26 citations · 58 across the 11 of their papers we have counts for
11 papers
Macro-aware time series forecasting via hierarchical mixed-frequency attention models
Daniel Cunha Oliveira, Kieran Wood, Stefan Zohren +2
Deep learning models show promise in financial forecasting, yet their generalization is often undermined by small datasets, noisy signals, and non-stationarity. While meta-learning…
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…
Detecting Changes in Causal Dependence with Kernels and Copulas
Shakeel Gavioli-Akilagun, Kieran Wood, Francesco Quinzan
We propose a framework for determining whether the causal dependence of an outcome on a covariate changes at a given time point, given confounders . For ins…
How do standard time series forecasting benchmarks translate to finance? A broad study of risk adjusted performance
Adir Saly-Kaufmann, Kieran Wood, Jan-Peter Calliess +2
Forecasting financial asset returns is challenging due to low signal-to-noise ratios, non-stationarity and heavy-tailed distributions. We benchmark 16 deep learning architectures f…
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…
Black Holes in Multi-Metric Gravity II: Hairy Solutions and Linear Stability of the Non- and Partially Proportional Branches
Kieran Wood, Paul M. Saffin, Anastasios Avgoustidis
Owing to our work in part I of this series of papers, it is understood that the analytically known black hole solutions in the theory of ghost free multi-metric gravity can be spli…
Black Holes in Multi-Metric Gravity
Kieran Wood, Paul M. Saffin, Anastasios Avgoustidis
We construct a wide class of black hole solutions to the general theory of ghost free multi-metric gravity in arbitrary spacetime dimension, extending and generalising the known re…
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…
Clockwork Cosmology
Kieran Wood, Paul M. Saffin, Anastasios Avgoustidis
The higher order generalisation of the clockwork mechanism to gravitational interactions provides a means to generate an exponentially suppressed coupling to matter from a fundamen…
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…
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…