11 citations · 46 across the 13 of their papers we have counts for
7 papers · 1 filter
BondBERT: What we learn when assigning sentiment in the bond market
Toby Barter, Zheng Gao, Eva Christodoulaki +2
Bond markets respond differently to macroeconomic news compared to equity markets, yet most sentiment models are trained primarily on general financial or equity news data. However…
How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation
Zinuo You, Jin Zheng, John Cartlidge
Existing graph neural networks typically rely on heuristic choices for hidden dimensions and propagation depths, which often lead to severe information loss during propagation, kno…
Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach
Lawrence Clegg, John Cartlidge
Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis. Yet, there are few known attempts to incorporate it within forecas…
VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting
Weibin Feng, Ran Tao, John Cartlidge +1
Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal pattern…
Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE
Rongwei Liu, Jin Zheng, John Cartlidge
The optimal asset allocation between risky and risk-free assets is a persistent challenge due to the inherent volatility in financial markets. Conventional methods rely on strict d…
Improving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling
Zinuo You, John Cartlidge, Karen Elliott +2
Existing black-box portfolio management systems are prevalent in the financial industry due to commercial and safety constraints, though their performance can fluctuate dramaticall…
Cross-Modal Temporal Fusion for Financial Market Forecasting
Yunhua Pei, John Cartlidge, Anandadeep Mandal +3
Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models…