IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2024i3p140-160.html
   My bibliography  Save this article

The Essential Role of Big Data: Could it Effectively Mitigate Non-Performing Loans?

Author

Listed:
  • Lianhong QIU

    (School of Marxism, Guangdong Provincial Party School of the CPC)

  • Haidan SU

    (Party School of the Central Committee of the Communist Party of China (National Academy of Governance))

  • Chi-Wei SU

    (Professor, School of Economics, Qingdao University)

  • Meng QIN

    (School of Marxism, Qingdao University)

Abstract

Investigating the role of digital technology in non-performing loans is crucial for China to prevent financial risks effectively. This analysis utilises the full-sample and advanced sub-sample methods, utilising quarterly data from the first quarter of 2010 to the fourth quarter of 2022, to examine the interplay between big data and non-performing loans, exploring whether big data serves as an innovative tool to reduce financial risks in China. The conclusions ascertain positive and adverse impacts exist from the big data index (BDI) to the non-performing loan ratio (NPLR). The negative effects point out that the accelerated development of big data technology promotes the reduction of financial risks and vice versa. However, the positive influence would refute this idea; the leading cause is that the economic situation might influence non-performing loans. Conversely, there is a negative effect of NPLR on BDI, highlighting that low NPLR accompanied by economic recovery might facilitate investors to invest in big data-related stocks. Under the background of the fourth industrial revolution and unstable international financial environment, this discussion would provide significant suggestions for China to mitigate non-performing loans by applying big data technology.

Suggested Citation

  • Lianhong QIU & Haidan SU & Chi-Wei SU & Meng QIN, 2024. "The Essential Role of Big Data: Could it Effectively Mitigate Non-Performing Loans?," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 140-160, October.
    • Handle: RePEc:rjr:romjef:v::y:2024:i:3:p:140-160
    as

    Download full text from publisher

    File URL: https://www.ipe.ro/ftp/RePEc/rjef3_2024/rjef3_2024p140-160.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lee, Chuan-Kai & Yu, Limeng, 2022. "A multi-level perspective on 5G transition: The China case," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Qin, Meng & Zhu, Yujie & Xie, Xin & Shao, Xuefeng & Lobonţ, Oana-Ramona, 2024. "The impact of climate risk on technological progress under the fourth industrial era," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    3. Qin, Meng & Zhang, Xiaojing & Li, Yameng & Badarcea, Roxana Maria, 2023. "Blockchain market and green finance: The enablers of carbon neutrality in China," Energy Economics, Elsevier, vol. 118(C).
    4. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(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. Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).
    2. Cao, Fangzhi & Su, Chi-Wei & Sun, Dian & Qin, Meng & Umar, Muhammad, 2024. "U.S. monetary policy: The pushing hands of crude oil price?," Energy Economics, Elsevier, vol. 134(C).
    3. Wu, Jiawen & Li, Jing-Ping & Su, Chi-Wei, 2024. "Can green bond hedges climate policy uncertainty in the United States: New insights from novel time-varying causality and quantile-on-quantile methods?," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 1158-1176.
    4. Cao, Fangzhi & Su, Chi-Wei & Qin, Meng & Moldovan, Nicoleta-Claudia, 2024. "The investment of renewable energy: Is green bond a safe-haven to hedge U.S. monetary policy uncertainty?," Energy, Elsevier, vol. 307(C).
    5. Su, Chi-Wei & Yang, Shengyao & Dumitrescu Peculea, Adelina & Ioana Biţoiu, Teodora & Qin, Meng, 2024. "Energy imports in turbulent eras: Evidence from China," Energy, Elsevier, vol. 306(C).
    6. Yang, Shengyao & Zhu, Meng Nan & Yu, Haiyan, 2024. "Are artificial intelligence and blockchain the key to unlocking the box of clean energy?," Energy Economics, Elsevier, vol. 134(C).
    7. Zhang, Xiuqi & Meng, Xiangyu & Su, Chi Wei, 2024. "The security of energy import: Do economic policy uncertainty and geopolitical risk really matter?," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 377-388.
    8. Wei Su, Chi & Yue Song, Xin & Qin, Meng & Lobonţ, Oana-Ramona & Umar, Muhammad, 2024. "Optimistic or pessimistic: How do investors impact the green bond market?," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    9. Ankur Tayal & Saurabh Agrawal & Rajan Yadav, 2024. "Implementation of industry 4.0 in construction industry: a review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4163-4182, September.
    10. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    11. Song GAO & Yating ZHU & Adelina DUMITRESCU PECULEA, 2024. "Green finance and innovation input: the promoters of carbon neutrality in China," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(641), W), pages 31-52, Winter.
    12. Zhong, Yufei & Chen, Xuesheng & Wang, Chengfang & Wang, Zhixian & Zhang, Yuchen, 2023. "The hedging performance of green bond markets in China and the U.S.: Novel evidence from cryptocurrency uncertainty," Energy Economics, Elsevier, vol. 128(C).
    13. Jianhua Zhu & Taiwen Feng & Ying Lu & Wenbo Jiang, 2024. "Using blockchain or not? A focal firm's blockchain strategy in the context of carbon emission reduction technology innovation," Business Strategy and the Environment, Wiley Blackwell, vol. 33(4), pages 3505-3531, May.
    14. Liao, Feimei & Hu, Yaoyao & Chen, Mengjie & Xu, Shulin, 2024. "Digital transformation and corporate green supply chain efficiency: Evidence from China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 195-207.
    15. Qin, Meng & Su, Chi-Wei & Lobonţ, Oana-Ramona & Umar, Muhammad, 2023. "Blockchain: A carbon-neutral facilitator or an environmental destroyer?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 604-615.
    16. Agnese, Pablo & Rios, Francisco, 2024. "Spillover effects of energy transition metals in Chile," Energy Economics, Elsevier, vol. 134(C).
    17. Jun Gao & Ning Xu & Ju Zhou, 2023. "Does Digital Transformation Contribute to Corporate Carbon Emissions Reduction? Empirical Evidence from China," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    18. Qin, Meng & Wu, Tong & Ma, Xuecheng & Albu, Lucian Liviu & Umar, Muhammad, 2023. "Are energy consumption and carbon emission caused by Bitcoin? A novel time-varying technique," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 109-120.
    19. Li, Zheng Zheng & Su, Chi-Wei & Moldovan, Nicoleta-Claudia & Umar, Muhammad, 2023. "Energy consumption within policy uncertainty: Considering the climate and economic factors," Renewable Energy, Elsevier, vol. 208(C), pages 567-576.
    20. Ding, Tao & Li, Hao & Liu, Li & Feng, Kui, 2024. "An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence," Energy Economics, Elsevier, vol. 136(C).

    More about this item

    Keywords

    big data; non-performing loans; time-varying; causal relation; China;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    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:rjr:romjef:v::y:2024:i:3:p:140-160. 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: Corina Saman The email address of this maintainer does not seem to be valid anymore. Please ask Corina Saman to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    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.