activity
20192022
most citedIncorporating Fine-grained Events in Stock Movement Prediction

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

q-fin.TR20221 cited

Stock Trading Volume Prediction with Dual-Process Meta-Learning

Ruibo Chen, Wei Li, Zhiyuan Zhang +3

Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a u…

q-fin.ST2021

Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling

Liang Zhao, Wei Li, Ruihan Bao +3

Trading volume movement prediction is the key in a variety of financial applications. Despite its importance, there is few research on this topic because of its requirement for com…

cs.LG20201 cited

Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network

Ruixuan Luo, Wei Li, Zhiyuan Zhang +3

Clustering is one of the fundamental problems in unsupervised learning. Recent deep learning based methods focus on learning clustering oriented representations. Among those method…

cs.CE20194 cited

Incorporating Fine-grained Events in Stock Movement Prediction

Deli Chen, Yanyan Zou, Keiko Harimoto +3

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the…

cs.CL2019

Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction

Deli Chen, Shuming ma, Keiko Harimoto +3

Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency ex…