No Stock is an Island: Learning Internal and Relational Attributes of Stocks with Contrastive Learning

Shicheng Li, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, Xu Sun

Abstract
Previous work has demonstrated the viability of applying deep learning techniques in the financial area. Recently, the task of stock embedding learning has been drawing attention from the research community, which aims to represent the characteristics of stocks with distributed vectors that can be used in various financial analysis scenarios. Existing approaches for learning stock embeddings either require expert knowledge, or mainly focus on the textual part of information corresponding to individual temporal movements. In this paper, we propose to model stock properties as the combination of internal attributes and relational attributes, which takes into consideration both the time-invariant properties of individual stocks and their movement patterns in relation to the market. To learn the two types of attributes from financial news and transaction data, we design several training objectives based on contrastive learning to extract and separate the long-term and temporary information in the data that are able to counter the inherent randomness of the stock market. Experiments and further analyses on portfolio optimization reveal the effectiveness of our method in extracting comprehensive stock information from various data sources.
Anthology ID:
2022.finnlp-1.20
Volume:
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Venue:
FinNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
147–153
Language:
URL:
https://aclanthology.org/2022.finnlp-1.20
DOI:
10.18653/v1/2022.finnlp-1.20
Bibkey:
Cite (ACL):
Shicheng Li, Wei Li, Zhiyuan Zhang, Ruihan Bao, Keiko Harimoto, and Xu Sun. 2022. No Stock is an Island: Learning Internal and Relational Attributes of Stocks with Contrastive Learning. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 147–153, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
No Stock is an Island: Learning Internal and Relational Attributes of Stocks with Contrastive Learning (Li et al., FinNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2022.finnlp-1.20.pdf
PDF Search