Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. (January 2020)

• Record Type: Journal Article
• Authors: Orimoloye, Larry Olanrewaju; Sung, Ming-Chien; Ma, Tiejun; Johnson, Johnnie E.V.
• Is Part Of: Expert systems with applications. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Financial time series forecasting -- Deep feedforward neural network -- Market efficiency -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.112828
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Deep neural networks (cf. shallower architectures) better predict stock indices. A rectifier linear (cf. tanh) activation function better predicts stock indices. Relative predictive accuracy of deep neural networks peaks with increasing data. 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.