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08/25/2023
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading
Financial exchanges across the world use limit order books (LOBs) to pro...
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08/23/2023
Learning to Learn Financial Networks for Optimising Momentum Strategies
Network momentum provides a novel type of risk premium, which exploits t...
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08/22/2023
Network Momentum across Asset Classes
We investigate the concept of network momentum, a novel trading signal d...
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05/11/2023
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
In multivariate time series systems, key insights can be obtained by dis...
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02/20/2023
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies
We introduce Spatio-Temporal Momentum strategies, a class of models that...
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01/20/2023
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets
We employ deep reinforcement learning (RL) to train an agent to successf...
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01/04/2023
On Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prev...
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12/09/2022
Understanding stock market instability via graph auto-encoders
Understanding stock market instability is a key question in financial ma...
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08/21/2022
Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity
Cross-sectional strategies are a classical and popular trading style, wi...
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05/20/2022
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
We present a novel approach incorporating transformer-based language mod...
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12/16/2021
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
Deep learning architectures, specifically Deep Momentum Networks (DMNs) ...
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08/01/2021
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
We develop FinText, a novel, state-of-the-art, financial word embedding ...
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06/05/2021
Same State, Different Task: Continual Reinforcement Learning without Interference
Continual Learning (CL) considers the problem of training an agent seque...
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05/28/2021
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection
Momentum strategies are an important part of alternative investments and...
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05/21/2021
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units
We design multi-horizon forecasting models for limit order book (LOB) da...
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05/20/2021
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures
The performance of a cross-sectional currency strategy depends crucially...
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02/17/2021
Deep Learning for Market by Order Data
Market by order (MBO) data - a detailed feed of individual trade instruc...
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12/13/2020
Building Cross-Sectional Systematic Strategies By Learning to Rank
The success of a cross-sectional systematic strategy depends critically ...
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12/10/2020
Large Non-Stationary Noisy Covariance Matrices: A Cross-Validation Approach
We introduce a novel covariance estimator that exploits the heteroscedas...
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08/18/2020
Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects
We introduce a new software toolbox, called Multi-Agent eXchange Environ...
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05/27/2020
Deep Learning for Portfolio Optimisation
We adopt deep learning models to directly optimise the portfolio Sharpe ...
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04/28/2020
Time Series Forecasting With Deep Learning: A Survey
Numerous deep learning architectures have been developed to accommodate ...
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12/19/2019
A Maximum Entropy approach to Massive Graph Spectra
Graph spectral techniques for measuring graph similarity, or for learnin...
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12/04/2019
Indian Buffet Neural Networks for Continual Learning
We place an Indian Buffet Process (IBP) prior over the neural structure ...
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11/22/2019
Deep Reinforcement Learning for Trading
We adopt Deep Reinforcement Learning algorithms to design trading strate...
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06/03/2019
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning
Efficient approximation lies at the heart of large-scale machine learnin...
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05/23/2019
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
Despite recent innovations in network architectures and loss functions, ...
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04/09/2019
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
While time series momentum is a well-studied phenomenon in finance, comm...
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01/23/2019
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Despite the recent popularity of deep generative state space models, few...
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11/08/2018
Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods
We introduce a novel framework for the estimation of the posterior distr...
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04/18/2018
Entropic Spectral Learning in Large Scale Networks
We present a novel algorithm for learning the spectral density of large ...
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03/24/2018