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Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market

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  • Xi Yang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-Making, Hefei 230009, China)

  • Wenjuan Fan

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-Making, Hefei 230009, China)

  • Shanlin Yang

    (School of Management, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-Making, Hefei 230009, China)

Abstract

Our study explores the factors influencing investors’ behavior in the peer-to-peer (P2P) lending market in China, and the relationships among them from the perspective of investors. The primary component analysis method was used to divide the P2P lending platforms into five categories. Then, a structural equation model was applied to analyze the interrelationship. Our results show that more exceptional operating ability, profitability, and security of the platform help to improve investor’s investment behavior. Operation ability is the most significant influencing factor, which also influences other factors to different degrees. After the analysis of the results, we found that the security degree of P2P lending platforms in China differs, and the risk due to the lack of bank depository for platforms is the most serious. In terms of the background, investors are less interested in the state-owned platforms compared to the bank- or listed-company-owned platforms, although the background strength of the state-owned platforms is more powerful.

Suggested Citation

  • Xi Yang & Wenjuan Fan & Shanlin Yang, 2020. "Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5345-:d:379216
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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. Qi Wei & Qiang Zhang, 2016. "P2P Lending Risk Contagion Analysis Based on a Complex Network Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, July.
    3. 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.
    4. 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:

    1. Li, ZhouPing & Ge, RuYi & Guo, XiaoShuang & Cai, Lingfei, 2021. "Can individual investors learn from experience in online P2P lending? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. Chengyuan Mao & Lewen Bao & Shengde Yang & Wenjiao Xu & Qin Wang, 2021. "Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain," Sustainability, MDPI, vol. 13(10), pages 1-15, May.

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