A Sequential Approach to Market State Modeling and Analysis in Online P2P Lending
针对P2P借贷中标的物市场状态（如冷热）难以识别的问题，提出两种增强的贝叶斯隐马尔可夫模型，分别利用局部和全局信息学习市场状态与出借人投标行为的关系，并在真实数据集上验证了投标预测和羊群检测等应用的有效性。
Online peer-to-peer (P2P) lending is an emerging wealth-management service for individuals, which allows lenders to directly bid and invest on the listings created by borrowers without going through any traditional financial intermediaries. As a nonbank financial platform, online P2P lending tends to have both high volatility and liquidity. Therefore, it is of significant importance to discern the hidden market states of the listings (e.g., hot and cold), which open venues for enhancing business analytics and investment decision making. However, the problem of market state modeling remains pretty open due to many technical and domain challenges, such as the dynamic and sequential characteristics of listings. To that end, in this paper, we present a focused study on market state modeling and analysis for online P2P lending. Specifically, we first propose two enhanced sequential models by extending the Bayesian hidden Markov model (BHMM), namely listing-BHMM (L-BHMM) and listing and marketing-BHMM (LM-BHMM), for learning the latent semantics between listings' market states and lenders' bidding behaviors. Particularly, L-BHMM is a straightforward model that only considers the local observations of a listing itself, while LM-BHMM considers not only the listing information but also the global information of current market (e.g., the competitive and complementary relations among listings). Furthermore, we demonstrate several motivating applications enabled by our models, such as bidding prediction and herding detection. Finally, we construct extensive experiments on two real-world data sets and make some deep analysis on bidding behaviors, which clearly validate the effectiveness of our models in terms of different applications and also reveal some interesting business findings.