We use machine learning techniques to conduct out-of-sample predictions of the underpricing of U.S. initial public offerings (IPOs) from 1990 to 2019. Using predicted underpricing based on ex ante information to sort the IPOs into 10 groups, we find that the underpricing averages for the top and...

The recovery rate on defaulted corporate bonds has a time-varying distribution. We propose machine learning approaches for intertemporal analysis of U.S. corporate bonds' recovery rates with a large number of predictors. The most informative macroeconomic variables are selected from a broad...

Innovations in statistical technology have sparked concerns about distributional impacts across categories such as race and gender. Theoretically, as statistical technology improves, distributional consequences depend on how changes in functional forms interact with cross-category distributions...

We develop empirical models for the detection of insider abuse and fraud occurring at U.S. commercial banks with the goals of identifying leading indicators of fraud and fraud prediction. We use information from enforcement actions taken by bank supervisors to remove offending bank employees and...

Advanced tree-based estimation methods, such as random forest, are ensembles of regression trees that are built using random subsets of explanatory variables. However, because of the random selection process, relevant variables may not be considered in some regression trees, thereby reducing...

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...

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to...

As supply chain channels physical, financial, and information flows as well as associated risks, a firm’s supply chain information should be helpful in understanding and predicting its credit risks. Credit ratings as an approximate but important measure of corporate credit risks have been...

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on studies that either use or inform on accounting variables. The article concludes by providing...

Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis...