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A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps

Author

Listed:
  • Ligang Zhou

    (Macau University of Science and Technology)

  • Chao Ma

    (Macau University of Science and Technology)

Abstract

This study investigates different investment rules on loans in peer-to-peer lending, such as default probability (DP), credit grade from the platform, internal rate of return (IRR), net present value (NPV), and Sharpe ratio (SR). We use classical models and gradient boosting models (GBM) to evaluate loans in a practical setting by considering two timestamps associated with each observation in the data set collected from a P2P platform. The empirical study demonstrates the realistic performance of different investment rules. Furthermore, some investment decisions based on IRR, NPV, and SR can outperform those based on DP and credit grade from the platform, which may provide a P2P lending platform with an impetus for deploying decision support systems to help investors improve investment performance.

Suggested Citation

  • Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:4:d:10.1007_s10614-022-10308-9
    DOI: 10.1007/s10614-022-10308-9
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    References listed on IDEAS

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