| 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