Identifying the Factors Influencing IPO Underpricing using Explainable Machine Learning Techniques

Abstract

We study the underpricing phenomenon in the U.S. Initial Public Offering (IPO) market using an interpretative machine learning approach. Inadequate conventional modelling of IPOs efficiency can lead to suboptimal investment decisions and a poor understanding of underlying factors that drive IPO underpricing. We employ interpretative machine learning for both predicting the numeric underpricing levels and classifying IPOs into underpriced and overpriced categories. We use the SHapley Additive exPlanations method to provide concise insights into the underlying factors that contribute to underpricing. Our numerical study reveals that offer price has the most predictive power for the models’ output, followed by equity retained and assets. Additionally, underpricing is more pronounced in technology-based sectors, and a higher dispersion in quality among firms during IPO surges results in higher levels of underpricing. Our analyses emphasize the importance of considering both the industry sector and market conditions when evaluating the firms going public.