IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0262539.html
   My bibliography  Save this article

Investor sentiment-aware prediction model for P2P lending indicators based on LSTM

Author

Listed:
  • Yanyan Cui
  • Lixin Liu

Abstract

In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China’s new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China’s online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China’s new financial formats are made.

Suggested Citation

  • Yanyan Cui & Lixin Liu, 2022. "Investor sentiment-aware prediction model for P2P lending indicators based on LSTM," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0262539
    DOI: 10.1371/journal.pone.0262539
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262539
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0262539&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0262539?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Qigui Liu & Luxi Zou & Xiaolin Yang & Jinghua Tang, 2019. "Survival or die: a survival analysis on peer‐to‐peer lending platforms in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(S2), pages 2105-2131, November.
    2. Michal Polena & Tobias Regner, 2018. "Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class," Games, MDPI, vol. 9(4), pages 1-17, October.
    3. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    4. Yeujun Yoon & Yu Li & Yan Feng, 2019. "Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data," Electronic Commerce Research, Springer, vol. 19(1), pages 131-158, March.
    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. Ying Liu & Rui Wang & Jin Qin, 2021. "CEO influence on P2P platform survival: Education and experience do matter!," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(3), pages 622-634, April.
    2. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    3. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    4. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    5. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
    6. Tashreef Muhammad & Tahsin Aziz & Mohammad Shafiul Alam, 2023. "Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange," Papers 2301.04455, arXiv.org.
    7. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    8. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    9. Akash Doshi & Alexander Issa & Puneet Sachdeva & Sina Rafati & Somnath Rakshit, 2020. "Deep Stock Predictions," Papers 2006.04992, arXiv.org.
    10. Andrew Brim & Nicholas S Flann, 2022. "Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
    11. Lu, Haitian & Wang, Bo & Wang, Haizhi & Zhao, Tianyu, 2020. "Does social capital matter for peer-to-peer-lending? Empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    12. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
    13. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
    14. Natnara Chulawate & Supaporn Kiattisin, 2023. "Success Factors Influencing Peer-to-Peer Lending to Support Financial Innovation," Sustainability, MDPI, vol. 15(5), pages 1-16, February.
    15. Na Sun & Liangrong Song & Yan Sun, 2021. "Fuze Effect: A Landmine in the Way of Sustainable Development of FinTech—The Lessons from the Peer-To-Peer Risk Outbreak," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    16. Catalin Stoean & Wiesław Paja & Ruxandra Stoean & Adrian Sandita, 2019. "Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    17. Li, Jianwen, 2023. "MSMEs meet FinTech: Chance or challenge?," Finance Research Letters, Elsevier, vol. 57(C).
    18. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
    19. Yan Wang & Xuelei Sherry Ni, 2020. "Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring," Papers 2009.04536, arXiv.org.
    20. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.

    More about this item

    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:plo:pone00:0262539. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.