Browsing by Author Arroyo, J.

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  • Authors: Arroyo, J.;  Advisor: -;  Participants: Corea, F.; Jimenez-Diaz, G.; A. Recio-Garcia, J. (2019)

  • The venture capital (VC) industry offers opportunities for investment in early-stage companies where uncertainty is very high. Unfortunately, the tools investors currently have available are not robust enough to reduce risk and help them managing uncertainty better. Machine learning data-driven approaches can bridge this gap, as they already do in the hedge fund industry. These approaches are now possible because data from thousands of companies over the world is available through platforms such as Crunchbase. Previous academic efforts have focused only on predicting two classes of exits, i.e., being acquired by other company or offering shares to the public, using only one or a few s...