Application of Pretrained Language Models in Modern Financial Research

Heungmin Lee

No 5s3nw, OSF Preprints from Center for Open Science

Abstract: In recent years, pretrained language models (PLMs) have emerged as a powerful tool for natural language processing (NLP) tasks. In this paper, we examine the potential of these models in the finance sector and the challenges they face in this domain. We also discuss the interpretability of these models and the ethical considerations associated with their deployment in finance. Our analysis shows that pretrained language models have the potential to revolutionize the way financial data is analyzed and processed. However, it is important to address the challenges and ethical considerations associated with their deployment to ensure that they are used in a responsible and accountable manner. Future research will focus on developing models that can handle the volatility of financial data, mitigate bias in the training data, and provide interpretable predictions. Overall, we believe that the future of AI in finance will be shaped by the continued development and deployment of pretrained language models.

Date: 2023-02-01
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:5s3nw

DOI: 10.31219/osf.io/5s3nw

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