Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models

Cheng Yu Chuang; Yi Yang

doi:10.18653/v1/2022.acl-short.12

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models

Cheng Yu Chuang, Yi Yang

Department of Information Systems, Business Statistics & Operations Management

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

12 Citations (Scopus)

Abstract

Pretrained language models such as BERT have achieved remarkable success in several NLP tasks. With the wide adoption of BERT in real-world applications, researchers begin to investigate the implicit biases encoded in the BERT. In this paper, we assess the implicit stock market preferences in BERT and its finance domain-specific model FinBERT. We find some interesting patterns. For example, the language models are overall more positive towards the stock market, but there are significant differences in preferences between a pair of industry sectors, or even within a sector. Given the prevalence of NLP models in financial decision making systems, this work raises the awareness of their potential implicit preferences in the stock markets. Awareness of such problems can help practitioners improve robustness and accountability of their financial NLP pipelines.

Original language	English
Title of host publication	ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)
Editors	Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Publisher	Association for Computational Linguistics (ACL)
Pages	100-105
Number of pages	6
ISBN (Electronic)	9781955917223
DOIs	https://doi.org/10.18653/v1/2022.acl-short.12
Publication status	Published - 2022
Event	60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Dublin, Ireland Duration: 22 May 2022 → 27 May 2022

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
Volume	2
ISSN (Print)	0736-587X

Conference

Conference	60th Annual Meeting of the Association for Computational Linguistics, ACL 2022
Country/Territory	Ireland
City	Dublin
Period	22/05/22 → 27/05/22

Bibliographical note

Publisher Copyright:
© 2022 Association for Computational Linguistics.

Access to Document

10.18653/v1/2022.acl-short.12

Cite this

Chuang, C. Y., & Yang, Y. (2022). Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers) (pp. 100-105). (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 2). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.acl-short.12

Chuang, Cheng Yu ; Yang, Yi. / Buy Tesla, Sell Ford : Assessing Implicit Stock Market Preference in Pre-trained Language Models. ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). editor / Smaranda Muresan ; Preslav Nakov ; Aline Villavicencio. Association for Computational Linguistics (ACL), 2022. pp. 100-105 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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title = "Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models",

abstract = "Pretrained language models such as BERT have achieved remarkable success in several NLP tasks. With the wide adoption of BERT in real-world applications, researchers begin to investigate the implicit biases encoded in the BERT. In this paper, we assess the implicit stock market preferences in BERT and its finance domain-specific model FinBERT. We find some interesting patterns. For example, the language models are overall more positive towards the stock market, but there are significant differences in preferences between a pair of industry sectors, or even within a sector. Given the prevalence of NLP models in financial decision making systems, this work raises the awareness of their potential implicit preferences in the stock markets. Awareness of such problems can help practitioners improve robustness and accountability of their financial NLP pipelines.",

author = "Chuang, \{Cheng Yu\} and Yi Yang",

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year = "2022",

doi = "10.18653/v1/2022.acl-short.12",

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series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

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Chuang, CY & Yang, Y 2022, Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models. in S Muresan, P Nakov & A Villavicencio (eds), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 2, Association for Computational Linguistics (ACL), pp. 100-105, 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022, Dublin, Ireland, 22/05/22. https://doi.org/10.18653/v1/2022.acl-short.12

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models. / Chuang, Cheng Yu; Yang, Yi.
ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). ed. / Smaranda Muresan; Preslav Nakov; Aline Villavicencio. Association for Computational Linguistics (ACL), 2022. p. 100-105 (Proceedings of the Annual Meeting of the Association for Computational Linguistics; Vol. 2).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AB - Pretrained language models such as BERT have achieved remarkable success in several NLP tasks. With the wide adoption of BERT in real-world applications, researchers begin to investigate the implicit biases encoded in the BERT. In this paper, we assess the implicit stock market preferences in BERT and its finance domain-specific model FinBERT. We find some interesting patterns. For example, the language models are overall more positive towards the stock market, but there are significant differences in preferences between a pair of industry sectors, or even within a sector. Given the prevalence of NLP models in financial decision making systems, this work raises the awareness of their potential implicit preferences in the stock markets. Awareness of such problems can help practitioners improve robustness and accountability of their financial NLP pipelines.

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Chuang CY, Yang Y. Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models. In Muresan S, Nakov P, Villavicencio A, editors, ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers). Association for Computational Linguistics (ACL). 2022. p. 100-105. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2022.acl-short.12

Buy Tesla, Sell Ford: Assessing Implicit Stock Market Preference in Pre-trained Language Models

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