150 citations · 416 across the 41 of their papers we have counts for
46 papers
Taming the Greeks: Option Portfolios with Inductive Biases
Wee Ling Tan, Stephen Roberts, Stefan Zohren
We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. Wh…
Stacking the Deck: Tunable Trainability in Stacked LCUs
Nikhil Khatri, Stefan Zohren, Gabriel Matos
Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests that trainability and quantum a…
DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
Kieran Wood, Stefan Zohren, Stephen J. Roberts
We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes from the underlying signal and…
Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts +1
The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimi…
DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management
Kieran Wood, Stephen J. Roberts, Stefan Zohren
We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three…
Trainability of Parametrised Linear Combinations of Unitaries
Nikhil Khatri, Stefan Zohren, Gabriel Matos
A principal concern in the optimisation of parametrised quantum circuits is the presence of barren plateaus, which present fundamental challenges to the scalability of applications…
Extracting Alpha from Financial Analyst Networks
Dragos Gorduza, Yaxuan Kong, Xiaowen Dong +1
We investigate the effectiveness of a momentum trading signal based on the coverage network of financial analysts. This signal builds on the key information-brokerage role financia…
Deep Learning for Options Trading: An End-To-End Approach
Wee Ling Tan, Stephen Roberts, Stefan Zohren
We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often req…
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
Kieran Wood, Samuel Kessler, Stephen J. Roberts +1
Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 202…
On statistical arbitrage under a conditional factor model of equity returns
Trent Spears, Stefan Zohren, Stephen Roberts
We consider a conditional factor model for a multivariate portfolio of United States equities in the context of analysing a statistical arbitrage trading strategy. A state space fr…
Learning to Learn Financial Networks for Optimising Momentum Strategies
Xingyue Pu, Stefan Zohren, Stephen Roberts +1
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current proc…
Network Momentum across Asset Classes
Xingyue Pu, Stephen Roberts, Xiaowen Dong +1
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic an…
Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB
Valentina Semenova, Dragos Gorduza, William Wildi +2
A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2…
Multi-Factor Inception: What to Do with All of These Features?
Tom Liu, Stefan Zohren
Cryptocurrency trading represents a nascent field of research, with growing adoption in industry. Aided by its decentralised nature, many metrics describing cryptocurrencies are ac…
Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies
Tom Liu, Stephen Roberts, Stefan Zohren
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time serie…
Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies
Wee Ling Tan, Stephen Roberts, Stefan Zohren
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-secti…
View fusion vis-à-vis a Bayesian interpretation of Black-Litterman for portfolio allocation
Trent Spears, Stefan Zohren, Stephen Roberts
The Black-Litterman model extends the framework of the Markowitz Modern Portfolio Theory to incorporate investor views. We consider a case where multiple view estimates, including…
On Sequential Bayesian Inference for Continual Learning
Samuel Kessler, Adam Cobb, Tim G. J. Rudner +2
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revis…
Understanding stock market instability via graph auto-encoders
Dragos Gorduza, Xiaowen Dong, Stefan Zohren
Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid…
Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity
Daniel Poh, Stephen Roberts, Stefan Zohren
Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies…
Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture
Kieran Wood, Sven Giegerich, Stephen Roberts +1
We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies. Unlike…
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
Chao Zhang, Zihao Zhang, Mihai Cucuringu +1
We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasti…
Same State, Different Task: Continual Reinforcement Learning without Interference
Samuel Kessler, Jack Parker-Holder, Philip Ball +2
Continual Learning (CL) considers the problem of training an agent sequentially on a set of tasks while seeking to retain performance on all previous tasks. A key challenge in CL i…
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection
Kieran Wood, Stephen Roberts, Stefan Zohren
Momentum strategies are an important part of alternative investments and are at the heart of commodity trading advisors (CTAs). These strategies have, however, been found to have d…