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

Abstract

We combine a hidden Markov model (HMM) and a kernel machine (SVM/MKL) into a hybrid HMM-SVM/MKL generative-discriminative machine learning approach to accurately classify high-frequency financial regimes and predict the direction of trades. The model captures 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 consistently over six trading regimes, outperforming a logistic classifier, single-kernel SVMs, a feed forward network and kernel target alignment methods. This hybrid HMM-SVM-MKL approach shows high-frequency time-series classification improvements that can significantly benefit applications in finance.