Graph Neural Networks for Forecasting Realized Volatility with Nonlinear Spillover Effects

Chao Zhang, University of Oxford – Department of Statistics

Xingyue (Stacy) Pu, University of Oxford – Oxford Man Institute of Quantitative Finance

Mihai Cucuringu, University of Oxford – Department of Statistics

Xiaowen Dong, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

View fusion vis-à-vis a Bayesian interpretation of Black-Litterman for portfolio allocation 

Trent Spears, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv

Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks

Milena Vuletic, University of Oxford – Department of Statistics

Felix Prenzel, University of Oxford – Mathematical Institute

Mihai Cucuringu, University of Oxford – Department of Statistics

SSRN

Learning to Collude: A Folk Theorem for Algorithms

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Patrick Chang, University of Oxford – Oxford Man Institute of Quantitative Finance

José Penalva, Universidad Carlos III, Madrid – Department of Business Administration

Harrison Waldon, University of Texas at Austin

SSRN

Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Fayçal Drissi, University of Oxford – Oxford Man Institute of Quantitative Finance, Université Paris1 Panthéon-Sorbonne – Centre d’Economie de la Sorbonne (CES)

Marcello Monga, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

DeFi: Data-Driven Characterisation of Uniswap V3 Ecosystem & and Ideal Crypto Law for Liquidity Pools

Deborah Miori, University of Oxford – Department of Statistics

Mihai Cucuringu, University of Oxford – Department of Statistics

SSRN

Volatility Forecasting with Machine Learning and Intraday Commonality

Chao Zhang, University of Oxford – Department of Statistics

Yihuang Zhang, University of Oxford – Mathematical Institute

Mihai Cucuringu, University of Oxford – Department of Statistics

Zhongmin Qian, University of Oxford – Mathematical Institute

SSRN

Graph-based Methods for Forecasting Realized Covariances

Chao Zhang, University of Oxford – Department of Statistics

Xingyue (Stacy) Pu, University of Oxford – Oxford Man Institute of Quantitative Finance

Mihai Cucuringu, University of Oxford – Department of Statistics

Xiaowen Dong, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

Kieran Wood, University of Oxford – Oxford Man Institute of Quantitative Finance

Sven Giegerich, QuantCo, Berlin

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv

Brokers and Informed Traders: dealing with toxic flow and extracting trading signals

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Leandro Sànchez-Betancourt, King’s College London

SSRN

The Algorithmic Learning Equations: Evolving Strategies in Dynamic Games

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Patrick Chang, University of Oxford – Oxford Man Institute of Quantitative Finance

José Penalva, Universidad Carlos III, Madrid – Department of Business Administration

Harrison Waldon, University of Texas at Austin

SSRN

Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning

Anthony Coache, University of Toronto 

Sebastian Jaimungal, University of Toronto

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Decentralised Finance and Automated Market Making: Execution and Speculation

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Fayçal Drissi, University of Oxford – Oxford Man Institute of Quantitative Finance, Université Paris1 Panthéon-Sorbonne – Centre d’Economie de la Sorbonne (CES)

Marcello Monga, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Graph Similarity Learning for Change-Point Detection in Dynamic Networks

Deborah Sulem, University of Oxford – Department of Statistics

Henry Kenlay, University of Oxford – Oxford Man Institute of Quantitative Finance

Mihai Cucuringu, University of Oxford – Department of Statistics

Xiaowen Dong, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv

Algorithmic Collusion in Electronic Markets: The Impact of Tick Size

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Patrick Chang, University of Oxford – Oxford Man Institute of Quantitative Finance

José Penalva, Universidad Carlos III, Madrid – Department of Business Administration

SSRN

AI Driven Liquidity Provision in OTC Financial Markets

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Patrick Chang, University of Oxford – Oxford Man Institute of Quantitative Finance

Mateusz Mroczka, University of Oxford

Roel Oomen, Deutsche Bank AG (London), London School of Economics

Taylor & Francis Online

Canonical Portfolios: Optimal Asset and Signal Combination

Vincent Tan, University of Oxford – Oxford Man Institute of Quantitative Finance

Nick Firoozye, University College London – Department of Computer Science

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2202.10817 .10430v2

Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention

Daniel Poh, University of Oxford – Oxford Man Institute of Quantitative Finance

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

Journal of Financial Data Science 2022

Gradient-based estimation of linear Hawkes processes with general kernels

Álvaro Cartea, University of Oxford – Oxford Man Institute of Quantitative Finance

