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

Koukorinis, Andreas, Peters, Gareth W. and Germano, Guido (2025) Generative-discriminative machine learning models for high-frequency financial regime classification. Methodology and Computing in Applied Probability, 27 (2). ISSN 1387-5841

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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.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics H Social Sciences > HG Finance
JEL classification:	C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Other Model Applications G - Financial Economics > G1 - General Financial Markets > G10 - General C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
Date Deposited:	29 Apr 2025 09:24
Last Modified:	02 May 2025 13:12
URI:	http://eprints.lse.ac.uk/id/eprint/128016

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