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<p>In contemporary financial markets, accurate risk prediction is critical for market participants. Existing approaches often face limitations in computational efficiency, model complexity, and predictive performance. This study proposes a novel Quantum-Inspired Chimpanzee Optimization Algorithm with Kernel Extreme Learning Machine (QChOA-KELM) for financial risk prediction. The methodology combines quantum computing principles with metaheuristic optimization to enhance the KELM’s parameter selection, improving both prediction accuracy and model robustness. Experimental validation using a Kaggle-sourced financial risk dataset demonstrates the model’s superior performance: QChOA-KELM achieves a 10.3% accuracy improvement over baseline KELM and outperforms conventional methods by at least 9% across evaluation metrics. The results indicate that our approach provides an effective computational framework for financial risk assessment, offering significant advantages in predictive performance while maintaining computational efficiency.</p>

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A new hybrid neural network framework inspired by biological systems for advanced financial forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

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  • Chuchu Rao,
  • Tianju Xue,
  • Mingqi Kan,
  • Peng Zhou,
  • Yeshen Lan

摘要

In contemporary financial markets, accurate risk prediction is critical for market participants. Existing approaches often face limitations in computational efficiency, model complexity, and predictive performance. This study proposes a novel Quantum-Inspired Chimpanzee Optimization Algorithm with Kernel Extreme Learning Machine (QChOA-KELM) for financial risk prediction. The methodology combines quantum computing principles with metaheuristic optimization to enhance the KELM’s parameter selection, improving both prediction accuracy and model robustness. Experimental validation using a Kaggle-sourced financial risk dataset demonstrates the model’s superior performance: QChOA-KELM achieves a 10.3% accuracy improvement over baseline KELM and outperforms conventional methods by at least 9% across evaluation metrics. The results indicate that our approach provides an effective computational framework for financial risk assessment, offering significant advantages in predictive performance while maintaining computational efficiency.