Recent a few years have witnessed the rapid expansion of the peer-to-peer lending marketplace. As a new field of investment and a novel channel of financing, it has drawn extensive attention throughout the world. Many investors have shown great enthusiasm for this field. However, investors are at the disadvantage of information asymmetry, which is a key issue in this marketplace that is unavoidable and can lead to moral hazard or adverse selection. In this paper, we propose an L 1 / 2 -regularized weighted logistic regression model for default prediction of peer-to-peer lending loans from investors’ perspective, which can reduce the impact of information asymmetry in the process of loan decision. Rather than solely focus on the accuracy of the prediction, we take into consideration the different risk preferences of different investors. We try to find a trade-off between the risk of losing principal and that of losing potential investment opportunities on the basis of investors’ risk preferences. Meanwhile, due to the nature of peer-to-peer lending loans, we add an L 1 / 2 -regularization term to reduce the chance of overfitting. Xu’s algorithm for L 1 / 2 -regularization problems is applied to solve our model. We perform training, in-sample test, and out-of-sample test with data from LendingClub. Numerical experiments demonstrate that regularization could enhance out-of-sample the area under the Precision–Recall curve (AUPRC). By applying the proposed model, the risk-averse investors could apply a higher penalty factor to lower the risk of losing principal at the cost of the loss of some potential investment opportunities according to their own risk preferences. This model can help investors reduce the impact of information asymmetry to a great extent.

中文翻译：

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从投资者角度看P2P借贷违约预测中的信息不对称

最近几年见证了点对点借贷市场的快速扩张。作为一个新的投资领域和新的融资渠道，它受到了全世界的广泛关注。许多投资者对这个领域表现出了极大的热情。然而，投资者处于信息不对称的劣势，这是这个市场中不可避免的关键问题，可能导致道德风险或逆向选择。在本文中，我们从投资者的角度提出了一种L 1 / 2 -正则化加权逻辑回归模型用于P2P借贷贷款的违约预测，可以减少贷款决策过程中信息不对称的影响。我们不仅关注预测的准确性，还考虑了不同投资者的不同风险偏好。我们试图在投资者风险偏好的基础上，在损失本金的风险和失去潜在投资机会的风险之间找到一种权衡。同时，由于P2P借贷的性质，我们增加了一个L 1 / 2 -正则化项以减少过拟合的机会。应用 L 1 / 2 正则化问题的 Xu 算法来解决我们的模型。我们使用来自 LendingClub 的数据进行训练、样本内测试和样本外测试。数值实验表明，正则化可以增强 Precision-Recall 曲线（AUPRC）下的样本外区域。通过应用所提出的模型，风险厌恶投资者可以根据自己的风险偏好，以损失一些潜在投资机会为代价，应用更高的惩罚因子来降低损失本金的风险。