A bank's decision to go public by issuing an Initial Public Offering (IPO) transforms its operations and capital structure. Much of the empirical investigation in this area focuses on the determinants of the IPO decision, applying accounting ratios and other publicly available information in nonlinear models. We mark a break with this literature by offering methodological extensions and an extensive and updated U.S. dataset to predict bank IPOs. Combining the least absolute shrinkage and selection operator with a Cox proportional hazard, we uncover value in several financial factors as well as market‐driven and macroeconomic variables in predicting a bank's decision to go public. Importantly, we document a significant improvement in the model's predictive ability compared with standard frameworks used in the literature. Finally, we show that the sensitivity of a bank's IPO to financial characteristics is higher during periods of global financial crisis than in calmer times.