Exploring Quantum Annealing for Enhanced International Financial Stock Portfolio Management

Quantum annealing (QA) represents a promising optimization technique to achieve both quantum and classical advantages and address real-world applications such as quantum finance, enabled by the rapid advancements in quantum computing (QC) in recent years. This study represents the first attempt to explore a novel weighted portfolio optimization model based on a new assessment strategy, trend ratio (TR), and extends its application to international stock markets, including DJIA, TSX, CAC, and FTSE. It utilizes the D-Wave Advantage quantum system and a hybrid solver to demonstrate its effectiveness and efficiency in solving enterprise-scale financial problems. Experimental findings demonstrate that QA outperforms both classical and state-of-the-art quantum-inspired optimization techniques in evaluating returns and risks, including metrics such as daily profit factor (DPF), maximum drawdown (MDD), and TR. Furthermore, QA exhibits outstanding search capabilities, facilitating rapid identification of high-performance portfolios within a remarkably short timeframe. Statistical tests are adopted to prove that QA significantly improves solution quality while requiring less computational cost.