Generative-Discriminative Machine Learning Models for High-Frequency Financial Regime Classification

Journal article

We combine a hidden Markov model (HMM) and a kernel machine (SVM/MKL) into a hybrid HMM-SVM/MKL generative-discriminative learning approach to accurately classify high-frequency financial regimes and predict the direction of trades. We capture temporal dependencies and key stylized facts in high-frequency financial time series by integrating the HMM to produce model-based generative feature embeddings from microstructure time series data. These generative embeddings then serve as inputs to a SVM with single- and multi-kernel (MKL) formulations for predictive discrimination. Our methodology, which does not require manual feature engineering, improves classification accuracy compared to single-kernel SVMs and kernel target alignment methods. It also outperforms both logistic classifier and feed-forward networks. This hybrid HMM-SVM-MKL approach shows high-frequency time-series classification improvements that can significantly benefit applications in finance.

Authors: Koukorinis, A; Peters, GW; Germano, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Methodology and Computing in Applied Probability
• Volume: 27
• Issue: 2
• Article number: 36
• Publication date: 2025-04-16
• Acceptance date: 2025-02-19
• DOI: 10.1007/s11009-025-10148-8
• EISSN: 1573-7713
• ISSN: 1387-5841
• Language: English
• Keywords: Hidden Markov model; Kernel methods; Fisher information kernel; Support vector machine