Generative-discriminative machine learning models for high-frequency financial regime classification

Andreas Koukorinis, Gareth W. Peters and Guido Germano

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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: Fisher information kernel; hidden Markov model; Kernel methods; support vector machine (search for similar items in EconPapers)
JEL-codes: C32 C45 C53 G10 (search for similar items in EconPapers)
Date: 2025-06
New Economics Papers: this item is included in nep-big
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Published in Methodology and Computing in Applied Probability, June, 2025, 27(2). ISSN: 1387-5841

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