We continue to research quantum topics: portfolio optimization & quantum walks on graphs
Our latest paper (3 of 3): Portfolio Optimization of 3,171 Stocks
Authors: Jeffrey Cohen & Clark Alexander
Date: October 30, 2020
Date: October 30, 2020
Abstract: We analyze 3,171 US common stocks to create an efficient portfolio based on the Chicago Quantum Net Score (CQNS) and portfolio optimization. We begin with classical solvers and incorporate quantum annealing. We add a simulated bifurcator as a new classical solver and the new D-Wave Advantage(TM) quantum annealing computer as our new quantum solver.
15 pages, 9 figures.
Quantum Physics (quant-ph); Portfolio Management (q-fin.PM)
Cite as: arXiv:2011.01308 [quant-ph]
15 pages, 9 figures.
Quantum Physics (quant-ph); Portfolio Management (q-fin.PM)
Cite as: arXiv:2011.01308 [quant-ph]
Article 3 walk through & Nov 14, 2020 research update
Recent presentations of our work
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Washington DC Quantum Computing Meetup: Sept 9 {completed}
"The race is on...Quantum Annealing vs. other methods we learned". A successful, 2-hour discussion. Zen4Quantum Meetup: Sept 18 {completed} "Quantum Stock Portfolios - Chicago Quantum Talk Series Part 2". An enjoyable 2-hour discussion where we discuss our goals, accomplishments, challenges, and newest methods. Recording here FS Club (London): Sept 21 {completed] "The Right Balance? Using a Quantum Annealing Computer for your Portfolio". A fast-paced 45 minute discussion on the the performance of published portfolios, the 'rig' we use, and our thoughts on scaling up further. Recording here Qubits 2020: Sept 28 {Completed} https://www.qubits.com/ Recording here IQT Europe: Oct 28 (Financial Services Panel Discussion) https://europe.iqtevent.com/ Recording here 
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Our 2nd paper (2 of 3): Portfolio Optimization of 60 Stocks
Authors: Jeffrey Cohen, Alex Khan, Clark Alexander
Date: August 19, 2020
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.
Comments:19 pages, 15 figures, 21 references
Subjects:General Finance (q-fin.GN); Quantum Physics (quant-ph)
Cite as:arXiv:2008.08669 [q-fin.GN]
Date: August 19, 2020
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.
Comments:19 pages, 15 figures, 21 references
Subjects:General Finance (q-fin.GN); Quantum Physics (quant-ph)
Cite as:arXiv:2008.08669 [q-fin.GN]
Our 1st paper (1 of 3): Portfolio Optimization of 40 Stocks
Authors: Jeffrey Cohen, Alex Khan, Clark Alexander
Date: July 6, 2020
Abstract: We investigate the use of quantum computers for building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter called D-Wave) to find the optimal risk vs return portfolio; an optimized portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this first classically, then by our new method on D-Wave. Our results show that practitioners can use a D-Wave to select attractive portfolios out of 40 U.S. liquid equities.
Comments:15 pages, 8 figures
Subjects:General Finance (q-fin.GN); Quantum Physics (quant-ph)
Cite as:arXiv:2007.01430 [q-fin.GN]
Date: July 6, 2020
Abstract: We investigate the use of quantum computers for building a portfolio out of a universe of U.S. listed, liquid equities that contains an optimal set of stocks. Starting from historical market data, we look at various problem formulations on the D-Wave Systems Inc. D-Wave 2000Q(TM) System (hereafter called D-Wave) to find the optimal risk vs return portfolio; an optimized portfolio based on the Markowitz formulation and the Sharpe ratio, a simplified Chicago Quantum Ratio (CQR), then a new Chicago Quantum Net Score (CQNS). We approach this first classically, then by our new method on D-Wave. Our results show that practitioners can use a D-Wave to select attractive portfolios out of 40 U.S. liquid equities.
Comments:15 pages, 8 figures
Subjects:General Finance (q-fin.GN); Quantum Physics (quant-ph)
Cite as:arXiv:2007.01430 [q-fin.GN]