A Quantum-Inspired Multi-objective Portfolio Strategy Based on Trend Ratio Model in Global Financial Network

Portfolio strategy search is an essential topic in the financial and economic networks. Various risk preferences of investors should be considered to accommodate their investment choices, thereby introducing multi-objective portfolio optimization. This study utilizes an innovative and accurate evaluation model, the trend ratio (TR), to assess return and risk and construct a multi-objective optimization (MOO) model. Within a huge search space, computational intelligence techniques achieve high-performance computing through effective nature-inspired computing. The proposed TR-based MOO quantum-inspired model with an entanglement-enhanced local search (EL) mech-anism can search for Pareto optimal solutions (POSs) in global investment markets. The experimental results demonstrate that the TR-based EL mechanism effectively achieves a larger number of POSs and improves efficient frontiers to meet the different risk profiles of investors across global markets. A visualized Pareto front (PF) of the TR model helps a user identify a more straightforward return and risk balance evaluation, helping investors make informed decisions. The entanglement-simulated local search mechanism has been proven to improve the search for a better PF significantly. The TR-based, quantum-inspired MOO portfolio strategy search has paved the way for flexible solutions in global financial networks.