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publications
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
Published in FinNLP@EMNLP, 2022
This paper presents a platform for systematically studying NLP-aided stock auto-trading algorithms, featuring financial news and stock factors for each stock.
Recommended citation: Zou, J., Cao, H., Liu, L., Lin, Y., Abbasnejad, E. and Shi, J.Q., 2022, December. Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP) (pp. 178-186).
Stock Market Prediction via Deep Learning Techniques: A survey
Published in arXiv, 2022
This paper is a survey for stock prediction via deep learning techniques.
Recommended citation: Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L. and Shi, J.Q., 2022. Stock market prediction via deep learning techniques: A survey. arXiv preprint arXiv:2212.12717.
A Generative Approach for Comprehensive Financial Event Extraction at the Document Level
Published in ICAIF, 2023
This paper proposes a generative approach for extracting financial events from documents, addressing the challenges of scattered arguments and multiple events by introducing specialized encoding and decoding schemes.
Recommended citation: Zou, J., Liu, Y., Qi, Y., Cao, H., Liu, L. and Shi, J.Q., 2023, November. A Generative Approach for Comprehensive Financial Event Extraction at the Document Level. In Proceedings of the Fourth ACM International Conference on AI in Finance (pp. 323-330).
Semantic Role Labeling Guided Out-of-distribution Detection
Published in COLING, 2024
This paper is about ood detection for NLP.
Recommended citation: Zou, J., Guo, M., Tian, Y., Lin, Y., Cao, H., Liu, L., Abbasnejad, E. and Shi, J.Q., 2024, May. Semantic Role Labeling Guided Out-of-distribution Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 14641-14651)
InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Published in Transactions on Machine Learning Research, 2024
InvariantStock introduces a novel framework to enhance robustness against market distribution shifts by learning invariant features across environments, delivering superior stock return predictions and outperforming baselines in dynamic markets.
Recommended citation: Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A.V.D. and Shi, J.Q., 2024. InvariantStock: Learning Invariant Features for Mastering the Shifting Market. arXiv preprint arXiv:2409.00671.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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