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

Andreas Koukorinis (), Gareth W. Peters () and Guido Germano ()
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Andreas Koukorinis: University College London
Gareth W. Peters: University of California
Guido Germano: University College London

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 2, 1-32

Abstract: 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.

Keywords: Kernel methods; Fisher information kernel; Hidden Markov model; Support vector machine (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11009-025-10148-8

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