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A Hybrid Deep Learning Approach for Systemic Financial Risk Prediction

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12249))

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Abstract

Systemic financial risk prediction is a complex nonlinear problem and tied tightly to financial stability since the recent global financial crisis. In this paper, we propose the Systemic Financial Risk Indicator (SFRI) and a hybrid deep learning model based on CNN and BiGRU to predict systemic financial risk. Experiments have been carried out over Chinese economic and financial actual data, and the results demonstrate that the proposed model achieves superior performance in feature learning and outperformance with the baseline methods in both single-step and multi-step systemic financial risk prediction.

This work was supported in part by the National Natural Science Foundation of China under Grant 61971382.

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Author information

Authors and Affiliations

  1. School of Information and Engineering, Communication University of China, Beijing, China

    Yue Zhou

  2. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China

    Jinyao Yan

Authors
  1. Yue Zhou
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  2. Jinyao Yan
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Corresponding author

Correspondence to Yue Zhou .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Zhou, Y., Yan, J. (2020). A Hybrid Deep Learning Approach for Systemic Financial Risk Prediction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_62

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  • DOI: https://doi.org/10.1007/978-3-030-58799-4_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58798-7

  • Online ISBN: 978-3-030-58799-4

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