Jingyi Wang

TL;DR: Textual sentiment can significantly predict market return and provides support for the noise-trading theory and the limits-to-arbitrage argument, as well as predictions from limited-attention and disagreement models.

TL;DR: Li et al. as discussed by the authors examined how loan sharks use the online peer-to-peer lending market to lend money to Chinese students at exorbitant interest rates, using relational repression, which is the use of cyber violence and the threat of revealing damaging information to clients' social contacts.

TL;DR: In this paper, the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models was compared using transaction-level data from a leading fintech company in China for the period between May and September 2017.

TL;DR: In this article , a multi-feature deep information bottleneck (MDIB) was proposed for breast cancer classification in contrast enhanced spectral mammography (CESM) images, which incorporated an information bottleneck based module to learn the prominent representation that provide concise input while informative for the classification.