150 citations · 416 across the 41 of their papers we have counts for
10 papers · 1 filter
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…