Predict high-frequency trading marker via manifold learning

Abstract High-frequency trading (HFT) has continuously evolved and dominated trading in the past decades. However, HFT trading marker prediction is a rarely investigated problem in Fintech literature.  In this study, we first propose a novel manifold learning based HFT trading marker prediction model: M-SCAN to handle this challenge. Our study takes advantage of manifold embeddings of HFT data and seeks potential markers among the outliers in density-based clustering. We further propose HFT trading marker evaluation algorithms to validate the prediction effectiveness besides unveiling trading marker discovery via visualization. Our results demonstrate locally linear embedding (LLE) outperforms its peers in capturing trading markers in terms of accuracy and complexity for its local data structure keeping mechanism in embedding though different stocks may demonstrate their model preference under M-SCAN. Our studies also propose novel entropy and variance concentration ratios (VCR) to quantify HFT data and show that a high-entropy dataset is more likely to have better trading marker prediction than a low-entropy one. To the best of our knowledge, it is the first study in HFT trading marker prediction and will inspire coming studies in this area.