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We consider the problem of evaluating the quality of startup companies. Thiscan be quite challenging due to the rarity of successful startup companies andthe complexity of factors which impact such success. In this work we collectdata on tens of thousands of startup companies, their performance, thebackgrounds of their founders, and their investors. We develop a novel modelfor the success of a startup company based on the first passage time of aBrownian motion. The drift and diffusion of the Brownian motion associated witha startup company are a function of features based its sector, founders, andinitial investors. All features are calculated using our massive dataset. Usinga Bayesian approach, we are able to obtain quantitative insights about thefeatures of successful startup companies from our model.  To test the performance of our model, we use it to build a portfolio ofcompanies where the goal is to maximize the probability of having at least onecompany achieve an exit (IPO or acquisition), which we refer to as winning.This $\textit{picking winners}$ framework is very general and can be used tomodel many problems with low probability, high reward outcomes, such aspharmaceutical companies choosing drugs to develop or studios selecting moviesto produce. We frame the construction of a picking winners portfolio as acombinatorial optimization problem and show that a greedy solution has strongperformance guarantees. We apply the picking winners framework to the problemof choosing a portfolio of startup companies. Using our model for the exitprobabilities, we are able to construct out of sample portfolios which achieveexit rates as high as 60%, which is nearly double that of top venture capitalfirms.

Picking Winners: A Data Driven Approach to Evaluating the Quality of Startup Companies