Namely ipo5/29/2023 ![]() PE investors critically need structured approaches allowing to infer basic future performance measures such as, the prospect for a company to IPO in the future or the value of a private company as a potential acquisition target. PE investors, looking at possible investments in privately held companies are lacking quantitative information allowing to build their investment case, and are resorting to methods such as portfolio diversification to compensate this lack of company specific information. READ FULL TEXT VIEW PDFĪs record amounts of funds are allocated to private equity (PE) investments – a record breaking $671 billion in 2017 – the main issue private equity investors are struggling with has not changed in decades: the absence of transparent, easily accessible valuation-related information. Valuable figure for Private Equity investors, who typically expect very high Our model achieves a 63% predictive accuracy, which is quite a Experiments have been conducted for predicting whether theĬompany eventually either gets acquired or goes public (IPO), against theĬomplementary event that it remains private or goes bankrupt, in the considered Thomson Reuters Eikon repository of 54697 US and European companies over theġ996-2011 time span. The models are trained using data extracted from the The output of theĬombined model is selected on the basis of the majority of the output classes Random Forest model, and a Support Vector Machine model. The outcomes of three classifiers, namely, a Logistic Regression model, a In this paper, we contribute to this endeavour byĬonstructing an exit predictor model based on qualitative data, which blends Theĭifficulty of the problem stems from the lack of reliable, quantitative and ![]() bankrupt, acquisition, etc.) of privately heldĬompanies is a current and relevant problem for investment firms.
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