id/eprint/432578
ISSN: 0957-4174
PURE UUID: cab9f9f3-c2d5-485b-9c51-9d97d9506175
ORCID for Ming-Chien Sung: ORCID iD orcid.org/0000-0002-2278-6185

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Date deposited: 19 Jul 2019 10:04
Last modified: 30 Jul 2019 00:35

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