A Classifiers Voting Model for Exit Prediction of Privately Held Companies

TL;DR

This paper presents a voting ensemble of classifiers to predict the exit outcomes of private companies using qualitative data, achieving 63% accuracy to aid investment decisions.

Contribution

It introduces a novel ensemble model combining Logistic Regression, Random Forest, and SVM for private company exit prediction from qualitative data.

Findings

Achieved 63% predictive accuracy.

Effectively predicted acquisition or IPO outcomes.

Utilized data from 54,697 companies over 15 years.

Abstract

Predicting the exit (e.g. bankrupt, acquisition, etc.) of privately held companies is a current and relevant problem for investment firms. The difficulty of the problem stems from the lack of reliable, quantitative and publicly available data. In this paper, we contribute to this endeavour by constructing an exit predictor model based on qualitative data, which blends the outcomes of three classifiers, namely, a Logistic Regression model, a Random Forest model, and a Support Vector Machine model. The output of the combined model is selected on the basis of the majority of the output classes of the component models. The models are trained using data extracted from the Thomson Reuters Eikon repository of 54697 US and European companies over the 1996-2011 time span. Experiments have been conducted for predicting whether the company eventually either gets acquired or goes public (IPO), against the complementary event that it remains private or goes bankrupt, in the considered time window. Our model achieves a 63\% predictive accuracy, which is quite a valuable figure for Private Equity investors, who typically expect very high returns from successful investments.