Mining Evidences for Concept Stock Recommendation

Qi Liu, Yue Zhang


Abstract
We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques.
Anthology ID:
N18-1191
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2103–2112
Language:
URL:
https://aclanthology.org/N18-1191
DOI:
10.18653/v1/N18-1191
Bibkey:
Cite (ACL):
Qi Liu and Yue Zhang. 2018. Mining Evidences for Concept Stock Recommendation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2103–2112, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Mining Evidences for Concept Stock Recommendation (Liu & Zhang, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1191.pdf