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Quantum-Inspired Portfolio Optimization In The QUBO Framework

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  • Ying-Chang Lu
  • Chao-Ming Fu
  • Lien-Po Yu
  • Yen-Jui Chang
  • Ching-Ray Chang

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.

Suggested Citation

  • Ying-Chang Lu & Chao-Ming Fu & Lien-Po Yu & Yen-Jui Chang & Ching-Ray Chang, 2024. "Quantum-Inspired Portfolio Optimization In The QUBO Framework," Papers 2410.05932, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2410.05932
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    File URL: http://arxiv.org/pdf/2410.05932
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    References listed on IDEAS

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    1. Samuel Palmer & Serkan Sahin & Rodrigo Hernandez & Samuel Mugel & Roman Orus, 2021. "Quantum Portfolio Optimization with Investment Bands and Target Volatility," Papers 2106.06735, arXiv.org, revised Aug 2021.
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