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Nonlinear effects of P2P lending on bank loans in a Panel Smooth Transition Regression model

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  • Zhang, Zan
  • Hu, Wenjun
  • Chang, Tsangyao

Abstract

This paper proposes an original framework to determine the impact of Peer to Peer lending on bank loans. Using a Panel Smooth Transition Regression (PSTR) model, the authors, set P2P lending balances as a threshold variable, average P2P lending rate, short-term benchmark lending rate and M2 as control variables, and established province-specific and time-specific coefficients of variables for 8 provinces of China from January 2014 to April 2016. The results show a nonlinear dynamic relationship between P2P lending balances and domestic bank loan balances: there are two threshold values and three regimes. In regime 1 and regime 2, the P2P lending balances are small, P2P lending balances and average P2P lending rate exert a positive impact on domestic bank loan balances, and short-term benchmark lending rate exerts a negative impact on domestic bank loan balances. In regime 3, P2P lending balances are bigger, P2P lending balances and average P2P lending rate exert a negative impact on the domestic bank loan balances, and the short-term benchmark lending rate exerts a positive impact on domestic bank loan balances. M2 has a positive impact on domestic bank loan balances in three regimes. Therefore, the results explained in this paper have important policy implications towards P2P lending in China.

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  • Zhang, Zan & Hu, Wenjun & Chang, Tsangyao, 2019. "Nonlinear effects of P2P lending on bank loans in a Panel Smooth Transition Regression model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 468-473.
  • Handle: RePEc:eee:reveco:v:59:y:2019:i:c:p:468-473
    DOI: 10.1016/j.iref.2018.10.010
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    References listed on IDEAS

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    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    3. Herzenstein, Michal & Dholakia, Utpal M. & Andrews, Rick L., 2011. "Strategic Herding Behavior in Peer-to-Peer Loan Auctions," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 27-36.
    4. Lu, Yunlin & Guo, Haifeng & Kao, Erin H. & Fung, Hung-Gay, 2015. "Shadow banking and firm financing in China," International Review of Economics & Finance, Elsevier, vol. 36(C), pages 40-53.
    5. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    6. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    7. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
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    Cited by:

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    6. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Zhao, Yang & Goodell, John W. & Wang, Yong & Abedin, Mohammad Zoynul, 2023. "Fintech, macroprudential policies and bank risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Qian Wang & Jinbao Yang & Yung‐ho Chiu & Tai‐Yu Lin, 2023. "Cross‐regional comparative study on digital finance and finance efficiency in China: The eastern and non‐eastern areas," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 68-83, January.

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