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. Second, they provide investors with a quantitative optimal ranking of technologies and thus, help them design their optimal portfolio. We propose this method as an alternative to traditional portfolio theory and, in the case of private equity investments, as a new way to price private equity investments for which cash flows are not observable.