This paper examines the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. We compare the predictive performance of machine learning-based estimators (linear regressions, tree-based models, and neural networks) to that of the most commonly...

This study explores whether financial literacy can enhance the ability to predict credit default by farmers using machine-learning models. It introduces a hybrid model combining k-means clustering and Adaboost to predict loan default using data on 10,396 farmers who obtained credit from Chinese...

SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private...

Owing to the convenience of online loans, an increasing number of people are borrowing money on online platforms. With the emergence of machine learning technology, predicting loan defaults has become a popular topic. However, machine learning models have a black-box problem that cannot be...

Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of...