Kernel-based Hybrid Interpretable Transformer for High-frequency Stock Movement Prediction-Reference-Cited by-同舟云学术

Kernel-based Hybrid Interpretable Transformer for High-frequency Stock Movement Prediction

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining (ICDM)
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Author:

Lin Fan¹,Li Pengfei²,Lin Yuanguo¹,Chen Zhennan¹,You Huanyu³,Feng Shibo¹

Affiliation:

1. Xiamen University,School of Informatics,Xiamen,China

2. Xiamen University,Institute of Artificial Intelligence,Xiamen,China

3. Xiamen University Malaysia,School of Computing and Data Science,Selangor State,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10027565/10027596/10027785.pdf?arnumber=10027785

Reference45 articles.

1. Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

2. The econometrics of financial markets

3. A novel graph convolutional feature based convolutional neural network for stock trend prediction

4. Tensor-Train Decomposition

5. Transformer-based capsule network for stock movement prediction;liu;Proceedings of the First Workshop on Financial Technology and Natural Language Processing,2019

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