TechRank

TL;DR

TechRank is a recursive, bi-partite graph-based algorithm that ranks companies and technologies, incorporating investor preferences, to assist in portfolio optimization and asset valuation, especially in sectors like cybersecurity.

Contribution

The paper introduces TechRank, a novel recursive algorithm that links companies and technologies using weighted bi-partite graphs and reflection methods, incorporating exogenous preferences.

Findings

Estimates influence of entities and explains rankings.

Provides a quantitative optimal ranking for investors.

Offers an alternative to traditional portfolio management.

Abstract

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.