Machine learning

TechRank (with A. Mezzetti et al.)

We introduce TechRank, a recursive algorithm based on a bi-partite graph with weighted nodes. We develop TechRank with the purpose of linking companies and technologies based on the method of reflection. We allow the algorithm to incorporate exogenous variables that reflect the preferences of an investor. We calibrate the algorithm in the cybersecurity sector. First, our results help to estimate the influence of each entity and explain companies and technologies ranking.

The new risk and return of venture capital investments (with F. Burguet)

This paper revisits the study of Cochrane (2005), to estimate the risk and returns of venture capital investments, while correcting for the selection bias. We use an up-to-date dataset and enhance it to account for missing firm valuations using machine learning. The model is able to infer, with a median error of less than 4%, the true log-value of the firm, for a total of nearly 120,000 observations, or six times more than the original paper, from 2010 to 2022.