Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2021: Intelligent Computing Theories and Application pp 3–11Cite as

Task-Oriented Snapshot Network Construction of Stock Market

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12838))

Abstract

The stock market is a dynamic and complex system in which individual stocks interact with each other and thus influence the rules of the market as a whole. Moreover, we usually want to know how individual stocks and their interactions affect a particular economic indicator. In this paper, for a task, we use eXtreme Gradient Boosting and SHapley Additive exPlanations to construct snapshot networks of the stock market. The snapshot network gives a quantitative explanation of the target output at each moment in terms of the stocks themselves and their interactions. We take the stocks contained in Dow Jones Industrial Average (DJIA) as an example and DJIA itself as the task to construct the snapshot networks. The experimental results show that the snapshot networks can explain the tasks from three aspects: dynamic evolution, stocks themselves and their interactions.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Caldarelli, G., Chessa, A., Pammolli, F., Gabrielli, A., Puliga, M.: Reconstructing a credit network. Nat. Phys. 9, 125–126 (2013). https://doi.org/10.1038/nphys2580

    Article  Google Scholar 

  2. D’Arcangelis, A.M., Rotundo, G.: Complex Networks in Finance. In: Commendatore, P., Matilla-García, M., Varela, L.M., Cánovas, J.S. (eds.) Complex networks and dynamics. LNEMS, vol. 683, pp. 209–235. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40803-3_9

    Chapter  Google Scholar 

  3. Esmaeilpour Moghadam, H., Mohammadi, T., Feghhi Kashani, M., Shakeri, A.: Complex networks analysis in Iran stock market: the application of centrality. Phys. A Stat. Mech. Appl. 531, 121800 (2019). https://doi.org/10.1016/j.physa.2019.121800

  4. Li, S., Yang, Y., Li, C., Li, L., Gui, X.: Stock correlation analysis based on complex network. In: 2016 6th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 174–177. IEEE (2016). https://doi.org/10.1109/ICEIEC.2016.7589713

  5. Li, X., Wang, Q., Jia, S.: Analysis of topological properties of complex network of Chinese stock based on Copula tail correlation. In: 2017 International Conference on Service Systems and Service Management, pp. 1–6. IEEE (2017). https://doi.org/10.1109/ICSSSM.2017.7996279

  6. Dong, Z., An, H., Liu, S., Li, Z., Yuan, M.: Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation. Int. Rev. Econ. Finance 69, 63–74 (2020). https://doi.org/10.1016/j.iref.2020.04.008

    Article  Google Scholar 

  7. Cao, G., Shi, Y., Li, Q.: Structure characteristics of the international stock market complex network in the perspective of whole and part. Discrete Dyn. Nat. Soc. 2017, 1–11 (2017). https://doi.org/10.1155/2017/9731219

    Article  MathSciNet  MATH  Google Scholar 

  8. Jiang, M., Gao, X., An, H., Li, H., Sun, B.: Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series. Sci. Rep. 7, 10486 (2017). https://doi.org/10.1038/s41598-017-10759-3

    Article  Google Scholar 

  9. Zhou, X., Pan, Z., Hu, G., Tang, S., Zhao, C.: Stock market prediction on high-frequency data using generative adversarial nets. Math. Probl. Eng. 2018, 1–11 (2018). https://doi.org/10.1155/2018/4907423

    Article  Google Scholar 

  10. Mishra, A., Ghorpade, C.: Credit card fraud detection on the skewed data using various classification and ensemble techniques. In: 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–5. IEEE (2018). https://doi.org/10.1109/SCEECS.2018.8546939.

  11. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM, New York (2016). https://doi.org/10.1145/2939672.2939785

  12. Scott, M., Lundberg, L.S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA, pp. 1–10 (2017)

    Google Scholar 

  13. Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020). https://doi.org/10.1038/s42256-019-0138-9

    Article  Google Scholar 

  14. Perkins, A.D., Langston, M.A.: Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC Bioinform. 10, S4 (2009). https://doi.org/10.1186/1471-2105-10-S11-S4

    Article  Google Scholar 

  15. Langer, N., Pedroni, A., Jäncke, L.: The problem of thresholding in small-world network analysis. PLoS ONE 8, e53199 (2013). https://doi.org/10.1371/journal.pone.0053199

  16. van den Elzen, S., Holten, D., Blaas, J., van Wijk, J.J.: Reducing snapshots to points: a visual analytics approach to dynamic network exploration. IEEE Trans. Vis. Comput. Graph. 22, 1 (2016). https://doi.org/10.1109/TVCG.2015.2468078

    Article  Google Scholar 

  17. Sun, J., Yang, Y., Xiong, N.N., Dai, L., Peng, X., Luo, J.: Complex network construction of multivariate time series using information geometry. IEEE Trans. Syst. Man Cybern. Syst. 49, 107–122 (2019). https://doi.org/10.1109/TSMC.2017.2751504

  18. Sun, J., Yang, Y., Liu, Y., Chen, C., Rao, W., Bai, Y.: Univariate time series classification using information geometry. Pattern Recognit. 95, 24–35 (2019). https://doi.org/10.1016/j.patcog.2019.05.040

    Article  Google Scholar 

  19. Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (2005). https://doi.org/10.1007/0-387-28981-X

  20. Zou, Y., Donner, R.V., Marwan, N., Donges, J.F., Kurths, J.: Complex network approaches to nonlinear time series analysis. Phys. Rep. 787, 1–97 (2019). https://doi.org/10.1016/j.physrep.2018.10.005

    Article  MathSciNet  Google Scholar 

  21. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv:1806.01261 (2018)

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China, grant number 62066017.

Author information

Authors and Affiliations

  1. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Jiancheng Sun, Huimin Niu & Si Chen

  2. Glasgow College, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Yunfan Hu

  3. School of Information Management, Jiangxi University of Finance and Economics, Nanchang, 330013, China

    Zhinan Wu

  4. School of Mathematics and Computer Science, Yichun University, Yichun, 336000, China

    Zhinan Wu

Authors
  1. Jiancheng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunfan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhinan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huimin Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Si Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiancheng Sun .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, J., Hu, Y., Wu, Z., Niu, H., Chen, S. (2021). Task-Oriented Snapshot Network Construction of Stock Market. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-84532-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84531-5

  • Online ISBN: 978-3-030-84532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation