1.8k citations · 2.1k across the 7 of their papers we have counts for
8 papers
From Economic Explanation to Performance Improvement in Machine-Learning Portfolios
Yunqi Liang, Stefan Zohren, Mihai Cucuringu
To extend conventional model-level feature importance to economically meaningful portfolio outcomes and decisions, we propose a local first-order attribution-propagation framework…
ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. W…
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationsh…
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network
Peer Nagy, Sascha Frey, Silvia Sapora +4
Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we pr…
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series…
Time Series Forecasting With Deep Learning: A Survey
Bryan Lim, Stefan Zohren
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains. In this article, we survey common encoder an…
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang, Stefan Zohren, Stephen Roberts
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to captur…
Quantum codes from classical graphical models
Joschka Roffe, Stefan Zohren, Dominic Horsman +1
We introduce a new graphical framework for designing quantum error correction codes based on classical principles. A key feature of this graphical language, over previous approache…