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  KEYWORD

Exit prediction for Privately Held Companies

keywords CLASSIFIERS, FINANCE, MACHINE LEARNING, PREDICTION METHODS

Reference persons GIUSEPPE CARLO CALAFIORE

Research Groups SYSTEMS AND DATA SCIENCE - SDS

Thesis type APPLIED RESEARCH

Description Predicting the exit (e.g. IPO, bankrupt, acquisition, etc.) of privately held companies is a current and relevant problem for investment firms, whose difficulty stems from the lack of reliable and publicly available data.
In this thesis we shall study exit predictor models based on qualitative data. Methodologies will include Logistic Regression models, Random Forest models, Support Vector Machine models, as well as Survival models. Experimental tests will be conducted on available data from a Thomson Eikon repository.

Required skills Machine learning, optimization, basics of corporate finance.


Deadline 10/12/2019      PROPONI LA TUA CANDIDATURA