This paper examines the prediction of IPO withdrawal using machine learning methods (lasso and random forest) and conventional regression (logit). The dataset comprises 2.444 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. These findings suggest that the generalizability of previous results.
External publication | 2025 Comparing the Prediction Performance of Random Forest, Lasso, and Logit in the Context of IPO Withdrawal
Reiff, A. (2025): Comparing the Prediction Performance of Random Forest, Lasso, and Logit in the Context of IPO Withdrawal, in: Intelligent Systems in Accounting, Finance and Management, Volume 32, Issue 3, p. 1-31.