Stock Market Forecasting Based on Quantum Fuzzy Support Vector Machine

This paper proposes a Quantum Fuzzy Support Vector Machine (QFSVM) model by integrating fuzzy set theory, quantum computing, and Support Vector Machines (SVM). A fuzzy membership function based on the K-nearest neighbor algorithm is introduced to reduce noise impact and differentiate sample importance. Quantum kernel methods replace traditional kernels to leverage quantum advantages. For stock market forecasting, the model incorporates stock factors and processes political and social news using FinBERT to extract news-related features. The QFSVM constructs a unique feature space, capturing complex nonlinear patterns in the data. Experimental results show that QFSVM outperforms SVM in accuracy, F1 score, and precision, achieving 76.55% accuracy—a 7.72% improvement over SVM—and surpassing state-of-the-art models. Ablation studies confirm the necessity of both quantum kernels and fuzzy membership functions. This research represents an innovative fusion of quantum computing, fuzzy set theory, and SVM in finance, advancing quantum machine learning and stock market prediction while offering new opportunities and challenges for future exploration.