Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms

TL;DR

This paper explores the use of classical and quantum algorithms, specifically quantum annealing on D-Wave hardware, for optimizing portfolios of 60 stocks, demonstrating practical applicability in financial decision-making.

Contribution

It introduces a novel application of quantum annealing to portfolio optimization with 60 stocks, extending previous work on 40 stocks and comparing classical and quantum methods.

Findings

Both classical and quantum methods effectively identify attractive portfolios.

Quantum annealing offers a viable alternative to classical optimization in finance.

Results support practical use of quantum algorithms for portfolio selection.

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.