2 papers
cs.NI2026
In-Network Market Prediction Using Machine Learning and Limit Order Books
Xinpeng Hong, Changgang Zheng, Joshua Lilley +2
Machine learning is significantly transforming algorithmic trading, yet the requirement for rapid execution speeds persists. While both aspects aim to boost profitability, embeddin…
q-fin.ST2026
Macro-aware time series forecasting via hierarchical mixed-frequency attention models
Daniel Cunha Oliveira, Kieran Wood, Stefan Zohren +2
Deep learning models show promise in financial forecasting, yet their generalization is often undermined by small datasets, noisy signals, and non-stationarity. While meta-learning…