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Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms

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  • Jeffrey Cohen
  • Alex Khan
  • Clark Alexander

Abstract

We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.

Suggested Citation

  • Jeffrey Cohen & Alex Khan & Clark Alexander, 2020. "Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms," Papers 2008.08669, arXiv.org.
    • Handle: RePEc:arx:papers:2008.08669
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    Cited by:

    1. Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
    2. Fred Glover & Gary Kochenberger & Rick Hennig & Yu Du, 2022. "Quantum bridge analytics I: a tutorial on formulating and using QUBO models," Annals of Operations Research, Springer, vol. 314(1), pages 141-183, July.
    3. Frank Phillipson & Harshil Singh Bhatia, 2020. "Portfolio Optimisation Using the D-Wave Quantum Annealer," Papers 2012.01121, arXiv.org.
    4. Francesco Catalano & Laura Nasello & Daniel Guterding, 2024. "Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios," Risks, MDPI, vol. 12(4), pages 1-20, April.
    5. Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.

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