Survival and Neural Models for Private Equity Exit Prediction

TL;DR

This paper introduces a neuro-survival model combining neural networks and survival analysis to predict IPO events in private equity, tested across multiple sectors with real-world data.

Contribution

It presents a novel neuro-survival modeling approach for IPO prediction, integrating neural networks with survival analysis for better temporal event forecasting.

Findings

Effective IPO probability estimation across sectors

Improved prediction accuracy over traditional methods

Validated on real Thomson Reuters Eikon PE data

Abstract

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.