Using Machine Learning in Venture Capital

I have already (partially) reviewed previous studies where data have been proved to help identify signals that are relevant to assess the success potential of a startup. Even though the list is quite comprehensive, every study usually tends to look at one single factor and a couple of different success scenarios (namely, acquisition and IPO). In our work, we tried to have a more holistic view and use over 120,000 companies to spot signals not only for acquisitions and IPOs but also to compute the probability of raising a subsequent round of funding or shutting the startup down. In the same fashion as backtesting, we created a time-aware approach and analyzed companies that were no older than four years old by 2015 and tried to predict their success in the following three years. We also used more than a hundred variables as possible explanatory indicators of success, as well as five different models: Support Vector Machines (SVM); Decision Trees (DT); Random Forests (RF); Extremely Randomized Trees (ERT); and Gradient Tree Boosting (GTB).