Extending Deep Learning Models for Limit Order Books to Quantile Regression
Trading and Market Microstructure
2019-06-13 v1
Abstract
We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-series. We develop a deep learning architecture that simultaneously models the return quantiles for both buy and sell positions. We test our model over millions of LOB updates across multiple different instruments on the London Stock Exchange. Our results suggest that the proposed network not only delivers excellent performance but also provides improved prediction robustness by combining quantile estimates.
Cite
@article{arxiv.1906.04404,
title = {Extending Deep Learning Models for Limit Order Books to Quantile Regression},
author = {Zihao Zhang and Stefan Zohren and Stephen Roberts},
journal= {arXiv preprint arXiv:1906.04404},
year = {2019}
}
Comments
5 pages, 4 figures, Time Series Workshop of the ICML (2019)