Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Technologies and Applications Pub Date : 2023-02-24 DOI:10.1108/dta-05-2022-0215
Hyogon Kim, Eunmi Lee, Donghee Yoo
{"title":"Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT","authors":"Hyogon Kim, Eunmi Lee, Donghee Yoo","doi":"10.1108/dta-05-2022-0215","DOIUrl":null,"url":null,"abstract":"PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.","PeriodicalId":56156,"journal":{"name":"Data Technologies and Applications","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Technologies and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1108/dta-05-2022-0215","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1

Abstract

PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
美国证券交易委员会的文件是否表明了某种趋势?来自FinBERT的10-K和10-Q表格情绪分布的证据
目的通过对美国上市公司信息披露的情绪分析,量化企业对COVID-19大流行的看法。本研究旨在通过探讨行业的情绪趋势和变化以及与股价指数的关系,为股东、投资者和消费者提供及时的见解。设计/方法/方法从2020年至2021年期间发布的5万多份10-K和10-Q表格中提取了100多万份与COVID-19大流行相关的文本。本研究采用FinBERT微调法,将文本分为积极情绪、消极情绪和中性情绪。通过统计检验量化情绪的变化和分布,研究了情绪趋势、行业情绪分布差异和股票价格指数之间的相关性。首先,美国公司披露的与COVID-19大流行相关的文本出现了数量变化。此外,还发现了积极情绪和消极情绪的变化趋势。第二,积极和消极情绪变化的行业模式相似,而中性情绪变化没有相似之处。第三,在分析美国代表性股指与情绪趋势的关系时,结果表明与积极情绪呈正相关,与消极情绪呈负相关。原创性/价值本研究对美国证券交易委员会(SEC)备案等正式文件进行情绪分析,通过揭示文件中隐含的情绪数量变化及其随时间的趋势,与以往的研究有所区别。此外,本文还提出了一种适用于SEC备案时间序列分析的数据预处理程序和分析方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Data Technologies and Applications
Data Technologies and Applications Social Sciences-Library and Information Sciences
CiteScore
3.80
自引率
6.20%
发文量
29
期刊介绍: Previously published as: Program Online from: 2018 Subject Area: Information & Knowledge Management, Library Studies
期刊最新文献
Understanding customer behavior by mapping complaints to personality based on social media textual data A systematic review of the use of FHIR to support clinical research, public health and medical education Novel framework for learning performance prediction using pattern identification and deep learning A comparative analysis of job satisfaction prediction models using machine learning: a mixed-method approach Assessing the alignment of corporate ESG disclosures with the UN sustainable development goals: a BERT-based text analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1