Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

This paper examines the impact of climate risks on the debt structure of a sample of U.S. firms from 2002 through 2020. Climate risks—mainly physical, regulatory, and transition risks—are associated with a concentrated debt structure for the affected firms. However, when climate risks propagate through the channels of expected bankruptcy costs and sustainability, they are associated with a more diversified debt structure. Additionally, climate risks asymmetrically impact the relationship between access to finance and debt structure. Results from a quasi-natural experiment reaffirm the impact of climate risks on debt structure.