6 citations · 14 across the 9 of their papers we have counts for
9 papers
Modal-adaptive Knowledge-enhanced Graph-based Financial Prediction from Monetary Policy Conference Calls with LLM
Kun Ouyang, Yi Liu, Shicheng Li +3
Financial prediction from Monetary Policy Conference (MPC) calls is a new yet challenging task, which targets at predicting the price movement and volatility for specific financial…
Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction
Ruibo Chen, Zhiyuan Zhang, Yi Liu +3
Multimodal stock trading volume movement prediction with stock-related news is one of the fundamental problems in the financial area. Existing multimodal works that train models fr…
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…
Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction
Lei Li, Zhiyuan Zhang, Ruihan Bao +2
Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available i…
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…
ASAT: Adaptively Scaled Adversarial Training in Time Series
Zhiyuan Zhang, Wei Li, Ruihan Bao +3
Adversarial training is a method for enhancing neural networks to improve the robustness against adversarial examples. Besides the security concerns of potential adversarial exampl…
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…
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…
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…