Data-Driven Robust Credit Portfolio Optimization for Investment Decisions in P2P Lending

Abstract

Peer-to-Peer (P2P) lending has attracted increasing attention recently. As an emerging micro-finance platform, P2P lending plays roles in removing intermediaries, reducing transaction costs, and increasing the benefits of both borrowers and lenders. However, for the P2P lending investment, there are two major challenges, the deficiency of loans’ historical observations about the certain borrower and the ambiguity problem of estimated loans’ distribution. In order to solve the difficulties, this paper proposes a data-driven robust model of portfolio optimization with relative entropy constraints based on an “instance-based” credit risk assessment framework. The model exploits a nonparametric kernel approach to estimate P2P loans’ expected return and risk under the condition that the historical data of the same borrower is unavailable. Furthermore, we construct a robust mean–variance optimization problem based on relative entropy method for P2P loan investment decision. Using the real-world dataset from a notable P2P lending platform, Prosper, we validate the proposed model. Empirical results reveal that our model provides better investment performances than the existing model.