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Analysis of the impact of social network financing based on deep learning and long short-term memory

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Abstract

The risk of peer to peer lending (P2P) platform is predicted based on text data on the Internet to avoid the risk of social network financing and improve the security of social network financing. First, the transaction and review text information of a third-party P2P platform are classified for the time series of emotional changes. Second, the Granger causal relation test is used to verify the correlation between the time series of emotional changes and trading volume. Finally, a long short-term memory (LSTM) forecasting model is proposed based on investors’ emotional changes to predict the trading volume of P2P platforms using emotional changes as a reference for social network financing to avoid risks. The results show that the value of Pearson correlation coefficient between the trading volume of P2P platforms and negative emotions is -0.2088, with a P value less than 1%, indicating a correlation between emotional changes and trading volume. The Pearson correlation coefficient between the predicted and actual values is 0.7995, whereas the mean square error is 0.2190 with a fitting degree of 0.6532. This shows that the LSTM forecasting model can accurately predict the trading volume of P2P platforms with good performance in comparison with other forecasting models.

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Data availability

The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The work is supported by Research Fund of ‘the Belt and Road’ National Audit Institute of Nanjing Audit University and Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China (Grant No. 22KJB630009).

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Authors and Affiliations

  1. School of Accounting, Nanjing Audit University, Nanjing, 211815, China

    Yuanjun Zhao

  2. Institute of Intelligent Management Accounting and Internal Control, Nanjing Audit University, Nanjing, 211815, China

    Yuanjun Zhao

  3. Faculty of Business Economics, Shanghai Business School, Shanghai, 200235, China

    Hongxin Yu

  4. Department of Management Birkbeck, University of London, Malet Street, Bloomsbury, London, WC1E 7HX, England

    Chunjia Han

  5. Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan

    Brij B. Gupta

  6. Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India

    Brij B. Gupta

  7. Department of Electrical and Computer Engineering, Lebanese American University, Beirut, 1102, Lebanon

    Brij B. Gupta

  8. Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India

    Brij B. Gupta

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  1. Yuanjun Zhao
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  2. Hongxin Yu
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Correspondence to Hongxin Yu or Brij B. Gupta.

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Zhao, Y., Yu, H., Han, C. et al. Analysis of the impact of social network financing based on deep learning and long short-term memory. Inf Syst E-Bus Manage (2024). https://doi.org/10.1007/s10257-023-00665-9

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