1 code implementation • 11 Sep 2023 • Ruibo Chen, Zhiyuan Zhang, Yi Liu, Ruihan Bao, Keiko Harimoto, Xu sun
Existing multimodal works that train models from scratch face the problem of lacking universal knowledge when modeling financial news.
1 code implementation • 11 Oct 2022 • Ruibo Chen, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu sun
Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters.
1 code implementation • 4 Aug 2022 • Lei LI, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu sun
Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction.
1 code implementation • 23 Aug 2021 • Liang Zhao, Wei Li, Ruihan Bao, Keiko Harimoto, YunfangWu, Xu sun
Trading volume movement prediction is the key in a variety of financial applications.
no code implementations • 20 Aug 2021 • Zhiyuan Zhang, Wei Li, Ruihan Bao, Keiko Harimoto, Yunfang Wu, Xu sun
Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks.
1 code implementation • 25 Dec 2020 • Ruixuan Luo, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu sun
Recent deep learning based methods focus on learning clustering oriented representations.
no code implementations • 26 Jun 2020 • Wei Li, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu and Qi Su
Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
no code implementations • WS 2019 • Deli Chen, Shuming Ma, Keiko Harimoto, Ruihan Bao, Qi Su, Xu sun
In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement.