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The lending risk predicting of the folk informal financial organization from big data using the deep learning hybrid model

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  • Shi, Tao
  • Li, Chongyang
  • Wanyan, Hong
  • Xu, Ying
  • Zhang, Wei

Abstract

This article is first to predict and earlier warning folk lending risk used deep learning hybrid model, we find that the LSTM hybrid model has a higher predict accuracy on lending risk forecasting and earlier warning of the FIFO, with an obviously improvement of the average value of forecasting accuracy. The predict accuracy of LSTM-GRU and LSTM-CNN models on lending risk forecasting of the FIFO is higher than others during COVID-19 pandemic. Therefore, we believe that the LSTM hybrid model, especially the LSTM-GRU model can better predict and early warn lending risk of the FIFO on big data.

Suggested Citation

  • Shi, Tao & Li, Chongyang & Wanyan, Hong & Xu, Ying & Zhang, Wei, 2022. "The lending risk predicting of the folk informal financial organization from big data using the deep learning hybrid model," Finance Research Letters, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:finlet:v:50:y:2022:i:c:s1544612322004172
    DOI: 10.1016/j.frl.2022.103212
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    1. Xu, Libo, 2021. "Stock Return and the COVID-19 pandemic: Evidence from Canada and the US," Finance Research Letters, Elsevier, vol. 38(C).
    2. Beutler, Toni & Bichsel, Robert & Bruhin, Adrian & Danton, Jayson, 2020. "The impact of interest rate risk on bank lending," Journal of Banking & Finance, Elsevier, vol. 115(C).
    3. Hughes, Joseph P. & Moon, Choon-Geol, 2022. "How bad is a bad loan? Distinguishing inherent credit risk from inefficient lending (Does the capital market price this difference?)," Journal of Economics and Business, Elsevier, vol. 120(C).
    4. Islam, Asadul & Nguyen, Chau & Smyth, Russell, 2015. "Does microfinance change informal lending in village economies? Evidence from Bangladesh," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 141-156.
    5. Alexandre, Michel & Silva, Thiago Christiano & Connaughton, Colm & Rodrigues, Francisco A., 2021. "The drivers of systemic risk in financial networks: a data-driven machine learning analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    6. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    7. Alberola, Enrique & Urrutia, Carlos, 2020. "Does informality facilitate inflation stability?," Journal of Development Economics, Elsevier, vol. 146(C).
    8. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    9. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    10. Hou, Liming & Hsueh, Shao-Chieh & Zhang, Shuoxun, 2020. "Does formal financial development crowd in informal financing? Evidence from Chinese private enterprises," Economic Modelling, Elsevier, vol. 90(C), pages 288-301.
    11. Boubaker, S. & Liu, Z. & Zhai, L., 2021. "Big data, news diversity and financial market crash," Post-Print hal-03323671, HAL.
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    1. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).

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