英文标题：
《Risk Prediction of Peer-to-Peer Lending Market by a LSTM Model with
  Macroeconomic Factor》
---
作者：
Yan Wang, Xuelei Sherry Ni
---
最新提交年份：
2020
---
英文摘要：
  In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing the risk through a comprehensive understanding of the P2P market. A low and stable average default rate across all the borrowers denotes a healthy P2P market and provides investors more confidence in a promising investment. Therefore, having a powerful model to describe the trend of the default rate in the P2P market is crucial. Different from previous studies that focus on modeling the default rate at the individual level, in this paper, we are the first to comprehensively explore the monthly trend of the default rate at the aggregative level for the P2P data from October 2007 to January 2016 in the US. We use the long short term memory (LSTM) approach to sequentially predict the default risk of the borrowers in Lending Club, which is the largest P2P lending platform in the US. Although being first applied in modeling the P2P sequential data, the LSTM approach shows its great potential by outperforming traditionally utilized time series models in our experiments. Furthermore, incorporating the macroeconomic feature \\textit{unemp\\_rate} (i.e., unemployment rate) can improve the LSTM performance by decreasing RMSE on both the training and the testing datasets. Our study can broaden the applications of the LSTM algorithm by using it on the sequential P2P data and guide the investors in making investment strategies.
---
中文摘要：
在点对点（P2P）借贷平台中，投资者希望通过全面了解P2P市场，实现回报最大化，同时将风险降至最低。所有借款人的低且稳定的平均违约率意味着一个健康的P2P市场，并为投资者提供了更大的信心。因此，拥有一个强大的模型来描述P2P市场中违约率的趋势至关重要。与以往侧重于在个人层面建模违约率的研究不同，在本文中，我们首次全面探讨了2007年10月至2016年1月美国P2P数据在聚合层面的违约率月度趋势。我们使用长-短期记忆（LSTM）方法对美国最大P2P借贷平台Lending Club中借款人的违约风险进行顺序预测。虽然LSTM方法首次应用于P2P序列数据建模，但在我们的实验中，它的性能优于传统使用的时间序列模型，显示了其巨大的潜力。此外，结合宏观经济特征（即失业率），可以通过降低训练和测试数据集的RMSE来改善LSTM性能。我们的研究可以拓宽LSTM算法在P2P序列数据上的应用，并指导投资者制定投资策略。
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
PDF下载：
-->