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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
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    References listed on IDEAS

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    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)

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    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

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