  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.
