  Within the Private Equity (PE) market, the event of a private company
undertaking an Initial Public Offering (IPO) is usually a very high-return one
for the investors in the company. For this reason, an effective predictive
model for the IPO event is considered as a valuable tool in the PE market, an
endeavor in which publicly available quantitative information is generally
scarce. In this paper, we describe a data-analytic procedure for predicting the
probability with which a company will go public in a given forward period of
time. The proposed method is based on the interplay of a neural network (NN)
model for estimating the overall event probability, and Survival Analysis (SA)
for further modeling the probability of the IPO event in any given interval of
time. The proposed neuro-survival model is tuned and tested across nine
industrial sectors using real data from the Thomson Reuters Eikon PE database.