Samuel N. Cohen, Mathematical Institute, University of Oxford

Saad Labyad, Mathematical Institute, Oxford Man Institute of Quantitative Finance, University of Oxford

SSRN

Strategic Execution Trajectories

Giuliana Bordigoni, Man AHL

Alessio Figalli, ETH Zurich

Anthony Ledford, Man AHL 

Philipp Ustinov, Man Group plc

SSRN

Adaptive Robust Control in Continuous-Time

Theerawat Bhudiskaksang, Mathematical Institute, University of Oxford

Álvaro Cartea, University of Oxford -Oxford Man Institute of Quantitative Finance

Forthcoming SIAM Journal on Control and Optimization

Online Drift Estimation for Jump-Diffusion Processes

Theerawat Bhudiskaksang, Mathematical Institute, University of Oxford

Álvaro Cartea, University of Oxford -Oxford Man Institute of Quantitative Finance

Bernoulli, 27 (4) 2494 – 2518, November 2021

Deep Reinforcement Learning for Algorithmic Trading

Álvaro Cartea, University of Oxford -Oxford Man Institute of Quantitative Finance

Sebastian Jaimungal, University of Toronto, Department of Statistics

Leandro Sánchez-Betancourt, Mathematical Institute, University of Oxford

In Machine Learning in Financial Markets: A guide to contemporary practices. Edited by C.-A. Lehalle and A. Capponi. Cambridge University Press.

Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units

Zihao Zhang. University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2105.10430v2

Deep Learning for Market by Order Data

Zihao Zhang. University of Oxford – Oxford Man Institute of Quantitative Finance

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2102.08811v2

Building Cross-Sectional Systematic Strategies By Learning to Rank

Daniel Poh, University of Oxford – Oxford Man Institute of Quantitative Finance

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

Journal of Financial Data Science 2021

Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection

Kieran Wood, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2105.13727v2

Quantifying long-term market impact

Campbell R. Harvey, Duke University, adviser to Man Group plc

Anthony Ledford, Man AHL 

Emidio Sciulli, Man AHL 

Philipp Ustinov, Man Group plc

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

Eghbal Rahimikia, University of Manchester – Alliance Manchester Business School

Stefan Zohren, University of Oxford – Oxford-Man Institute of Quantitative Finance

Ser-Huang Poon, Alliance Manchester Business School, University of Manchester; Alan Turing Institute

SSRN

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures

Daniel Poh, University of Oxford – Oxford Man Institute of Quantitative Finance

Bryan Lim, University of Oxford -Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Large Non-Stationary Noisy Covariance Matrices: A Cross-Validation Approach

Vincent W. C. Tan, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2012.05757v1

Sentiment Correlation in Financial News Networks and Associated Market Movements

Xingchen Wan, University of Oxford – Oxford Man Institute of Quantitative Finance

Jie Yang, School of Public Health, Zhejiang University, China, Harvard Medical School, Harvard University, USA

Slavi Marinov, Man AHL

Jan-Peter Calliess, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Xiaowen Dong, University of Oxford – Oxford Man Institute of Quantitative Finance

Investment Sizing with Deep Learning Prediction Uncertainties for High-Frequency Eurodollar Futures Trading

Trent Spears, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Deep Learning for Portfolio Optimisation

Zihao Zhang, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Time Series Forecasting With Deep Learning: A Survey

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:2004.13408v2

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN

Deep Reinforcement Learning for Trading

Zihao Zhang, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:1911.10107v1

Extending Deep Learning Models for Limit Order Books to Quantile Regression

Zihao Zhang, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:1906.04404v1

Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Bryan Lim, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:1904.04912v3

BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books

Zihao Zhang, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

arXiv:1811.10041v1

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

Zihao Zhang, University of Oxford – Oxford Man Institute of Quantitative Finance

Stefan Zohren, University of Oxford – Oxford Man Institute of Quantitative Finance

Stephen Roberts, University of Oxford – Oxford Man Institute of Quantitative Finance

SSRN