Quantum-Inspired Chimp Optimizer Evolving Kernel Extreme Learning Machine for Financial Risk Prediction: A Case Study on Bankruptcy

Abstract

Kernel-based extreme learning machines (KBELMs) have demonstrated great potential in predicting bankruptcy because of their rapid learning capability and the ability to model nonlinear financial relationships. However, the predictive performance of KBELM is very sensitive to the choice of hyperparameters, and the traditional tuning strategies tend to have the shortcomings of early convergence and insufficient search space exploration. To overcome these problems, this study proposes a quantum-inspired chimp optimization algorithm (QICHOA) for effective hyperparameter optimization of KBELM, and the resultant hybrid model is called KBELM-QICHOA. The proposed optimizer is an improvement of the classical chimp optimization algorithm by adding quantum-inspired mechanisms for better global search capability and balancing exploration and exploitation. The performance of KBELM-QICHOA is evaluated using two real-world bankruptcy datasets, that is, Wieslaw dataset and Japanese bankruptcy dataset, under a nested cross-validation framework. The proposed model is compared with five benchmark approaches, which are conventional KBELM, KBELM-HFDO, KBELM-HAOA, KBELM-RCGWO, and KBELM-IPBBO. Experimental results show that KBELM-QICHOA is significantly better than competing models in terms of prediction accuracy, robustness and stability in terms of RMSE, Nash–Sutcliffe efficiency (NSEF), and bias. The results show that combining quantum-inspired optimization with kernel-based learning has a significant impact on improving the performance of bankruptcy prediction. The proposed KBELM-QICHOA framework therefore constitutes a reliable and economically meaningful early warning tool for the financial risk assessment and decision support.