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

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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.

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Notes

  1. https://github.com/yumoxu/stocknet-dataset

  2. https://github.com/fulifeng/Adv-ALSTM/tree/master/data/kdd17/ourpped

  3. https://www.cis.fordham.edu/wisdm/dataset.php

  4. https://github.com/hennande/Adv-ALSTM

  5. https://github.com/floft/codats

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Acknowledgements

This research was funded by University of Macau (File no. MYRG2019-00136-FST).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, China

    Qi-Qiao He & Yain-Whar Si

  2. Institute of Science and Environment, University of Saint Joseph, Macau, China

    Shirley Weng In Siu

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  1. Qi-Qiao He
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Correspondence to Yain-Whar Si.

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Appendices

Appendix A: ACL Dataset

Table 17 88 company stocks selected from nine sectors
Full size table

Appendix B: KDD Dataset

Table 18 50 company stocks selected from 10 sectors
Full size table

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He, QQ., Siu, S.W.I. & Si, YW. Instance-based deep transfer learning with attention for stock movement prediction. Appl Intell 53, 6887–6908 (2023). https://doi.org/10.1007/s10489-022-03755-2

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