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TechRank

Author

Listed:
  • Anita Mezzetti
  • Loic Mar'echal
  • Dimitri Percia David
  • William Lacube
  • S'ebastien Gillard
  • Michael Tsesmelis
  • Thomas Maillart
  • Alain Mermoud

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.

Suggested Citation

  • Anita Mezzetti & Loic Mar'echal & Dimitri Percia David & William Lacube & S'ebastien Gillard & Michael Tsesmelis & Thomas Maillart & Alain Mermoud, 2022. "TechRank," Papers 2210.07824, arXiv.org.
    • Handle: RePEc:arx:papers:2210.07824
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    References listed on IDEAS

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