Assessment of the Applicability of Large Language Models for Quantitative Stock Price Prediction

被引：1

作者：

Voigt, Frederic ^{[1
]}

von Luck, Kai ^{[1
]}

Stelldinger, Peer ^{[1
]}

机构：

[1] Hamburg Univ Appl Sci, Hamburg, Germany

来源：

17TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS, PETRA 2024 | 2024年

关键词：

stock price prediction; quantitative analysis; stock embeddings; large language models; natural language processing; big data;

D O I：

10.1145/3652037.3652047

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In accordance with the findings presented in [34], this study examines the applicability of Machine Learning (ML) models and training strategies from the Natural Language Processing (NLP) domain in addressing time series problems, emphasizing the structural and operational aspects of these models and strategies. Recognizing the structural congruence within the data, we opt for Stock Price Prediction (SPP) as the designated domain to assess the transferability of NLP models and strategies. Building upon initial positive outcomes derived from quantitative SPP models in our ongoing research endeavors, we provide a rationale for exploring a range of additional methods and conducting subsequent research experiments. The outlined research aims to elucidate the efficacy of leveraging NLP models and techniques for addressing time series problems exemplified as SPP.

引用

页码：293 / 302

页数：10

共 44 条

[1] Beltagy I, 2020, Arxiv, DOI arXiv:2004.05150
[2] Bulatov A, 2024, Arxiv, DOI [arXiv:2304.11062, DOI 10.48550/ARXIV.2304.11062]
[3] Chen D., 2019, P 2 WORKSH EC NAT LA, P31, DOI [10.18653/v1/D19-5105, DOI 10.18653/V1/D19-5105]
[4] Chen YC, 2020, Arxiv, DOI [arXiv:1909.11740, DOI 10.48550/ARXIV.1909.11740]
[5] Child R, 2019, Arxiv, DOI arXiv:1904.10509
[6] Dai ZH, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2978
[7] STOCK MOVEMENT PREDICTION AND PORTFOLIO MANAGEMENT VIA MULTIMODAL LEARNING WITH TRANSFORMER
Daiya, Divyanshu
Lin, Che
[J]. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 3305 - 3309
[8] Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
[9] Dolphin Rian, 2023, Artificial Intelligence and Cognitive Science: 30th Irish Conference, AICS 2022, Revised Selected Papers. Communications in Computer and Information Science (1662), P81, DOI 10.1007/978-3-031-26438-2_7
[10] Dolphin R, 2023, Arxiv, DOI arXiv:2305.00245

← 1 2 3 4 5 →