21 citations · 24 across the 5 of their papers we have counts for
5 papers
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…
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
Chao Zhang, Xingyue Pu, Mihai Cucuringu +1
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks…
Learning to Learn Graph Topologies
Xingyue Pu, Tianyue Cao, Xiaoyun Zhang +2
Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumpt…
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Xingyue Pu, Siu Lun Chau, Xiaowen Dong +1
The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an inc…