Comparing the Prediction Performance of Random Forest, Lasso, and Logit in the Context of IPO Withdrawal

Abstract

This paper examines the prediction of IPO withdrawal using machine learning methods (lasso and random forest) and conventional regression (logit). The dataset comprises 2444 US first-time IPOs from 1997 to 2014. Results show that random forest outperforms both logit and lasso in in-sample and cross-sectional out-of-sample predictions when the training and test sets are drawn from the same time period. However, when models are trained on past data and tested on future observations, all models fail to accurately predict IPO withdrawal. This failure is attributed to concept drift—a change in the relationship between predictors and IPO withdrawal over time. I show that concept drift occurs at multiple points in time, affects various predictors, and persists even when accounting for economic shocks, institutional changes, or different prediction horizons. These findings suggest that the generalizability of previous results on IPO withdrawal is limited, as the relationship between various predictors and IPO withdrawal seems to vary across time periods.