A hybrid quantum–classical machine learning framework for commodity market crash prediction
TL;DR
The results demonstrate the feasibility of integrating simulated quantum-kernel learning with classical machine learning techniques for commodity market crash prediction, and the proposed framework shows promising predictive performance.
Abstract
Stock market crashes pose a significant risk to investors and regulators; they are difficult to predict in their early stages because of market volatility. The conventional machine learning methods often struggle to learn financial time series having complex structures such as non-linearity, high dimensionality, and dynamic volatility, often found in a real-life turbulent market. Although gold and silver have been considered relatively stable investment assets, the crashing of these metal rates impacts retail investors as well as global finance in a powerful manner. To overcome these limitations, the current work presents a hybrid quantum-classical Machine Learning (QCML) approach for forecasting commodity market crashes that combines Quantum Support Vector Machines (QSVM) with eXtreme Gradient Boosting (XGBoost). In order to spot crash events, we have proposed a new drawdown-based labeling approach based on significant price declines as well as short-term recovery trends. Feature engineering and training is performed on baselines such as volatility measures, momentum indicators, and trend-based ratios. The proposed design also features Random Forest thresholding, ensemble weight optimization, and feature selection. As practical quantum hardware with sufficient capability is currently unavailable for our limited experiment, the quantum component is implemented using a simulator. The proposed Hybrid QSVM–XGBoost framework achieved its best performance for the Gold 3-month prediction horizon with an ROC-AUC of 0.81. The silver market, in contrast, has lower predictability (ROC-AUC = 0.62) because of greater volatility and complex dynamics. Our results demonstrate the feasibility of integrating simulated quantum-kernel learning with classical machine learning techniques for commodity market crash prediction. While the proposed framework shows promising predictive performance, it does not claim quantum advantage or quantum supremacy under current NISQ-era hardware limitations.