Utility-Maximizing Binary Prediction via the Nearest Neighbor Method and Its Application to Credit Scoring
提出在效用最大化二元预测框架下使用k近邻规则，通过变量选择缓解维度灾难，并证明其效用一致性，在P2P借贷信用评分中验证了实用性。
We propose nonparametric <i>k</i>-nearest neighbor prediction rules under the framework of utility-maximizing binary prediction with possibly many predictors. One of these prediction rules, with an attempt to “break” the curse of dimensionality, is constructed based on the predictors selected by variable selection methods. We establish that allowing for the data-dependent selection of parameter <i>k</i>, these prediction rules are utility consistent under regularity assumptions. Such utility consistency is confirmed by the simulation results. We illustrate these prediction rules with an application to credit scoring in peer-to-peer lending and find that common predictors of the business cycle yield limited improvement in profitability.