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DATT-NGRU: a novel deep learning model with data augmentation for daily stock indexes prediction

Yuefeng Cen (School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China)
Minglu Wang (School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China)
Gang Cen (School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China)
Yongping Cai (School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China)
Cheng Zhao (School of Economics, Zhejiang University of Technology, Hangzhou, China)
Zhigang Cheng (School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China)

Kybernetes

ISSN: 0368-492X

Article publication date: 11 October 2022

Issue publication date: 2 January 2024

110

Abstract

Purpose

The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns.

Design/methodology/approach

To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States.

Findings

The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction.

Originality/value

A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model.

Keywords

Acknowledgements

The study is funded by the National Natural Science Foundation of China (61902349).

Citation

Cen, Y., Wang, M., Cen, G., Cai, Y., Zhao, C. and Cheng, Z. (2024), "DATT-NGRU: a novel deep learning model with data augmentation for daily stock indexes prediction", Kybernetes, Vol. 53 No. 1, pp. 58-82. https://doi.org/10.1108/K-04-2022-0629

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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