2026/03/04 by Weinberg, Abraham Itzhak, Abraham Itzhak Weinberg · 1 voice
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Asset allocation #Cluster analysis #Financial Markets and Investment Strategies #Pareto principle #Portfolio #Portfolio optimization #Quantum Computing Algorithms and Architecture #Schedule #Scheduling (production processes) #Sharpe ratio #Stock Market Forecasting Methods #Testbed #cs.AI #q-fin.PM
paper · pdf · doi:10.48550/arxiv.2603.16904
openalex created_date 2026/03/03 · openalex publication_date 2026/03/04 · arxiv published 2026/03/04 · arxiv updated 2026/03/04 · openalex updated_date 2026/07/28
We present a hybrid classical-quantum framework for portfolio construction and rebalancing. Asset selection is performed using Ledoit-Wolf shrinkage covariance estimation combined with hierarchical correlation clustering to extract n = 10 decorrelated stocks from the S&P 500 universe without survivorship bias. Portfolio weights are optimised via an entropy-regularised Genetic Algorithm (GA) accelerated on GPU, alongside closed-form minimum-variance and equal-weight benchmarks. Our primary contribution is the formulation of the portfolio rebalancing schedule as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. The resulting combinatorial optimisation task is solved using the Quantum Approximate Optimisation Algorithm (QAOA) within a walk-forward framework designed to eliminate lookahead bias. This approach recasts dynamic rebalancing as a structured binary scheduling problem amenable to variational quantum methods. Backtests on S&P 500 data (training: 2010-2024; out-of-sample test: 2025, n = 249 trading days) show that the GA + QAOA strategy attains a Sharpe ratio of 0.588 and total return of 10.1%, modestly outperforming the strongest classical baseline (GA with 10-day periodic rebalancing, Sharpe 0.575) while executing 8 rebalances versus 24, corresponding to a 44.5% reduction in transaction costs. Multi-restart QAOA (4096 measurement shots per run) exhibits concentrated probability mass on high-quality schedules, indicating stable convergence of the variational procedure. These findings suggest that hybrid classical-quantum architectures can reduce turnover in portfolio rebalancing while preserving competitive risk-adjusted performance, providing a structured testbed for near-term quantum optimisation in financial applications.