Academic Journal
Textual Financial Data Repository for Machine Learning, Artificial Intelligence, and Textual Analyses: Major Sections from 10-K, 10-Q, and Financial Statement Notes Extracted Using Shared Python Code.
| Title: | Textual Financial Data Repository for Machine Learning, Artificial Intelligence, and Textual Analyses: Major Sections from 10-K, 10-Q, and Financial Statement Notes Extracted Using Shared Python Code. |
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| Authors: | Codesso, Mauricio M., Hoitash, Rani, Hoitash, Udi |
| Source: | Journal of Information Systems; Summer2026, Vol. 40 Issue 2, p117-137, 21p |
| Subject Terms: | Quarterly reports, Python programming language, Sentiment analysis, Financial statements, Business enterprises, Text mining |
| Abstract: | Financial reports, including 10-K and 10-Q filings, are a primary source of textual data in business disciplines. However, extracting specific sections from these lengthy documents remains a challenge. Custom code development by each research team to parse these files leads to redundancy, inefficiency, and inconsistencies and is especially challenging for teams lacking technical expertise. We address this by offering raw textual data from MD&A, risk factors, and business description sections, and financial statement notes, for all firms from 2008 onward. We share Python code to facilitate download and parsing. We also provide pre-calculated textual metrics, such as word counts, readability measures, and several bags of word metrics, including negative sentiment, forward-looking statements, and R&D. Additionally, we contribute two new word lists, COVID-19 and human capital, developed using a novel approach based on disclosure shocks. Our goal is to streamline research processes, ensure consistency, and enable further advances in the field. Data Availability: Data are available for download at http://www.analytext.com/. Code is available for download at https://github.com/mmcodesso/edgar-metrics-parser JEL Classifications: C55; C88; M4; M48. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Systems is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 194973100 RelevancyScore: 1162 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1162.42175292969 |
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| Items | – Name: Title Label: Title Group: Ti Data: Textual Financial Data Repository for Machine Learning, Artificial Intelligence, and Textual Analyses: Major Sections from 10-K, 10-Q, and Financial Statement Notes Extracted Using Shared Python Code. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Codesso%2C+Mauricio+M%2E%22">Codesso, Mauricio M.</searchLink><br /><searchLink fieldCode="AR" term="%22Hoitash%2C+Rani%22">Hoitash, Rani</searchLink><br /><searchLink fieldCode="AR" term="%22Hoitash%2C+Udi%22">Hoitash, Udi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Information Systems; Summer2026, Vol. 40 Issue 2, p117-137, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Quarterly+reports%22">Quarterly reports</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+statements%22">Financial statements</searchLink><br /><searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Financial reports, including 10-K and 10-Q filings, are a primary source of textual data in business disciplines. However, extracting specific sections from these lengthy documents remains a challenge. Custom code development by each research team to parse these files leads to redundancy, inefficiency, and inconsistencies and is especially challenging for teams lacking technical expertise. We address this by offering raw textual data from MD&A, risk factors, and business description sections, and financial statement notes, for all firms from 2008 onward. We share Python code to facilitate download and parsing. We also provide pre-calculated textual metrics, such as word counts, readability measures, and several bags of word metrics, including negative sentiment, forward-looking statements, and R&D. Additionally, we contribute two new word lists, COVID-19 and human capital, developed using a novel approach based on disclosure shocks. Our goal is to streamline research processes, ensure consistency, and enable further advances in the field. Data Availability: Data are available for download at http://www.analytext.com/. Code is available for download at https://github.com/mmcodesso/edgar-metrics-parser JEL Classifications: C55; C88; M4; M48. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Information Systems is the property of American Accounting Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2308/ISYS-2024-084 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 117 Subjects: – SubjectFull: Quarterly reports Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Sentiment analysis Type: general – SubjectFull: Financial statements Type: general – SubjectFull: Business enterprises Type: general – SubjectFull: Text mining Type: general Titles: – TitleFull: Textual Financial Data Repository for Machine Learning, Artificial Intelligence, and Textual Analyses: Major Sections from 10-K, 10-Q, and Financial Statement Notes Extracted Using Shared Python Code. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Codesso, Mauricio M. – PersonEntity: Name: NameFull: Hoitash, Rani – PersonEntity: Name: NameFull: Hoitash, Udi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Summer2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08887985 Numbering: – Type: volume Value: 40 – Type: issue Value: 2 Titles: – TitleFull: Journal of Information Systems Type: main |
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