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

Author

Listed:
  • Andreas Koukorinis

    (University College London
    Trading)

  • Gareth W. Peters

    (University of California
    London School of Economics and Political Science
    University of Oxford)

  • Guido Germano

    (University College London
    London School of Economics and Political Science)

Abstract

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.

Suggested Citation

  • Andreas Koukorinis & Gareth W. Peters & Guido Germano, 2025. "Generative-Discriminative Machine Learning Models for High-Frequency Financial Regime Classification," Methodology and Computing in Applied Probability, Springer, vol. 27(2), pages 1-32, June.
    • Handle: RePEc:spr:metcap:v:27:y:2025:i:2:d:10.1007_s11009-025-10148-8
    DOI: 10.1007/s11009-025-10148-8
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