Paper: arXiv 2410.05932

Abstract

A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which is validated through experiments using a real-world dataset of quarterly financial data spanning over ten-year period. In addition, the proposed preprocessing method of two-stage search further enhances the effectiveness of our approach, showing the ability to improve computational efficiency while maintaining solution accuracy through appropriate setting of parameters. This research contributes to the growing body of literature on quantum-inspired techniques in finance, demonstrating its potential as a useful tool for asset allocation and portfolio management.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical techniques including quadratic unconstrained binary optimization (QUBO), binary expansion for variable discretization, and Monte Carlo simulation for parameter tuning, indicating high mathematical complexity. While it uses a real-world dataset spanning ten years, the excerpt lacks specific performance metrics, detailed backtest results, or comparative benchmarks, suggesting moderate empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Portfolio Optimization via<br/>Quantum-Inspired QUBO Framework"] --> B["Data Input<br/>Real-world Quarterly Financial Data<br/>(10-year period)"]
  B --> C["Methodology<br/>Two-Stage Search Preprocessing"]
  C --> D["Core Process<br/>Quantum-Inspired Optimization<br/>+ Penalty Coefficient Estimation"]
  D --> E{"Computational Process<br/>Solve QUBO for Optimal Asset Mix"}
  E --> F["Outcomes<br/>Enhanced Efficiency & Solution Accuracy"]
  F --> G["Conclusion<br/>Validated Tool for<br/>Asset Allocation & Portfolio Management"]

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0 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.