Extending Deep Learning Models for Limit Order Books to Quantile Regression

Zihao Zhang, Stefan Zohren and Stephen Roberts

Papers from arXiv.org

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.

Date: 2019-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-mst
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Published in Proceedings of Time Series Workshop of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1906.04404

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