Skip to main content
SpringerLink
Log in

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

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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

References

  1. Ariyo AA, Adewumi AO, Ayo CK (2014) Stock price prediction using the arima model. In: UKSIm-AMSS 16th international conference on computer modelling and simulation, pp 106–112

  2. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8. https://doi.org/10.1016/j.jocs.2010.12.007https://doi.org/10.1016/j.jocs.2010.12.007. https://www.sciencedirect.com/science/article/pii/S187775031100007X

    Article  Google Scholar 

  3. Chen P, Tan Y (2021) Stock market movement prediction by gated hierarchical encoder. In: International conference on swarm intelligence, Springer, pp 511–521

  4. Cruciani F, Sun C, Zhang S, Nugent C, Li C, Song S, Cheng C, Cleland I, Mccullagh P (2019) A public domain dataset for human activity recognition in free-living conditions. In: Smartworld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (smartworld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, pp 166–171

  5. Dau HA, Keogh E, Kamgar K, Yeh CCM, Zhu Y, Gharghabi S, Ratanamahatana CA, Yanping BH, Begum N, Bagnall A, Mueen A, Batista G (2018) Hexagon-ML: The ucr time series classification archive

  6. Day O, Khoshgoftaar T (2017) A survey on heterogeneous transfer learning. J Big Data 4:29

    Article  Google Scholar 

  7. Ding Q, Wu S, Sun H, Guo J, Guo J (2020) Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp 4640–4646

  8. Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA (2018) Transfer learning for time series classification. In: International conference on big data (big data), IEEE, pp 1367–1376

  9. Feng F, Chen H, He X, Ding J, Sun M, Chua TS (2019) Enhancing stock movement prediction with adversarial training. In: Proceedings of the twenty-tighth international joint conference on artificial intelligence, IJCAI-19, International Joint Conferences on Artificial Intelligence organization, pp 5843–5849

  10. Gabriel AS (2012) Evaluating the forecasting performance of garch models. Procedia Soc Behav Sci 62:1006–1010. Evidence from Romania

    Article  Google Scholar 

  11. Glasmachers T (2017) Limits of end-to-end learning. In: Asian Conference on Machine Learning, PMLR, pp 17–32

  12. Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: international conference on acoustics, speech and signal processing, IEEE, pp 6645–6649

  13. He QQ, Pang PCI, Si YW (2019) Transfer learning for financial time series forecasting. In: Nayak AC, Sharma A (eds) PRICAI 2019: Trends in artificial intelligence, pp 24–36. Springer International Publishing, Cham

  14. He QQ, Pang PCI, Si YW (2020) Multi-source transfer learning with ensemble for financial time series forecasting. In: International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE/WIC/ACM, pp 227–233

  15. Hu Z, Liu W, Bian J, Liu X, Liu TY (2018) Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM ’18, Association for computing machinery, New York, NY, USA, pp 261–269

  16. Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1):307–319. Support Vector Machines

    Article  Google Scholar 

  17. Li W, Bao R, Harimoto K, Chen D, Xu J, Su Q (2020) Modeling the stock relation with graph network for overnight stock movement prediction. In: Bessiere C (ed) Proceedings of the twenty-ninth international joint conference on artificial intelligence, IJCAI-20, pp 4541–4547. in FinTech. International Joint Conferences on Artificial Intelligence Organization, Special Track on AI

  18. Liu S, Johns E, Davison AJ (2019) End-to-end multi-task learning with attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1871–1880

  19. Lo AW, MacKinlay AC (1990) When are contrarian profits due to stock market overreaction? Rev Financ Stud 3(2):175–205. http://www.jstor.org/stable/2962020

    Article  Google Scholar 

  20. Mech TS (1993) Portfolio return autocorrelation. J Financ Econ 34(3):307–344

    Article  Google Scholar 

  21. Moon S, Carbonell J (2017) Completely heterogeneous transfer learning with attention - what and what not to transfer. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp 2508–2514. https://doi.org/10.24963/ijcai.2017/349

  22. Nguyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(24):9603–9611

    Article  Google Scholar 

  23. Nguyen TT, Yoon S (2019) A novel approach to short-term stock price movement prediction using transfer learning. Appl Sci 9(22):4745

    Article  Google Scholar 

  24. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. In: Wallach H., Larochelle H., Beygelzimer A., d’Alché-Buc F, fox E, Garnett R (eds) Advances in Neural Information Processing Systems, vol 32. pp 8024–8035. Inc, Curran Associates

  25. Qin Y, Song D, Cheng H, Cheng W, Jiang G, Cottrell GW (2017) A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI’17, AAAI Press, pp 2627–2633

  26. Rosenstein MT, Marx Z, Kaelbling LP, Dietterich TG (2005) To transfer or not to transfer. In: NIPS 2005 workshop on transfer learning, vol 898. pp 1–4

  27. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9

  28. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) Artificial Neural Networks and Machine Learning – ICANN 2018. In: Kůrková V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (eds). Springer International Publishing, Cham, pp 270–279

  29. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, Curran associates inc, pp 6000–6010

  30. Walczak S (2001) An empirical analysis of data requirements for financial forecasting with neural networks. J Manag Inf Syst 17(4):203–222. http://www.jstor.org/stable/40398510

    Article  Google Scholar 

  31. Wilson G, Doppa JR, Cook DJ (2020) Multi-source deep domain adaptation with weak supervision for time-series sensor data. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1768–1778

  32. Wu X, Chen H, Wang J, Troiano L, Loia V, Fujita H (2020) Adaptive stock trading strategies with deep reinforcement learning methods. Inf Sci 538:142–158

    Article  MathSciNet  Google Scholar 

  33. Xu H, Chai L, Luo Z, Li S (2022) Stock movement prediction via gated recurrent unit network based on reinforcement learning with incorporated attention mechanisms. Neurocomputing 467:214–228

    Article  Google Scholar 

  34. Xu Y, Cohen SB (2018) Stock movement prediction from tweets and historical prices. In: Proceedings of the 56th annual meeting of the association for computational linguistics, (vol 1. Long Papers), pp 1970–1979. Association for Computational Linguistics, Melbourne

  35. Ye R, Dai Q (2018) A novel transfer learning framework for time series forecasting. Knowl-Based Syst 156:74–99

    Article  Google Scholar 

  36. Ye R, Dai Q (2021) Implementing transfer learning across different datasets for time series forecasting. Pattern Recogn 109:107617

    Article  Google Scholar 

  37. Yoo J, Soun Y, Park YC, Kang U (2021) Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 2037–2045

  38. Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol 2. NIPS’14, pp 3320–3328, MIT Press, Cambridge, MA, USA

  39. Zhang L, Aggarwal C, Qi GJ (2017) Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2141–2149

Download references

Acknowledgements

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

Author information

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

Authors
  1. Qi-Qiao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shirley Weng In Siu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yain-Whar Si
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yain-Whar Si.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03755-2

Keywords

Search

Navigation