α -threshold networks in credit risk models
针对P2P借贷市场的高维数据，提出两种基于网络的特征提取方法（k-PAM和α阈值模型），通过识别贷款间的相似性来改进利润评分模型，实证表明网络特征能提升预测表现和投资回报。
Peer-to-peer (P2P) lending markets offer risky investment opportunities, for which accurate credit risk models are in high demand. Loan books offer a broad spectrum of loan and borrower characteristics, making it challenging to construct high-dimensional systems that make the use of traditional credit scoring models. In this study, we propose two network-based feature extraction methods that extract complex relationships between risky assets, namely, loans, which are represented as vertices, and weighted edges, which correspond to the feature-based similarity between loans. Our two methods differ with respect to how similar loans are identified. The traditional approach uses partitioning based on the medoid algorithm to identify similar loans (the k-PAM model). A much faster alternative is to eliminate 100[%](1−𝛼) of the largest distances (the α-threshold model). The resulting network structure is used to extract features that augment profit scoring models. Utilizing P2P loan data, we find that forecasting models that use network-based features consistently outperform the benchmarks in a statistical sense and lead to higher returns and risk-adjusted returns.