Instance-based deep transfer learning with attention for stock movement prediction

Resource type
Authors/contributors
Title
Instance-based deep transfer learning with attention for stock movement prediction
Abstract
Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of stock movement. The success of deep learning models relies on the assumption that massive training data are available. However, this assumption is impractical for stock movement prediction. In stock markets, a large number of stocks do not have enough historical data, especially for the companies which underwent initial public offering in recent years. In these situations, the accuracy of deep learning models to predict the stock movement could be affected. To address this problem, in this paper, we propose novel instance-based deep transfer learning models with attention mechanism. In the experiments, we compare our proposed methods with state-of-the-art prediction models. Experimental results on three public datasets reveal that our proposed methods significantly improve the performance of deep learning models when limited training data are available.
Publication
Applied Intelligence
Date
2022-07-12
Journal Abbr
Appl Intell
Language
en
ISSN
1573-7497
Accessed
9/21/22, 5:33 AM
Library Catalog
Springer Link
Extra
0 citations (Crossref) [2022-09-21]
Citation
He, Q.-Q., Siu, S. W. I., & Si, Y.-W. (2022). Instance-based deep transfer learning with attention for stock movement prediction. Applied Intelligence. https://doi.org/10.1007/s10489-022-03755-2