Skip to main content
SpringerLink
Log in

Stock index prediction based on multi-time scale learning with multi-graph attention networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

We present a stock index prediction model based on a multi-time scale learning (MTSL) and the multi-graph attention network (MGAT) approach. Instead of dealing with individual stock markets, we consider a group of stock markets simultaneously and exploit the effects of their interactions when making predictions. Our model consists of a boosting Hodrick-Prescott (bHP) filter and MGATs, along with MTSL processes. The bHP filter decomposes the stock index series into the slow-varying growth trend and the fast-varying cyclical volatility for training the MGAT to facilitate the learning of multi-time scale data. The MGAT exploits the interplays of stock markets with three different types of graphs: a regionality graph that qualitatively describes the linkages among stock markets within the same region or similar financial systems; a correlativity graph that quantifies the Pearson correlation between stock markets; and a causality graph that measures the convergence cross mapping (CCM) causality between stock markets. Particularly, the last graph is essentially a directed graph, which captures the nonreciprocal relationship between different stock markets. Experimental results on real stock indices reveal the effectiveness and merit of the proposed model in comparison with other models adopted in this paper.

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
Fig. 5

Similar content being viewed by others

References

  1. Xu Y, Yang C, Peng S, Nojima Y (2020) A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. Appl Intell 50(11):3852–3867

    Article  Google Scholar 

  2. Bazrkar MJ, Hosseini S (2022) Predict stock prices using supervised learning algorithms and particle swarm optimization algorithm. Comput Econ:1–22

  3. Zhang J, Li L, Chen W (2021) Predicting stock price using two-stage machine learning techniques. Comput Econ 57(4):1237–1261

    Article  Google Scholar 

  4. Wang X, Li X, Li S (2022) A novel stock indices hybrid forecasting system based on features extraction and multi-objective optimizer. Appl Intell:1–24

  5. Jia L, Li W, Qiao J (2022) An online adjusting RBF neural network for nonlinear system modeling. Appl Intell:1–14

  6. Singh R, Srivastava S (2017) Stock prediction using deep learning. Multimed Tools Appl 76 (18):18569–18584

    Article  Google Scholar 

  7. Wang J, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8–20. https://doi.org/10.1016/j.neunet.2017.03.004

    Article  Google Scholar 

  8. Niu H, Xu K, Wang W (2020) A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network. Appl Intell 50(12):4296–4309

    Article  Google Scholar 

  9. Sitte R, Sitte J (2002) Neural networks approach to the random walk dilemma of financial time series. Appl Intell 16(3):163–171

    Article  MATH  Google Scholar 

  10. Karevan Z, Suykens JA (2020) Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw 125:1–9

    Article  Google Scholar 

  11. Koo E, Kim G (2022) A new neural network approach for predicting the volatility of stock market. Comput Econ:1–15

  12. Khandelwal I, Adhikari R, Verma G (2015) Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Procedia Comput Sci 48:173–179. https://doi.org/10.1016/j.procs.2015.04.167

    Article  Google Scholar 

  13. Chen W, Jiang M, Zhang WG, Chen Z (2021) A novel graph convolutional feature based convolutional neural network for stock trend prediction. Inf Sci 556:67–94

    Article  MathSciNet  Google Scholar 

  14. Cheng D, Yang F, Xiang S, Liu J (2022) Financial time series forecasting with multi-modality graph neural network. Pattern Recogn 121:108218

    Article  Google Scholar 

  15. Phillips PCB, Shi Z (2021) Boosting: why you can use the HP filter. Int Econ Rev 62 (2):521–570

    Article  MathSciNet  MATH  Google Scholar 

  16. Sugihara G, May R, Ye H, Hsieh C-H, Deyle E, Fogarty M, Munch S (2012) Detecting causality in complex ecosystems. Science 338(6106):496–500

    Article  MATH  Google Scholar 

  17. Stolbov M, Shchepeleva M (2020) Systemic risk, economic policy uncertainty and firm bankruptcies: Evidence from multivariate causal inference. Res Int Business Finance 52:101172. https://doi.org/10.1016/j.ribaf.2019.101172

    Article  Google Scholar 

  18. Wu T, Gao X, An S, Liu S (2021) Time-varying pattern causality inference in global stock markets. Int Rev Financial Anal 77:101806. https://doi.org/10.1016/j.irfa.2021.101806

    Article  Google Scholar 

  19. (2020). Yahoo Finance: stock data. [EB/OL]. https://hk.finance.yahoo.com/ Accessed 30 Dec 2020

  20. Hodrick RJ, Prescott EC (1997) Postwar U.S. business cycles: an empirical investigation. J Money Credit Bank 29(1):1–16. https://doi.org/10.2307/2953682

    Article  Google Scholar 

  21. Wang Y, Yang J, Chen Y, De Maeyer P, Li Z, Duan W (2018) Detecting the causal effect of soil moisture on precipitation using convergent cross mapping. Sci Reports 8(1):1–8

    Google Scholar 

  22. Dost F, Maier E (2018) E-commerce effects on energy consumption: a multi-year ecosystem-level assessment. J Ind Ecol 22(4):799–812

    Article  Google Scholar 

  23. Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations (ICLR), pp 1–12

  24. Liu Q, Ghosh S, Li J, Wong L, Ramamohanarao K (2018) Discovering pan-correlation patterns from time course data sets by efficient mining algorithms. Computing 100(4):421– 437

    Article  MathSciNet  MATH  Google Scholar 

  25. Weron R, Zator M (2015) A note on using the Hodrick-Prescott filter in electricity markets. Energy Econ 48:1–6. https://doi.org/10.1016/j.eneco.2014.11.014

    Article  Google Scholar 

  26. Das A (2016) Cyclical behavior analysis of indian market using HP filter and spectral techniques. IUP J Appl Finance 22(2):62–78

    Google Scholar 

  27. Weerakody PB, Wong KW, Wang G, Ela W (2021) A review of irregular time series data handling with gated recurrent neural networks. Neurocomputing 441:161–178

    Article  Google Scholar 

  28. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)

  29. Deng C, Huang Y, Hasan N, Bao Y (2022) Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition. Inf Sci:297–321

  30. Banerjee T, Sinha S, Choudhury P (2022) Long term and short term forecasting of horticultural produce based on the LSTM network model. Appl Intell 52(8):9117–9147

    Article  Google Scholar 

Download references

Acknowledgements

Project supported by the National Natural Science Foundation of China (Grant Nos. 61673027 and 62106047), Beijing Natural Science Foundation (No. 19L2037)) and the Industry-University-Research Innovation Fund for Chinese Universities (No. 2020HYB04001).

Author information

Authors and Affiliations

  1. College of Engineering, Peking University, Beijing, 100871, China

    Yuxia Liu & Tianguang Chu

  2. School of Information Technology & Management, University of International Business & Economics, Beijing, 100029, China

    Qi Zhang

Authors
  1. Yuxia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianguang Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuxia Liu.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Zhang, Q. & Chu, T. Stock index prediction based on multi-time scale learning with multi-graph attention networks. Appl Intell 53, 16263–16274 (2023). https://doi.org/10.1007/s10489-022-04285-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04285-7

Keywords

Search

Navigation