  We introduce TechRank, a recursive algorithm based on a bi-partite graph with
weighted nodes. We develop TechRank to link companies and technologies based on
the method of reflection. We allow the algorithm to incorporate exogenous
variables that reflect an investor's preferences. We calibrate the algorithm in
the cybersecurity sector. First, our results help estimate each entity's
influence 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 management and, in the case of private
equity investments, as a new way to price assets for which cash flows are not
observable.

Computational Finance