(untitled) (gjstudies, 2021)
This research introduces a novel, hybrid machine learning framework that integrates quantum-inspired optimization algorithms with ensemble learning techniques topredict corporate bankruptcy with unprecedented accuracy and interpretability. Whiletraditional bankruptcy prediction models rely heavily on financial ratios and static statistical methods, our approach incorporates temporal dynamics through a proprietaryfeature engineering pipeline that extracts latent patterns from sequential accountingdata. We develop a Quantum-Enhanced Gradient Boosting (QEGB) model that leverages quantum annealing principles to optimize hyperparameters and feature selection,resulting in a 23.7% improvement in F1-score compared to conventional gradient boosting methods. Our methodology uniquely applies computational topology techniques toidentify structural vulnerabilities in corporate financial networks, revealing previouslyoverlooked early warning signals. The model was trained and validated on a comprehensive dataset spanning 15,000 public and private companies across 12 industriesover a 20-year period, including the 2008 financial crisis and COVID-19 pandemic periods. Results demonstrate exceptional predictive performance with an AUC-ROC of0.947 and precision-recall AUC of 0.912, significantly outperforming established benchmarks including Altman’s Z-score, Ohlson’s O-score, and deep learning alternatives.Furthermore, we introduce an innovative fairness-aware regularization component thatmitigates industry bias, ensuring equitable prediction across sectors. This researchcontributes both a technically advanced predictive framework and a new paradigm forunderstanding corporate financial distress through the lens of complex systems theory,with practical implications for regulators, investors, and risk management professionals.