IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0286685.html
   My bibliography  Save this article

Early warning model and prevention of regional financial risk integrated into legal system

Author

Listed:
  • Yanyu Zhuang
  • Hua Wei

Abstract

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.

Suggested Citation

  • Yanyu Zhuang & Hua Wei, 2023. "Early warning model and prevention of regional financial risk integrated into legal system," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-19, June.
  • Handle: RePEc:plo:pone00:0286685
    DOI: 10.1371/journal.pone.0286685
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286685
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0286685&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0286685?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    2. Duprey, Thibaut & Klaus, Benjamin, 2022. "Early warning or too late? A (pseudo-)real-time identification of leading indicators of financial stress," Journal of Banking & Finance, Elsevier, vol. 138(C).
    3. Zhou, Wenwen & Chen, Mengyao & Yang, Zaoli & Song, Xiaobo, 2021. "Real estate risk measurement and early warning based on PSO-SVM," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    4. Wang Ping & Feng Wang & Aihua Wang & Yuncheng Huang, 2021. "Risk Early Warning Research on China’s Futures Company," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(8), pages 2259-2270, June.
    5. Yunxian Chen, 2021. "The Prevention and Diffusion of Systematic or Regional Financial Risks—The Methodology for China to Resolve Financial Crises," Springer Books, in: National Finance, chapter 7, pages 217-264, Springer.
    6. Sun, Yongping & Yang, Ying & Huang, Nan & Zou, Xin, 2020. "The impacts of climate change risks on financial performance of mining industry: Evidence from listed companies in China," Resources Policy, Elsevier, vol. 69(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    2. Yang, Xite & Zhang, Qin & Liu, Haiyue & Liu, Zihan & Tao, Qiufan & Lai, Yongzeng & Huang, Linya, 2024. "Economic policy uncertainty, macroeconomic shocks, and systemic risk: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    3. Sui, Jianli & Lv, Wenqiang & Gao, Xiang & Koedijk, Kees G., 2024. "China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects," Journal of International Money and Finance, Elsevier, vol. 147(C).
    4. Jorge M. Uribe, 2023. ""Fiscal crises and climate change"," IREA Working Papers 202303, University of Barcelona, Research Institute of Applied Economics, revised Feb 2023.
    5. Dutta, Anupam & Bouri, Elie & Noor, Md Hasib, 2021. "Climate bond, stock, gold, and oil markets: Dynamic correlations and hedging analyses during the COVID-19 outbreak," Resources Policy, Elsevier, vol. 74(C).
    6. Salamatu J. Tannor & Christian Borgemeister & Shalom D. Addo–Danso & Klaus Greve & Bernhard Tischbein, 2023. "Climate variability and mining sustainability: exploring operations’ perspectives on local effects and the willingness to adapt in Ghana," SN Business & Economics, Springer, vol. 3(8), pages 1-26, August.
    7. Yuanying Chi & Mingjian Yan & Yuexia Pang & Hongbo Lei, 2022. "Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    8. Tihana Skrinjaric, 2023. "Leading indicators of financial stress in Croatia: a regime switching approach," Public Sector Economics, Institute of Public Finance, vol. 47(2), pages 205-232.
    9. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    10. Xiaoyan Qian & Helen Huifen Cai & Nisreen Innab & Danni Wang & Tiziana Ciano & Ali Ahmadian, 2025. "A novel deep learning approach to enhance creditworthiness evaluation and ethical lending practices in the economy," Annals of Operations Research, Springer, vol. 346(2), pages 1597-1619, March.
    11. Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    12. Kim Long Tran & Hoang Anh Le & Cap Phu Lieu & Duc Trung Nguyen, 2023. "Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam," IJFS, MDPI, vol. 11(4), pages 1-18, November.
    13. An, Hui & Wang, Hao & Delpachitra, Sarath & Cottrell, Simon & Yu, Xiao, 2022. "Early warning system for risk of external liquidity shock in BRICS countries," Emerging Markets Review, Elsevier, vol. 51(PA).
    14. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
    15. Tianbao Zhou & Zhixin Liu & Yingying Xu, 2024. "How do financial variables impact public debt growth in China? An empirical study based on Markov regime-switching model," Papers 2407.02183, arXiv.org.
    16. Fan, Wenna & Wang, Feng & Zhang, Hao & Yan, Bin & Ling, Rui & Jiang, Hongfei, 2024. "Is climate change fueling commercial banks’ non-performing loan ratio? Empirical evidence from 31 provinces in China," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    17. Xu, Weidong & Huang, Wenxuan & Li, Donghui, 2024. "Climate risk and investment efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    18. Xiaojun Chu & Nianrong Sui, 2023. "Does Weather-Related Disaster Affect the Financing Costs of Enterprises? Evidence from Chinese Listed Companies in the Mining Industry," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    19. Pinto-Gutiérrez, Cristian A., 2023. "Drought risk and the cost of debt in the mining industry," Resources Policy, Elsevier, vol. 83(C).
    20. Mikhail Stolbov & Maria Shchepeleva, 2023. "Sentiment-based indicators of real estate market stress and systemic risk: international evidence," Annals of Finance, Springer, vol. 19(3), pages 355-382, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0286685. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.